From e387d2b490f3bfdaa41e1cbce4a7dc233830f1a2 Mon Sep 17 00:00:00 2001 From: docs-sched-rebuild Date: Mon, 6 Nov 2023 17:22:08 +0000 Subject: [PATCH] Pushing changes to GitHub Pages. --- main/api/merlin_standard_lib.proto.html | 10 +++++----- main/api/transformers4rec.config.html | 4 ++-- main/api/transformers4rec.torch.html | 2 +- ...ession-based-Yoochoose-multigpu-training-PyT.html | 12 +++++++++++- main/searchindex.js | 2 +- stable/api/merlin_standard_lib.proto.html | 10 +++++----- stable/api/transformers4rec.config.html | 4 ++-- stable/api/transformers4rec.torch.html | 2 +- stable/searchindex.js | 2 +- v0.1.16/api/merlin_standard_lib.proto.html | 10 +++++----- v0.1.16/api/transformers4rec.config.html | 4 ++-- v0.1.16/api/transformers4rec.torch.html | 2 +- v0.1.16/searchindex.js | 2 +- v23.02.00/api/merlin_standard_lib.proto.html | 10 +++++----- v23.02.00/api/transformers4rec.config.html | 4 ++-- v23.02.00/api/transformers4rec.torch.html | 2 +- v23.02.00/searchindex.js | 2 +- v23.04.00/api/merlin_standard_lib.proto.html | 10 +++++----- v23.04.00/api/transformers4rec.config.html | 4 ++-- v23.04.00/api/transformers4rec.torch.html | 2 +- v23.04.00/searchindex.js | 2 +- v23.05.00/api/merlin_standard_lib.proto.html | 10 +++++----- v23.05.00/api/transformers4rec.config.html | 4 ++-- v23.05.00/api/transformers4rec.torch.html | 2 +- v23.05.00/searchindex.js | 2 +- v23.06.00/api/merlin_standard_lib.proto.html | 10 +++++----- v23.06.00/api/transformers4rec.config.html | 4 ++-- v23.06.00/api/transformers4rec.torch.html | 2 +- v23.06.00/searchindex.js | 2 +- v23.08.00/api/merlin_standard_lib.proto.html | 10 +++++----- v23.08.00/api/transformers4rec.config.html | 4 ++-- v23.08.00/api/transformers4rec.torch.html | 2 +- v23.08.00/searchindex.js | 2 +- 33 files changed, 83 insertions(+), 73 deletions(-) diff --git a/main/api/merlin_standard_lib.proto.html b/main/api/merlin_standard_lib.proto.html index c59d9880c..b21ef9f66 100644 --- a/main/api/merlin_standard_lib.proto.html +++ b/main/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f21029081f0>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f21029081f0>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f21029081f0>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f21029081f0>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f21029081f0>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f3d2a225130>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f21029081f0>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f21029081f0>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f21029081f0>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f21029081f0>)[source]

Bases: betterproto.Message

diff --git a/main/api/transformers4rec.config.html b/main/api/transformers4rec.config.html index b57f2f839..bcb5a25e7 100644 --- a/main/api/transformers4rec.config.html +++ b/main/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -243,7 +243,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/main/api/transformers4rec.torch.html b/main/api/transformers4rec.torch.html index bd6512869..c0d0d6c0e 100644 --- a/main/api/transformers4rec.torch.html +++ b/main/api/transformers4rec.torch.html @@ -1141,7 +1141,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

diff --git a/main/examples/end-to-end-session-based/03-Session-based-Yoochoose-multigpu-training-PyT.html b/main/examples/end-to-end-session-based/03-Session-based-Yoochoose-multigpu-training-PyT.html index 664e05601..16fe28947 100644 --- a/main/examples/end-to-end-session-based/03-Session-based-Yoochoose-multigpu-training-PyT.html +++ b/main/examples/end-to-end-session-based/03-Session-based-Yoochoose-multigpu-training-PyT.html @@ -308,12 +308,22 @@

