diff --git a/.gitignore b/.gitignore index 134275a..50eefed 100644 --- a/.gitignore +++ b/.gitignore @@ -11,7 +11,7 @@ tests/__pycache__/ *.log *.log.* *.log.* - +wandb # anndata files # only track data files in data/demo data/* diff --git a/conf/train_conf.yaml b/conf/train_conf.yaml index 4b7a9d8..c0b568b 100644 --- a/conf/train_conf.yaml +++ b/conf/train_conf.yaml @@ -8,7 +8,7 @@ # python train.py dataset.basename=my_awesome_dataset # Here are some defaults: -embedding_method: "geneformer" #This refers to the precomputed "embeddings" in .obsm of the anndata object accessible throgh the sharelink in the dataset +embedding_method: "scvi" #This refers to the precomputed "embeddings" in .obsm of the anndata object accessible throgh the sharelink in the dataset input_dim_map: hvg: 2000 @@ -18,7 +18,7 @@ input_dim_map: dataset: basename: "geo_7k_cellxgene_3_5K" - type: "pairs" + type: "multiplets" test_datasets: - "jo-mengr/bowel_disease_single" @@ -27,32 +27,31 @@ text_encoder: freeze_text_encoder: True unfreeze_last_n_layers: 0 -#loss: "MultipleNegativesRankingLoss" -loss: "ContrastiveLoss" +loss: "MultipleNegativesRankingLoss" +#loss: "ContrastiveLoss" #evaluator: "TripletEvaluator" # If not provided, the default evaluator is used adapter: omics_input_dim: None #this will be overwritten by the embedding_dim_map hidden_dim: 512 - output_dim: 2048 + output_dim: 1024 trainer: unfreeze_epoch: 0.03 output_dir: "../../models/trained" - num_train_epochs: 64 - per_device_train_batch_size: 128 - per_device_eval_batch_size: 128 + num_train_epochs: 32 + per_device_train_batch_size: 64 + per_device_eval_batch_size: 54 learning_rate: 2e-5 warmup_ratio: 0.1 - fp16: true + fp16: false bf16: false eval_strategy: "steps" - eval_steps: 500 + eval_steps: 100 save_strategy: "steps" - save_steps: 2000 + save_steps: 500 save_total_limit: 2 - logging_steps: 500 - run_name: "mmcontext" + logging_steps: 100 save_dir: "out" # You could also define logging via Hydra’s logging config if desired, diff --git a/pyproject.toml b/pyproject.toml index 6d4b5fa..ab53561 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,6 +46,7 @@ dependencies = [ "sentence-transformers", "session-info", "torch", + "wandb>=0.19.6", "zarr<=2.18.4", ] optional-dependencies.dev = [ diff --git a/scripts/start_job.slurm b/scripts/start_job.slurm index eac15b8..7af5b68 100644 --- a/scripts/start_job.slurm +++ b/scripts/start_job.slurm @@ -3,12 +3,12 @@ #SBATCH --output=mmcontext_slurm.out #SBATCH --error=mmcontext_slurm.err #SBATCH --partition=gpu -#SBATCH --gres=gpu:1 # Request amount of GPUs +#SBATCH --gres=gpu:2 # Request amount of GPUs #SBATCH --mem=64G # Request 64GB of host RAM, not GPU VRAM! #SBATCH --time=01:00:00 # Max job time of 1 hour source .venv/bin/activate # Now run your Python script -#accelerate launch --num_processes 2 scripts/train_hydra.py -python3 scripts/train_hydra.py +accelerate launch --num_processes 2 scripts/train_hydra.py +#python3 scripts/train_hydra.py diff --git a/scripts/train_hydra.py b/scripts/train_hydra.py index 56be35b..6767eaa 100644 --- a/scripts/train_hydra.py +++ b/scripts/train_hydra.py @@ -17,6 +17,7 @@ SentenceTransformerTrainingArguments, ) from sentence_transformers.evaluation import BinaryClassificationEvaluator +from transformers.integrations import WandbCallback from mmcontext.engine.callback import UnfreezeTextEncoderCallback from mmcontext.eval import SystemMonitor, zero_shot_classification_roc @@ -123,7 +124,7 @@ def main(cfg: DictConfig): save_steps=cfg.trainer.save_steps, save_total_limit=cfg.trainer.save_total_limit, logging_steps=cfg.trainer.logging_steps, - run_name=cfg.trainer.run_name, + run_name=str(hydra_run_dir), ) # ------------------------------------------------------------------------- @@ -144,7 +145,7 @@ def main(cfg: DictConfig): loss=loss_obj, evaluator=dev_evaluator, extra_feature_keys=["omics_representation"], - callbacks=[unfreeze_callback], + callbacks=[unfreeze_callback, WandbCallback()], ) 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