2. Executing the multi-gpu training
+
# If only 1 GPU are available, starts a single process to use that GPU
+from torch.cuda import device_count
+num_gpus = device_count()
+NUM_PROCESSES = min(num_gpus, 2)
+
+
+
+ +
+
import os
 OUTPUT_DIR = os.environ.get("OUTPUT_DIR", "/workspace/data/preproc_sessions_by_day")
 LR = float(os.environ.get("LEARNING_RATE", "0.0005"))
 BATCH_SIZE_TRAIN = int(os.environ.get("BATCH_SIZE_TRAIN", "256"))
 BATCH_SIZE_VALID = int(os.environ.get("BATCH_SIZE_VALID", "128"))
-!python -m torch.distributed.run --nproc_per_node 2 {TRAINER_FILE} --path {OUTPUT_DIR} --learning-rate {LR} --per-device-train-batch-size {BATCH_SIZE_TRAIN} --per-device-eval-batch-size {BATCH_SIZE_VALID}
+!python -m torch.distributed.run --nproc_per_node {NUM_PROCESSES} {TRAINER_FILE} --path {OUTPUT_DIR} --learning-rate {LR} --per-device-train-batch-size {BATCH_SIZE_TRAIN} --per-device-eval-batch-size {BATCH_SIZE_VALID}
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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":32,"export":[16,20,27],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:36,Tying:31,Using:1,achiev:1,add:27,addit:34,aggreg:[13,27,31],aliv:17,api:[6,25],approach:34,architectur:[31,34],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,29,34],benefit:1,between:36,bias:25,block:[10,21,28,31],build:[21,31],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:32,competit:34,comput:21,conda:1,config:8,connect:[17,28],consecut:26,content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,32,35],data_util:14,dataload:28,dataparallel:32,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,design:31,develop:0,distributeddataparallel:32,doc_util:5,docker:1,document:6,embed:[11,28,31],embedding_util:5,encod:27,end:[17,19,24,29,33],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,35],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[33,34],featur:[11,16,20,26,27,31],feedback:1,file:[27,28],fine:[17,21,28],finetun:28,first:[0,16,26],free:28,from:[26,28],get:[17,22,23,24,29],gpu:[18,24,28,32],group:27,head:[12,28,31],hug:[33,34],huggingfac:36,includ:26,increment:28,indic:30,infer:[17,33],inform:28,initi:27,input:[16,20,21,27,28,31],instal:1,instanti:[17,28],integr:[33,36],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:28,label:0,launch:17,learn:29,librari:[20,21,22,26,27,28],load:[16,17,35],log:25,mask:[9,31],memori:28,merlin:[30,33,34],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,31],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:31,multi:[18,32],next:28,nlp:36,normal:27,notebook:[1,24],nvidia:[33,34],nvtabular:[16,20,27,33],object:[21,22,28,29],organ:25,other:34,our:28,out:20,output:[16,20,27,31,33],over:[17,21,28],overview:[16,33],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:32,parquet:[27,28],path:[16,20,27],perform:32,pip:1,pipelin:33,pre:[16,20,34],predict:[28,31],prediction_task:12,preliminari:26,preprocess:[16,20,26],process:[16,20,31],product:27,proto:3,proto_util:5,python:18,pytorch:[17,18,35],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,34],recsi:36,refer:[17,18,28],registri:2,regular:31,relat:30,relationship:36,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22],requir:[20,21,22,25,26],resourc:[30,34],respons:22,restart:28,rnn:[28,31],run:24,same:16,sampl:1,save:[16,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sequenc:[11,31],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,33],session:[16,17,18,19,21,22,23,24,27,28,29,34],set:[17,21,22,27,28],setup:25,side:28,start:[17,22,23,24,29],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,34],synthet:[20,23,24],tabl:30,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],torch:[9,10,11,12,13,14,22],torch_util:14,trace:[17,22],train:[1,17,18,21,25,28,32,34,35],trainer:[8,9,17,28,32],transform:[8,10,13,16,17,21,28,31,33,36],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,30,34,36],triton:[17,22,33],tune:[17,21,28],tutori:[1,24,29],tying:28,type:[7,9],user:26,user_sess:26,using:[17,32],util:[5,14,15],valid:21,via:26,visual:17,weight:25,what:28,when:16,why:36,window:[17,21,28],within:16,workflow:[16,27],wrap:[27,28],xlnet:[21,28],your:0}}) 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":32,"export":[16,20,27],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:36,Tying:31,Using:1,achiev:1,add:27,addit:34,aggreg:[13,27,31],aliv:17,api:[6,25],approach:34,architectur:[31,34],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,29,34],benefit:1,between:36,bias:25,block:[10,21,28,31],build:[21,31],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:32,competit:34,comput:21,conda:1,config:8,connect:[17,28],consecut:26,content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,32,35],data_util:14,dataload:28,dataparallel:32,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,design:31,develop:0,distributeddataparallel:32,doc_util:5,docker:1,document:6,embed:[11,28,31],embedding_util:5,encod:27,end:[17,19,24,29,33],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,35],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[33,34],featur:[11,16,20,26,27,31],feedback:1,file:[27,28],fine:[17,21,28],finetun:28,first:[0,16,26],free:28,from:[26,28],get:[17,22,23,24,29],gpu:[18,24,28,32],group:27,head:[12,28,31],hug:[33,34],huggingfac:36,includ:26,increment:28,indic:30,infer:[17,33],inform:28,initi:27,input:[16,20,21,27,28,31],instal:1,instanti:[17,28],integr:[33,36],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:28,label:0,launch:17,learn:29,librari:[20,21,22,26,27,28],load:[16,17,35],log:25,mask:[9,31],memori:28,merlin:[30,33,34],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,31],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:31,multi:[18,32],next:28,nlp:36,normal:27,notebook:[1,24],nvidia:[33,34],nvtabular:[16,20,27,33],object:[21,22,28,29],organ:25,other:34,our:28,out:20,output:[16,20,27,31,33],over:[17,21,28],overview:[16,33],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:32,parquet:[27,28],path:[16,20,27],perform:32,pip:1,pipelin:33,pre:[16,20,34],predict:[28,31],prediction_task:12,preliminari:26,preprocess:[16,20,26],process:[16,20,31],product:27,proto:3,proto_util:5,python:18,pytorch:[17,18,35],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,34],recsi:36,refer:[17,18,28],registri:2,regular:31,relat:30,relationship:36,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22],requir:[20,21,22,25,26],resourc:[30,34],respons:22,restart:28,rnn:[28,31],run:24,same:16,sampl:1,save:[16,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sequenc:[11,31],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,33],session:[16,17,18,19,21,22,23,24,27,28,29,34],set:[17,21,22,27,28],setup:25,side:28,start:[17,22,23,24,29],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,34],synthet:[20,23,24],tabl:30,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],torch:[9,10,11,12,13,14,22],torch_util:14,trace:[17,22],train:[1,17,18,21,25,28,32,34,35],trainer:[8,9,17,28,32],transform:[8,10,13,16,17,21,28,31,33,36],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,30,34,36],triton:[17,22,33],tune:[17,21,28],tutori:[1,24,29],tying:28,type:[7,9],user:26,user_sess:26,using:[17,32],util:[5,14,15],valid:21,via:26,visual:17,weight:25,what:28,when:16,why:36,window:[17,21,28],within:16,workflow:[16,27],wrap:[27,28],xlnet:[21,28],your:0}}) \ No newline at end of file diff --git a/stable/api/merlin_standard_lib.proto.html b/stable/api/merlin_standard_lib.proto.html index a38a7abe6..6474b36b3 100644 --- a/stable/api/merlin_standard_lib.proto.html +++ b/stable/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f0403a16280>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f0403a16280>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f0403a16280>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f0403a16280>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f0403a16280>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f0403a16280>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f0403a16280>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f0403a16280>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f0403a16280>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f5cdd80a2b0>)[source]

Bases: betterproto.Message

diff --git a/stable/api/transformers4rec.config.html b/stable/api/transformers4rec.config.html index ba2a6a5ea..965d4db76 100644 --- a/stable/api/transformers4rec.config.html +++ b/stable/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -243,7 +243,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/stable/api/transformers4rec.torch.html b/stable/api/transformers4rec.torch.html index 1561c11e7..850d6164e 100644 --- a/stable/api/transformers4rec.torch.html +++ b/stable/api/transformers4rec.torch.html @@ -1141,7 +1141,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

diff --git a/stable/searchindex.js b/stable/searchindex.js index e6270dc69..52cea0ebf 100644 --- a/stable/searchindex.js +++ b/stable/searchindex.js @@ -1 +1 @@ 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":32,"export":[16,20,27],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:36,Tying:31,Using:1,achiev:1,add:27,addit:34,aggreg:[13,27,31],aliv:17,api:[6,25],approach:34,architectur:[31,34],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,29,34],benefit:1,between:36,bias:25,block:[10,21,28,31],build:[21,31],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:32,competit:34,comput:21,conda:1,config:8,connect:[17,28],consecut:26,content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,32,35],data_util:14,dataload:28,dataparallel:32,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,design:31,develop:0,distributeddataparallel:32,doc_util:5,docker:1,document:6,embed:[11,28,31],embedding_util:5,encod:27,end:[17,19,24,29,33],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,35],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[33,34],featur:[11,16,20,26,27,31],feedback:1,file:[27,28],fine:[17,21,28],finetun:28,first:[0,16,26],free:28,from:[26,28],get:[17,22,23,24,29],gpu:[18,24,28,32],group:27,head:[12,28,31],hug:[33,34],huggingfac:36,includ:26,increment:28,indic:30,infer:[17,33],inform:28,initi:27,input:[16,20,21,27,28,31],instal:1,instanti:[17,28],integr:[33,36],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:28,label:0,launch:17,learn:29,librari:[20,21,22,26,27,28],load:[16,17,35],log:25,mask:[9,31],memori:28,merlin:[30,33,34],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,31],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:31,multi:[18,32],next:28,nlp:36,normal:27,notebook:[1,24],nvidia:[33,34],nvtabular:[16,20,27,33],object:[21,22,28,29],organ:25,other:34,our:28,out:20,output:[16,20,27,31,33],over:[17,21,28],overview:[16,33],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:32,parquet:[27,28],path:[16,20,27],perform:32,pip:1,pipelin:33,pre:[16,20,34],predict:[28,31],prediction_task:12,preliminari:26,preprocess:[16,20,26],process:[16,20,31],product:27,proto:3,proto_util:5,python:18,pytorch:[17,18,35],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,34],recsi:36,refer:[17,18,28],registri:2,regular:31,relat:30,relationship:36,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22],requir:[20,21,22,25,26],resourc:[30,34],respons:22,restart:28,rnn:[28,31],run:24,same:16,sampl:1,save:[16,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sequenc:[11,31],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,33],session:[16,17,18,19,21,22,23,24,27,28,29,34],set:[17,21,22,27,28],setup:25,side:28,start:[17,22,23,24,29],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,34],synthet:[20,23,24],tabl:30,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],torch:[9,10,11,12,13,14,22],torch_util:14,trace:[17,22],train:[1,17,18,21,25,28,32,34,35],trainer:[8,9,17,28,32],transform:[8,10,13,16,17,21,28,31,33,36],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,30,34,36],triton:[17,22,33],tune:[17,21,28],tutori:[1,24,29],tying:28,type:[7,9],user:26,user_sess:26,using:[17,32],util:[5,14,15],valid:21,via:26,visual:17,weight:25,what:28,when:16,why:36,window:[17,21,28],within:16,workflow:[16,27],wrap:[27,28],xlnet:[21,28],your:0}}) 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":32,"export":[16,20,27],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:36,Tying:31,Using:1,achiev:1,add:27,addit:34,aggreg:[13,27,31],aliv:17,api:[6,25],approach:34,architectur:[31,34],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,29,34],benefit:1,between:36,bias:25,block:[10,21,28,31],build:[21,31],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:32,competit:34,comput:21,conda:1,config:8,connect:[17,28],consecut:26,content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,32,35],data_util:14,dataload:28,dataparallel:32,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,design:31,develop:0,distributeddataparallel:32,doc_util:5,docker:1,document:6,embed:[11,28,31],embedding_util:5,encod:27,end:[17,19,24,29,33],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,35],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[33,34],featur:[11,16,20,26,27,31],feedback:1,file:[27,28],fine:[17,21,28],finetun:28,first:[0,16,26],free:28,from:[26,28],get:[17,22,23,24,29],gpu:[18,24,28,32],group:27,head:[12,28,31],hug:[33,34],huggingfac:36,includ:26,increment:28,indic:30,infer:[17,33],inform:28,initi:27,input:[16,20,21,27,28,31],instal:1,instanti:[17,28],integr:[33,36],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:28,label:0,launch:17,learn:29,librari:[20,21,22,26,27,28],load:[16,17,35],log:25,mask:[9,31],memori:28,merlin:[30,33,34],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,31],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:31,multi:[18,32],next:28,nlp:36,normal:27,notebook:[1,24],nvidia:[33,34],nvtabular:[16,20,27,33],object:[21,22,28,29],organ:25,other:34,our:28,out:20,output:[16,20,27,31,33],over:[17,21,28],overview:[16,33],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:32,parquet:[27,28],path:[16,20,27],perform:32,pip:1,pipelin:33,pre:[16,20,34],predict:[28,31],prediction_task:12,preliminari:26,preprocess:[16,20,26],process:[16,20,31],product:27,proto:3,proto_util:5,python:18,pytorch:[17,18,35],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,34],recsi:36,refer:[17,18,28],registri:2,regular:31,relat:30,relationship:36,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22],requir:[20,21,22,25,26],resourc:[30,34],respons:22,restart:28,rnn:[28,31],run:24,same:16,sampl:1,save:[16,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sequenc:[11,31],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,33],session:[16,17,18,19,21,22,23,24,27,28,29,34],set:[17,21,22,27,28],setup:25,side:28,start:[17,22,23,24,29],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,34],synthet:[20,23,24],tabl:30,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],torch:[9,10,11,12,13,14,22],torch_util:14,trace:[17,22],train:[1,17,18,21,25,28,32,34,35],trainer:[8,9,17,28,32],transform:[8,10,13,16,17,21,28,31,33,36],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,30,34,36],triton:[17,22,33],tune:[17,21,28],tutori:[1,24,29],tying:28,type:[7,9],user:26,user_sess:26,using:[17,32],util:[5,14,15],valid:21,via:26,visual:17,weight:25,what:28,when:16,why:36,window:[17,21,28],within:16,workflow:[16,27],wrap:[27,28],xlnet:[21,28],your:0}}) \ No newline at end of file diff --git a/v0.1.16/api/merlin_standard_lib.proto.html b/v0.1.16/api/merlin_standard_lib.proto.html index 762e46d31..519888d61 100644 --- a/v0.1.16/api/merlin_standard_lib.proto.html +++ b/v0.1.16/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f6c56ba53a0>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f7e0c4c1e80>)[source]

Bases: betterproto.Message

diff --git a/v0.1.16/api/transformers4rec.config.html b/v0.1.16/api/transformers4rec.config.html index 1d33e63b0..0f14f3645 100644 --- a/v0.1.16/api/transformers4rec.config.html +++ b/v0.1.16/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -242,7 +242,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/v0.1.16/api/transformers4rec.torch.html b/v0.1.16/api/transformers4rec.torch.html index 19203459d..0195618a0 100644 --- a/v0.1.16/api/transformers4rec.torch.html +++ b/v0.1.16/api/transformers4rec.torch.html @@ -1086,7 +1086,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

diff --git a/v0.1.16/searchindex.js b/v0.1.16/searchindex.js index bd882338f..0448eac87 100644 --- a/v0.1.16/searchindex.js +++ b/v0.1.16/searchindex.js @@ -1 +1 @@ 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-end pipeline with NVIDIA Merlin","Additional Resources","Training and Evaluation","Why 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-end pipeline with NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"case":1,"class":33,"export":[16,17,20,27,28,29],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:37,Tying:32,Use:1,add:27,addit:35,aggreg:[13,27,32],aliv:17,api:[6,25],approach:35,architectur:[32,35],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[1,10,11,17,18,19,21,22,23,24,28,30,35],between:37,bias:25,block:[10,21,28,32],build:21,calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,25],column:26,command:25,comparison:33,competit:35,comput:21,conda:1,config:8,connect:[17,28],consecut:26,contain:[17,29],content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,33,36],data_util:14,dataload:28,dataparallel:33,dataset:[25,27],datetim:26,defin:[16,17,20,21,28],definit:17,depend:15,deploi:29,deploy:28,develop:0,distributeddataparallel:33,doc_util:5,docker:[1,29],document:6,down:29,embed:[11,28,32],embedding_util:5,encod:27,end:[17,19,24,30,34],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,36],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,featur:[11,16,20,26,27,32],feedback:1,file:[27,28,29],fine:[17,21,28],finetun:28,first:[0,16,26],format:17,free:28,from:[26,28],get:[17,22,23,24,30],gpu:[18,24,28,33],group:27,head:[12,28,32],highlight:1,huggingfac:[35,37],includ:26,increment:[28,36],indic:31,infer:[17,29,34],inform:28,initi:27,input:[16,20,21,27,28,32],instal:1,instanti:[17,28],integr:[34,37],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:[28,29],label:0,launch:17,learn:30,librari:[20,21,22,26,27,28],load:[16,17,29,36],log:25,mask:[9,32],memori:28,merlin:[31,34,35],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[12,17,21,22,28,29,32],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],multi:[18,33],next:28,nlp:37,normal:27,notebook:[1,24],nvidia:[34,35],nvtabular:[16,20,27,29,34],object:[21,22,28,30],organ:25,other:35,our:28,output:[16,20,27,32,34],over:[17,21,28],overview:16,packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:33,parquet:27,path:[16,20,27],perform:33,pip:1,pipelin:34,pre:[16,20,35],predict:[28,29,32],prediction_task:12,preliminari:26,preprocess:[16,17,20,26,28],process:[16,20,32],product:27,proto:3,proto_util:5,pull:[17,29],python:18,pytorch:[17,18,29,36],quick:1,ranking_metr:9,raw:[16,17],read:[26,27],recenc:[26,27],recommend:[1,17,18,19,21,22,23,24,28,29,30,35],recsi:37,refer:[17,18,28],registri:2,regular:32,relat:31,relationship:37,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22,29],requir:[17,20,21,22,25,26],resourc:[31,35],respons:22,restart:28,review:29,rnn:[28,32],run:24,same:16,save:[16,17,21],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sent:29,sequenc:[11,32],sequenti:[1,16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,28,29,34],session:[1,16,17,18,19,21,22,23,24,27,28,30,35],set:[17,21,22,27,28],setup:25,shut:29,side:28,start:[17,22,23,24,29,30],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,35],synthet:[20,23,24],system:29,tabl:31,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],top:29,torch:[9,10,11,12,13,14,22],torch_util:14,tour:1,trace:22,train:[17,18,21,25,28,33,35,36],trainer:[8,9,17,28,33],transform:[8,10,13,16,17,21,28,32,37],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,31,35,37],triton:[17,22,28,29,34],tune:[17,21,28],tutori:[1,24,30],tying:28,type:[7,9],unload:29,user:26,user_sess:26,using:[17,33],util:[5,14,15],valid:21,via:26,visual:17,visualis:29,weight:25,what:28,when:16,why:37,window:[17,21,28],within:16,workflow:[16,17,27,28],wrap:[27,28],xlnet:[21,28],your:0}}) \ No newline at end of file diff --git a/v23.02.00/api/merlin_standard_lib.proto.html b/v23.02.00/api/merlin_standard_lib.proto.html index 714e13e0b..f401af205 100644 --- a/v23.02.00/api/merlin_standard_lib.proto.html +++ b/v23.02.00/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7fec97884820>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fec97884820>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fec97884820>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7fec97884820>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7fec97884820>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f81e8a21e50>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7fec97884820>, int_value: int = <betterproto._PLACEHOLDER object at 0x7fec97884820>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7fec97884820>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7fec97884820>)[source]

Bases: betterproto.Message

diff --git a/v23.02.00/api/transformers4rec.config.html b/v23.02.00/api/transformers4rec.config.html index e6a32999c..161609ba1 100644 --- a/v23.02.00/api/transformers4rec.config.html +++ b/v23.02.00/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -242,7 +242,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/v23.02.00/api/transformers4rec.torch.html b/v23.02.00/api/transformers4rec.torch.html index 47dabe4bd..b96d6a8dd 100644 --- a/v23.02.00/api/transformers4rec.torch.html +++ b/v23.02.00/api/transformers4rec.torch.html @@ -1086,7 +1086,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

diff --git a/v23.02.00/searchindex.js b/v23.02.00/searchindex.js index dc54f3d22..dde5c063e 100644 --- a/v23.02.00/searchindex.js +++ b/v23.02.00/searchindex.js @@ -1 +1 @@ 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":33,"export":[16,17,20,27,28,29],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:37,Tying:32,Using:1,achiev:1,add:27,addit:35,aggreg:[13,27,32],aliv:17,api:[6,25],approach:35,architectur:[32,35],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,30,35],benefit:1,between:37,bias:25,block:[10,21,28,32],build:[21,32],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:33,competit:35,comput:21,conda:1,config:8,connect:[17,28],consecut:26,contain:[17,29],content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,33,36],data_util:14,dataload:28,dataparallel:33,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,deploi:29,deploy:28,design:32,develop:0,distributeddataparallel:33,doc_util:5,docker:[1,29],document:6,down:29,embed:[11,28,32],embedding_util:5,encod:27,end:[17,19,24,30,34],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,36],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[34,35],featur:[11,16,20,26,27,32],feedback:1,file:[27,28,29],fine:[17,21,28],finetun:28,first:[0,16,26],format:17,free:28,from:[26,28],get:[17,22,23,24,30],gpu:[18,24,28,33],group:27,head:[12,28,32],hug:[34,35],huggingfac:37,includ:26,increment:28,indic:31,infer:[17,29,34],inform:28,initi:27,input:[16,20,21,27,28,32],instal:1,instanti:[17,28],integr:[34,37],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:[28,29],label:0,launch:17,learn:30,librari:[20,21,22,26,27,28],load:[16,17,29,36],log:25,mask:[9,32],memori:28,merlin:[31,34,35],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,29,32],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:32,multi:[18,33],next:28,nlp:37,normal:27,notebook:[1,24],nvidia:[34,35],nvtabular:[16,20,27,29,34],object:[21,22,28,30],organ:25,other:35,our:28,output:[16,20,27,32,34],over:[17,21,28],overview:[16,34],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:33,parquet:27,path:[16,20,27],perform:33,pip:1,pipelin:34,pre:[16,20,35],predict:[28,29,32],prediction_task:12,preliminari:26,preprocess:[16,17,20,26,28],process:[16,20,32],product:27,proto:3,proto_util:5,pull:[17,29],python:18,pytorch:[17,18,29,36],ranking_metr:9,raw:[16,17],read:[26,27],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,30,35],recsi:37,refer:[17,18,28],registri:2,regular:32,relat:31,relationship:37,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22,29],requir:[17,20,21,22,25,26],resourc:[31,35],respons:22,restart:28,review:29,rnn:[28,32],run:24,same:16,sampl:1,save:[16,17,21],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sent:29,sequenc:[11,32],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,28,29,34],session:[16,17,18,19,21,22,23,24,27,28,30,35],set:[17,21,22,27,28],setup:25,shut:29,side:28,start:[17,22,23,24,29,30],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,35],synthet:[20,23,24],system:29,tabl:31,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],top:29,torch:[9,10,11,12,13,14,22],torch_util:14,trace:22,train:[1,17,18,21,25,28,33,35,36],trainer:[8,9,17,28,33],transform:[8,10,13,16,17,21,28,32,34,37],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,31,35,37],triton:[17,22,28,29,34],tune:[17,21,28],tutori:[1,24,30],tying:28,type:[7,9],unload:29,user:26,user_sess:26,using:[17,33],util:[5,14,15],valid:21,via:26,visual:17,visualis:29,weight:25,what:28,when:16,why:37,window:[17,21,28],within:16,workflow:[16,17,27,28],wrap:[27,28],xlnet:[21,28],your:0}}) 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":33,"export":[16,17,20,27,28,29],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:37,Tying:32,Using:1,achiev:1,add:27,addit:35,aggreg:[13,27,32],aliv:17,api:[6,25],approach:35,architectur:[32,35],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,30,35],benefit:1,between:37,bias:25,block:[10,21,28,32],build:[21,32],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:33,competit:35,comput:21,conda:1,config:8,connect:[17,28],consecut:26,contain:[17,29],content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,33,36],data_util:14,dataload:28,dataparallel:33,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,deploi:29,deploy:28,design:32,develop:0,distributeddataparallel:33,doc_util:5,docker:[1,29],document:6,down:29,embed:[11,28,32],embedding_util:5,encod:27,end:[17,19,24,30,34],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,36],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[34,35],featur:[11,16,20,26,27,32],feedback:1,file:[27,28,29],fine:[17,21,28],finetun:28,first:[0,16,26],format:17,free:28,from:[26,28],get:[17,22,23,24,30],gpu:[18,24,28,33],group:27,head:[12,28,32],hug:[34,35],huggingfac:37,includ:26,increment:28,indic:31,infer:[17,29,34],inform:28,initi:27,input:[16,20,21,27,28,32],instal:1,instanti:[17,28],integr:[34,37],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:[28,29],label:0,launch:17,learn:30,librari:[20,21,22,26,27,28],load:[16,17,29,36],log:25,mask:[9,32],memori:28,merlin:[31,34,35],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,29,32],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:32,multi:[18,33],next:28,nlp:37,normal:27,notebook:[1,24],nvidia:[34,35],nvtabular:[16,20,27,29,34],object:[21,22,28,30],organ:25,other:35,our:28,output:[16,20,27,32,34],over:[17,21,28],overview:[16,34],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:33,parquet:27,path:[16,20,27],perform:33,pip:1,pipelin:34,pre:[16,20,35],predict:[28,29,32],prediction_task:12,preliminari:26,preprocess:[16,17,20,26,28],process:[16,20,32],product:27,proto:3,proto_util:5,pull:[17,29],python:18,pytorch:[17,18,29,36],ranking_metr:9,raw:[16,17],read:[26,27],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,30,35],recsi:37,refer:[17,18,28],registri:2,regular:32,relat:31,relationship:37,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22,29],requir:[17,20,21,22,25,26],resourc:[31,35],respons:22,restart:28,review:29,rnn:[28,32],run:24,same:16,sampl:1,save:[16,17,21],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sent:29,sequenc:[11,32],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,28,29,34],session:[16,17,18,19,21,22,23,24,27,28,30,35],set:[17,21,22,27,28],setup:25,shut:29,side:28,start:[17,22,23,24,29,30],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,35],synthet:[20,23,24],system:29,tabl:31,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],top:29,torch:[9,10,11,12,13,14,22],torch_util:14,trace:22,train:[1,17,18,21,25,28,33,35,36],trainer:[8,9,17,28,33],transform:[8,10,13,16,17,21,28,32,34,37],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,31,35,37],triton:[17,22,28,29,34],tune:[17,21,28],tutori:[1,24,30],tying:28,type:[7,9],unload:29,user:26,user_sess:26,using:[17,33],util:[5,14,15],valid:21,via:26,visual:17,visualis:29,weight:25,what:28,when:16,why:37,window:[17,21,28],within:16,workflow:[16,17,27,28],wrap:[27,28],xlnet:[21,28],your:0}}) \ No newline at end of file diff --git a/v23.04.00/api/merlin_standard_lib.proto.html b/v23.04.00/api/merlin_standard_lib.proto.html index d5eee5c9e..826f9aad8 100644 --- a/v23.04.00/api/merlin_standard_lib.proto.html +++ b/v23.04.00/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f3ab1571a00>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>, int_value: int = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7fcaf1620520>)[source]

Bases: betterproto.Message

diff --git a/v23.04.00/api/transformers4rec.config.html b/v23.04.00/api/transformers4rec.config.html index 707dad084..bbe5e1946 100644 --- a/v23.04.00/api/transformers4rec.config.html +++ b/v23.04.00/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -242,7 +242,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/v23.04.00/api/transformers4rec.torch.html b/v23.04.00/api/transformers4rec.torch.html index 5af49bacd..c47097469 100644 --- a/v23.04.00/api/transformers4rec.torch.html +++ b/v23.04.00/api/transformers4rec.torch.html @@ -989,7 +989,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

diff --git a/v23.04.00/searchindex.js b/v23.04.00/searchindex.js index 16d5d35bc..10fdfce31 100644 --- a/v23.04.00/searchindex.js +++ b/v23.04.00/searchindex.js @@ -1 +1 @@ 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":33,"export":[16,17,20,27,28,29],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:37,Tying:32,Using:1,achiev:1,add:27,addit:35,aggreg:[13,27,32],aliv:17,api:[6,25],approach:35,architectur:[32,35],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,30,35],benefit:1,between:37,bias:25,block:[10,21,28,32],build:[21,32],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:33,competit:35,comput:21,conda:1,config:8,connect:[17,28],consecut:26,contain:[17,29],content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,33,36],data_util:14,dataload:28,dataparallel:33,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,deploi:29,deploy:28,design:32,develop:0,distributeddataparallel:33,doc_util:5,docker:[1,29],document:6,down:29,embed:[11,28,32],embedding_util:5,encod:27,end:[17,19,24,30,34],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,36],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[34,35],featur:[11,16,20,26,27,32],feedback:1,file:[27,28,29],fine:[17,21,28],finetun:28,first:[0,16,26],format:17,free:28,from:[26,28],get:[17,22,23,24,30],gpu:[18,24,28,33],group:27,head:[12,28,32],hug:[34,35],huggingfac:37,includ:26,increment:28,indic:31,infer:[17,29,34],inform:28,initi:27,input:[16,20,21,27,28,32],instal:1,instanti:[17,28],integr:[34,37],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:[28,29],label:0,launch:17,learn:30,librari:[20,21,22,26,27,28],load:[16,17,29,36],log:25,mask:[9,32],memori:28,merlin:[31,34,35],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,29,32],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:32,multi:[18,33],next:28,nlp:37,normal:27,notebook:[1,24],nvidia:[34,35],nvtabular:[16,20,27,29,34],object:[21,22,28,30],organ:25,other:35,our:28,out:20,output:[16,20,27,32,34],over:[17,21,28],overview:[16,34],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:33,parquet:[27,28],path:[16,20,27],perform:33,pip:1,pipelin:34,pre:[16,20,35],predict:[28,29,32],prediction_task:12,preliminari:26,preprocess:[16,17,20,26,28],process:[16,20,32],product:27,proto:3,proto_util:5,pull:[17,29],python:18,pytorch:[17,18,29,36],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,30,35],recsi:37,refer:[17,18,28],registri:2,regular:32,relat:31,relationship:37,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22,29],requir:[17,20,21,22,25,26],resourc:[31,35],respons:22,restart:28,review:29,rnn:[28,32],run:24,same:16,sampl:1,save:[16,17,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sent:29,sequenc:[11,32],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,28,29,34],session:[16,17,18,19,21,22,23,24,27,28,30,35],set:[17,21,22,27,28],setup:25,shut:29,side:28,start:[17,22,23,24,29,30],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,35],synthet:[20,23,24],system:29,tabl:31,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],top:29,torch:[9,10,11,12,13,14,22],torch_util:14,trace:22,train:[1,17,18,21,25,28,33,35,36],trainer:[8,9,17,28,33],transform:[8,10,13,16,17,21,28,32,34,37],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,31,35,37],triton:[17,22,28,29,34],tune:[17,21,28],tutori:[1,24,30],tying:28,type:[7,9],unload:29,user:26,user_sess:26,using:[17,33],util:[5,14,15],valid:21,via:26,visual:17,visualis:29,weight:25,what:28,when:16,why:37,window:[17,21,28],within:16,workflow:[16,17,27,28],wrap:[27,28],xlnet:[21,28],your:0}}) 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":33,"export":[16,17,20,27,28,29],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:37,Tying:32,Using:1,achiev:1,add:27,addit:35,aggreg:[13,27,32],aliv:17,api:[6,25],approach:35,architectur:[32,35],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,30,35],benefit:1,between:37,bias:25,block:[10,21,28,32],build:[21,32],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:33,competit:35,comput:21,conda:1,config:8,connect:[17,28],consecut:26,contain:[17,29],content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,33,36],data_util:14,dataload:28,dataparallel:33,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,deploi:29,deploy:28,design:32,develop:0,distributeddataparallel:33,doc_util:5,docker:[1,29],document:6,down:29,embed:[11,28,32],embedding_util:5,encod:27,end:[17,19,24,30,34],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,36],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[34,35],featur:[11,16,20,26,27,32],feedback:1,file:[27,28,29],fine:[17,21,28],finetun:28,first:[0,16,26],format:17,free:28,from:[26,28],get:[17,22,23,24,30],gpu:[18,24,28,33],group:27,head:[12,28,32],hug:[34,35],huggingfac:37,includ:26,increment:28,indic:31,infer:[17,29,34],inform:28,initi:27,input:[16,20,21,27,28,32],instal:1,instanti:[17,28],integr:[34,37],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:[28,29],label:0,launch:17,learn:30,librari:[20,21,22,26,27,28],load:[16,17,29,36],log:25,mask:[9,32],memori:28,merlin:[31,34,35],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,29,32],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:32,multi:[18,33],next:28,nlp:37,normal:27,notebook:[1,24],nvidia:[34,35],nvtabular:[16,20,27,29,34],object:[21,22,28,30],organ:25,other:35,our:28,out:20,output:[16,20,27,32,34],over:[17,21,28],overview:[16,34],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:33,parquet:[27,28],path:[16,20,27],perform:33,pip:1,pipelin:34,pre:[16,20,35],predict:[28,29,32],prediction_task:12,preliminari:26,preprocess:[16,17,20,26,28],process:[16,20,32],product:27,proto:3,proto_util:5,pull:[17,29],python:18,pytorch:[17,18,29,36],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,30,35],recsi:37,refer:[17,18,28],registri:2,regular:32,relat:31,relationship:37,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22,29],requir:[17,20,21,22,25,26],resourc:[31,35],respons:22,restart:28,review:29,rnn:[28,32],run:24,same:16,sampl:1,save:[16,17,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sent:29,sequenc:[11,32],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,28,29,34],session:[16,17,18,19,21,22,23,24,27,28,30,35],set:[17,21,22,27,28],setup:25,shut:29,side:28,start:[17,22,23,24,29,30],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,35],synthet:[20,23,24],system:29,tabl:31,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],top:29,torch:[9,10,11,12,13,14,22],torch_util:14,trace:22,train:[1,17,18,21,25,28,33,35,36],trainer:[8,9,17,28,33],transform:[8,10,13,16,17,21,28,32,34,37],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,31,35,37],triton:[17,22,28,29,34],tune:[17,21,28],tutori:[1,24,30],tying:28,type:[7,9],unload:29,user:26,user_sess:26,using:[17,33],util:[5,14,15],valid:21,via:26,visual:17,visualis:29,weight:25,what:28,when:16,why:37,window:[17,21,28],within:16,workflow:[16,17,27,28],wrap:[27,28],xlnet:[21,28],your:0}}) \ No newline at end of file diff --git a/v23.05.00/api/merlin_standard_lib.proto.html b/v23.05.00/api/merlin_standard_lib.proto.html index 6003cfb4b..dc9a4c0e7 100644 --- a/v23.05.00/api/merlin_standard_lib.proto.html +++ b/v23.05.00/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>, int_value: int = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7fcc0c1faf70>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f1ead84b0a0>)[source]

Bases: betterproto.Message

diff --git a/v23.05.00/api/transformers4rec.config.html b/v23.05.00/api/transformers4rec.config.html index 2185d25f3..e8ea5c2e1 100644 --- a/v23.05.00/api/transformers4rec.config.html +++ b/v23.05.00/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -242,7 +242,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/v23.05.00/api/transformers4rec.torch.html b/v23.05.00/api/transformers4rec.torch.html index 179592ad1..4e13a04c8 100644 --- a/v23.05.00/api/transformers4rec.torch.html +++ b/v23.05.00/api/transformers4rec.torch.html @@ -989,7 +989,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 0, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

diff --git a/v23.05.00/searchindex.js b/v23.05.00/searchindex.js index 1001bc72f..34463db15 100644 --- a/v23.05.00/searchindex.js +++ b/v23.05.00/searchindex.js @@ -1 +1 @@ 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Triton for Recommender Systems","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments 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\ No newline at end of file diff --git a/v23.06.00/api/merlin_standard_lib.proto.html b/v23.06.00/api/merlin_standard_lib.proto.html index 6ef4131f5..e89812035 100644 --- a/v23.06.00/api/merlin_standard_lib.proto.html +++ b/v23.06.00/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f58c8a3ca00>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>, int_value: int = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7f8d1d4cb370>)[source]

Bases: betterproto.Message

diff --git a/v23.06.00/api/transformers4rec.config.html b/v23.06.00/api/transformers4rec.config.html index e40a5d024..e0bf509b4 100644 --- a/v23.06.00/api/transformers4rec.config.html +++ b/v23.06.00/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -243,7 +243,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/v23.06.00/api/transformers4rec.torch.html b/v23.06.00/api/transformers4rec.torch.html index 7431be97a..10a8fb648 100644 --- a/v23.06.00/api/transformers4rec.torch.html +++ b/v23.06.00/api/transformers4rec.torch.html @@ -1141,7 +1141,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments 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\ No newline at end of file diff --git a/v23.08.00/api/merlin_standard_lib.proto.html b/v23.08.00/api/merlin_standard_lib.proto.html index 450b6af9c..b4b3813d4 100644 --- a/v23.08.00/api/merlin_standard_lib.proto.html +++ b/v23.08.00/api/merlin_standard_lib.proto.html @@ -288,7 +288,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>)[source]
+class merlin_standard_lib.proto.schema_bp.ValueCountList(value_count: List[ForwardRef('ValueCount')] = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>)[source]

Bases: betterproto.Message

@@ -663,7 +663,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureIndexFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>)[source]

Bases: betterproto.Message

@@ -674,7 +674,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>)[source]
+class merlin_standard_lib.proto.schema_bp.SparseFeatureValueFeature(name: str = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>)[source]

Bases: betterproto.Message

@@ -1077,7 +1077,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>)[source]
+class merlin_standard_lib.proto.schema_bp.FeatureComparator(infinity_norm: 'InfinityNorm' = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>, jensen_shannon_divergence: 'JensenShannonDivergence' = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>)[source]

Bases: betterproto.Message

@@ -1145,7 +1145,7 @@

Submodules
-class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>, int_value: int = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7fe1caf2dac0>)[source]
+class merlin_standard_lib.proto.schema_bp.TensorRepresentationDefaultValue(float_value: float = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>, int_value: int = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>, bytes_value: bytes = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>, uint_value: int = <betterproto._PLACEHOLDER object at 0x7fa92f8dddf0>)[source]

Bases: betterproto.Message

diff --git a/v23.08.00/api/transformers4rec.config.html b/v23.08.00/api/transformers4rec.config.html index 48cb9f90f..1b1c79b2f 100644 --- a/v23.08.00/api/transformers4rec.config.html +++ b/v23.08.00/api/transformers4rec.config.html @@ -150,7 +150,7 @@

Submodules

transformers4rec.config.trainer module

-class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

@@ -243,7 +243,7 @@

Submodules
-class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.config.trainer.T4RecTrainingArgumentsTF(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers4rec.config.trainer.T4RecTrainingArguments, transformers.training_args_tf.TFTrainingArguments

Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments

diff --git a/v23.08.00/api/transformers4rec.torch.html b/v23.08.00/api/transformers4rec.torch.html index 56a959811..ec5c0aa0d 100644 --- a/v23.08.00/api/transformers4rec.torch.html +++ b/v23.08.00/api/transformers4rec.torch.html @@ -1141,7 +1141,7 @@

Submodules
-class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_cpu: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: Union[str, List[transformers.debug_utils.DebugOption]] = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: Optional[Union[List[transformers.trainer_utils.ShardedDDPOption], str]] = '', fsdp: Optional[Union[List[transformers.trainer_utils.FSDPOption], str]] = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, ddp_broadcast_buffers: Optional[bool] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, hub_always_push: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, dispatch_batches: Optional[bool] = None, include_tokens_per_second: Optional[bool] = False, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]
+class transformers4rec.torch.T4RecTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = False, do_predict: bool = False, evaluation_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, eval_delay: Optional[float] = 0, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = - 1, lr_scheduler_type: Union[transformers.trainer_utils.SchedulerType, str] = 'linear', warmup_ratio: float = 0.0, warmup_steps: int = 0, log_level: Optional[str] = 'passive', log_level_replica: Optional[str] = 'warning', log_on_each_node: bool = True, logging_dir: Optional[str] = None, logging_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', logging_first_step: bool = False, logging_steps: float = 500, logging_nan_inf_filter: bool = True, save_strategy: Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps', save_steps: float = 500, save_total_limit: Optional[int] = None, save_safetensors: Optional[bool] = False, save_on_each_node: bool = False, no_cuda: bool = False, use_mps_device: bool = False, seed: int = 42, data_seed: Optional[int] = None, jit_mode_eval: bool = False, use_ipex: bool = False, bf16: bool = False, fp16: bool = False, fp16_opt_level: str = 'O1', half_precision_backend: str = 'auto', bf16_full_eval: bool = False, fp16_full_eval: bool = False, tf32: Optional[bool] = None, local_rank: int = - 1, ddp_backend: Optional[str] = None, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: str = '', dataloader_drop_last: bool = False, eval_steps: Optional[float] = None, dataloader_num_workers: int = 0, past_index: int = - 1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: str = '', fsdp: str = '', fsdp_min_num_params: int = 0, fsdp_config: Optional[str] = None, fsdp_transformer_layer_cls_to_wrap: Optional[str] = None, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, optim: Union[transformers.training_args.OptimizerNames, str] = 'adamw_hf', optim_args: Optional[str] = None, adafactor: bool = False, group_by_length: bool = False, length_column_name: Optional[str] = 'length', report_to: Optional[List[str]] = None, ddp_find_unused_parameters: Optional[bool] = None, ddp_bucket_cap_mb: Optional[int] = None, dataloader_pin_memory: bool = True, skip_memory_metrics: bool = True, use_legacy_prediction_loop: bool = False, push_to_hub: bool = False, resume_from_checkpoint: Optional[str] = None, hub_model_id: Optional[str] = None, hub_strategy: Union[transformers.trainer_utils.HubStrategy, str] = 'every_save', hub_token: Optional[str] = None, hub_private_repo: bool = False, gradient_checkpointing: bool = False, include_inputs_for_metrics: bool = False, fp16_backend: str = 'auto', push_to_hub_model_id: Optional[str] = None, push_to_hub_organization: Optional[str] = None, push_to_hub_token: Optional[str] = None, mp_parameters: str = '', auto_find_batch_size: bool = False, full_determinism: bool = False, torchdynamo: Optional[str] = None, ray_scope: Optional[str] = 'last', ddp_timeout: Optional[int] = 1800, torch_compile: bool = False, torch_compile_backend: Optional[str] = None, torch_compile_mode: Optional[str] = None, xpu_backend: Optional[str] = None, max_sequence_length: Optional[int] = None, shuffle_buffer_size: int = 0, data_loader_engine: str = 'merlin', eval_on_test_set: bool = False, eval_steps_on_train_set: int = 20, predict_top_k: int = 100, learning_rate_num_cosine_cycles_by_epoch: float = 1.25, log_predictions: bool = False, compute_metrics_each_n_steps: int = 1, experiments_group: str = 'default')[source]

Bases: transformers.training_args.TrainingArguments

Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation

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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":32,"export":[16,20,27],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:36,Tying:31,Using:1,achiev:1,add:27,addit:34,aggreg:[13,27,31],aliv:17,api:[6,25],approach:34,architectur:[31,34],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,29,34],benefit:1,between:36,bias:25,block:[10,21,28,31],build:[21,31],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:32,competit:34,comput:21,conda:1,config:8,connect:[17,28],consecut:26,content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,32,35],data_util:14,dataload:28,dataparallel:32,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,design:31,develop:0,distributeddataparallel:32,doc_util:5,docker:1,document:6,embed:[11,28,31],embedding_util:5,encod:27,end:[17,19,24,29,33],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,35],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[33,34],featur:[11,16,20,26,27,31],feedback:1,file:[27,28],fine:[17,21,28],finetun:28,first:[0,16,26],free:28,from:[26,28],get:[17,22,23,24,29],gpu:[18,24,28,32],group:27,head:[12,28,31],hug:[33,34],huggingfac:36,includ:26,increment:28,indic:30,infer:[17,33],inform:28,initi:27,input:[16,20,21,27,28,31],instal:1,instanti:[17,28],integr:[33,36],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:28,label:0,launch:17,learn:29,librari:[20,21,22,26,27,28],load:[16,17,35],log:25,mask:[9,31],memori:28,merlin:[30,33,34],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,31],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:31,multi:[18,32],next:28,nlp:36,normal:27,notebook:[1,24],nvidia:[33,34],nvtabular:[16,20,27,33],object:[21,22,28,29],organ:25,other:34,our:28,out:20,output:[16,20,27,31,33],over:[17,21,28],overview:[16,33],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:32,parquet:[27,28],path:[16,20,27],perform:32,pip:1,pipelin:33,pre:[16,20,34],predict:[28,31],prediction_task:12,preliminari:26,preprocess:[16,20,26],process:[16,20,31],product:27,proto:3,proto_util:5,python:18,pytorch:[17,18,35],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,34],recsi:36,refer:[17,18,28],registri:2,regular:31,relat:30,relationship:36,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22],requir:[20,21,22,25,26],resourc:[30,34],respons:22,restart:28,rnn:[28,31],run:24,same:16,sampl:1,save:[16,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sequenc:[11,31],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,33],session:[16,17,18,19,21,22,23,24,27,28,29,34],set:[17,21,22,27,28],setup:25,side:28,start:[17,22,23,24,29],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,34],synthet:[20,23,24],tabl:30,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],torch:[9,10,11,12,13,14,22],torch_util:14,trace:[17,22],train:[1,17,18,21,25,28,32,34,35],trainer:[8,9,17,28,32],transform:[8,10,13,16,17,21,28,31,33,36],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,30,34,36],triton:[17,22,33],tune:[17,21,28],tutori:[1,24,29],tying:28,type:[7,9],user:26,user_sess:26,using:[17,32],util:[5,14,15],valid:21,via:26,visual:17,weight:25,what:28,when:16,why:36,window:[17,21,28],within:16,workflow:[16,27],wrap:[27,28],xlnet:[21,28],your:0}}) 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to Transformers4Rec","Transformers4Rec","merlin_standard_lib package","merlin_standard_lib.proto package","merlin_standard_lib.schema package","merlin_standard_lib.utils package","API Documentation","transformers4rec package","transformers4rec.config package","transformers4rec.torch package","transformers4rec.torch.block package","transformers4rec.torch.features package","transformers4rec.torch.model package","transformers4rec.torch.tabular package","transformers4rec.torch.utils package","transformers4rec.utils package","ETL with NVTabular","End-to-end session-based recommendations with PyTorch","Multi-GPU training for session-based recommendations with PyTorch","End-to-end session-based recommendation","ETL with NVTabular","Session-based Recommendation with XLNET","Serving a Session-based Recommendation model with Torch Backend","Getting Started: Session-based Recommendation with Synthetic Data","Transformers4Rec Example Notebooks","Transformers4Rec paper - Experiments reproducibility","Preliminary Preprocessing","ETL with NVTabular","Session-based recommendation with Transformers4Rec","Tutorial: End-to-end Session-based Recommendation","Merlin Transformers4Rec","Model Architectures","Multi-GPU data-parallel training using the Trainer class","End-to-End Pipeline with Hugging Face Transformers and NVIDIA Merlin","Additional Resources","Training and Evaluation","Why Transformers4Rec?"],titleterms:{"class":32,"export":[16,20,27],"function":28,"import":[20,21,22,26,27,28],"new":16,PRs:0,The:36,Tying:31,Using:1,achiev:1,add:27,addit:34,aggreg:[13,27,31],aliv:17,api:[6,25],approach:34,architectur:[31,34],argument:[17,21,28],attribut:0,averag:17,backend:22,base:[10,11,17,18,19,21,22,23,24,28,29,34],benefit:1,between:36,bias:25,block:[10,21,28,31],build:[21,31],calcul:26,categor:27,categorifi:26,check:[17,20],clean:16,code:[0,1,25],column:26,command:25,comparison:32,competit:34,comput:21,conda:1,config:8,connect:[17,28],consecut:26,content:[2,3,4,5,7,8,9,10,11,12,13,14,15],context:25,continu:[11,27],contribut:0,convert:26,creat:[16,18,20,27],csv:26,cudf:26,dai:[16,20],daili:[17,21,28],data:[16,17,20,21,23,24,26,27,32,35],data_util:14,dataload:28,dataparallel:32,dataset:[25,27],datetim:26,defin:[1,16,17,20,21,28],definit:17,depend:15,design:31,develop:0,distributeddataparallel:32,doc_util:5,docker:1,document:6,embed:[11,28,31],embedding_util:5,encod:27,end:[17,19,24,29,33],engin:[16,20],ensembl:17,etl:[16,20,27],evalu:[17,21,25,28,35],exampl:[1,24],examples_util:14,execut:[16,18],experi:[24,25],extract:27,face:[33,34],featur:[11,16,20,26,27,31],feedback:1,file:[27,28],fine:[17,21,28],finetun:28,first:[0,16,26],free:28,from:[26,28],get:[17,22,23,24,29],gpu:[18,24,28,32],group:27,head:[12,28,31],hug:[33,34],huggingfac:36,includ:26,increment:28,indic:30,infer:[17,33],inform:28,initi:27,input:[16,20,21,27,28,31],instal:1,instanti:[17,28],integr:[33,36],interact:[16,26,27],introduct:[27,28],inventori:24,issu:0,item:[16,26,28],kernel:28,label:0,launch:17,learn:29,librari:[20,21,22,26,27,28],load:[16,17,35],log:25,mask:[9,31],memori:28,merlin:[30,33,34],merlin_standard_lib:[2,3,4,5],metric:[17,21,28],misc_util:5,mlp:10,model:[1,12,17,21,22,28,31],modul:[2,3,4,5,7,8,9,10,11,12,13,14,15,21,28],modular:31,multi:[18,32],next:28,nlp:36,normal:27,notebook:[1,24],nvidia:[33,34],nvtabular:[16,20,27,33],object:[21,22,28,29],organ:25,other:34,our:28,out:20,output:[16,20,27,31,33],over:[17,21,28],overview:[16,33],packag:[2,3,4,5,7,8,9,10,11,12,13,14,15],paper:[24,25],parallel:32,parquet:[27,28],path:[16,20,27],perform:32,pip:1,pipelin:33,pre:[16,20,34],predict:[28,31],prediction_task:12,preliminari:26,preprocess:[16,20,26],process:[16,20,31],product:27,proto:3,proto_util:5,python:18,pytorch:[17,18,35],ranking_metr:9,raw:[16,17],read:[26,27,28],recenc:[26,27],recommend:[17,18,19,21,22,23,24,28,29,34],recsi:36,refer:[17,18,28],registri:2,regular:31,relat:30,relationship:36,releas:25,remark:25,remov:[16,26],repeat:[16,26],reproduc:[24,25],request:[17,22],requir:[20,21,22,25,26],resourc:[30,34],respons:22,restart:28,rnn:[28,31],run:24,same:16,sampl:1,save:[16,21,28],schema:[4,8,17,21,22,28],schema_bp:3,schema_util:14,script:18,season:0,seen:[16,26],send:[17,22],sequenc:[11,31],sequenti:[16,21,27],sequentialblock:28,serv:[17,22],server:[17,22,33],session:[16,17,18,19,21,22,23,24,27,28,29,34],set:[17,21,22,27,28],setup:25,side:28,start:[17,22,23,24,29],submodul:[2,3,4,5,7,8,9,10,11,12,13,14,15],subpackag:[2,7,9],support:[1,34],synthet:[20,23,24],tabl:30,tabular:[11,13],tabularsequencefeatur:28,tag:4,tempor:27,text:11,time:[17,21,26,28],timestamp:[16,26],torch:[9,10,11,12,13,14,22],torch_util:14,trace:[17,22],train:[1,17,18,21,25,28,32,34,35],trainer:[8,9,17,28,32],transform:[8,10,13,16,17,21,28,31,33,36],transformers4rec:[0,1,7,8,9,10,11,12,13,14,15,17,21,24,25,28,30,34,36],triton:[17,22,33],tune:[17,21,28],tutori:[1,24,29],tying:28,type:[7,9],user:26,user_sess:26,using:[17,32],util:[5,14,15],valid:21,via:26,visual:17,weight:25,what:28,when:16,why:36,window:[17,21,28],within:16,workflow:[16,27],wrap:[27,28],xlnet:[21,28],your:0}}) 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