Improve nebullvm logging (#141)

* improved logs

* upgrade version numbers

* fixed links on speedster readme & added save/load example to all notebooks

apps/accelerate/speedster/README.md

@@ -38,7 +38,7 @@ python -m nebullvm.installers.auto_installer --backends all --compilers all
 > on your environment before proceeding to the next step, please install it from this 
 > [link](https://pytorch.org/get-started/locally/).
 
-For more details on the installation step, please visit [Installation](https://docs.nebuly.com/speedster/installation).
+For more details on the installation step, please visit [Installation](https://docs.nebuly.com/modules/speedster/installation).
 
 
 # API quick view
@@ -116,8 +116,8 @@ Don't forget to leave a star ⭐ to support the project and happy acceleration 
 
 
 <p align="center">
-<a href="https://docs.nebuly.com/speedster/installation">Installation</a> •
-<a href="https://docs.nebuly.com/speedster/get-started">Get started</a> •
+<a href="https://docs.nebuly.com/modules/speedster/installation">Installation</a> •
+<a href="https://docs.nebuly.com/modules/speedster/getting-started">Get started</a> •
 <a href="https://github.com/nebuly-ai/nebullvm/tree/main/notebooks">Notebooks</a> •
-<a href="https://docs.nebuly.com/speedster/benchmarks">Benchmarks</a>
+<a href="https://docs.nebuly.com/modules/speedster/benchmarks">Benchmarks</a>
 </p>

apps/accelerate/speedster/setup.py

@@ -12,7 +12,7 @@
 
 setup(
     name="speedster",
-    version="0.0.2",
+    version="0.1.0",
     packages=find_packages(),
     install_requires=REQUIREMENTS,
     long_description=long_description,

apps/accelerate/speedster/speedster/api/functions.py

@@ -9,13 +9,14 @@
     Optional,
 )
 
+from loguru import logger
 from nebullvm.optional_modules.tensorflow import tensorflow as tf
 from nebullvm.optional_modules.torch import torch
 from nebullvm.tools.base import Device
+from nebullvm.tools.logger import debug_mode_enabled, LoggingContext
 from nebullvm.tools.utils import gpu_is_available
-from speedster.root_op import SpeedsterRootOp
 
-logger = logging.getLogger("nebullvm_logger")
+from speedster.root_op import SpeedsterRootOp
 
 
 def _check_device(device: Optional[str]) -> Device:
@@ -143,18 +144,22 @@ def optimize_model(
     """
     root_op = SpeedsterRootOp()
     device = _check_device(device)
-    root_op.to(device).execute(
-        model=model,
-        input_data=input_data,
-        metric_drop_ths=metric_drop_ths,
-        metric=metric,
-        optimization_time=optimization_time,
-        dynamic_info=dynamic_info,
-        config_file=config_file,
-        ignore_compilers=ignore_compilers,
-        ignore_compressors=ignore_compressors,
-        store_latencies=store_latencies,
-        **kwargs,
-    )
+
+    disable_log = True if not debug_mode_enabled() else False
+
+    with LoggingContext(logging.getLogger(), disabled=disable_log):
+        root_op.to(device).execute(
+            model=model,
+            input_data=input_data,
+            metric_drop_ths=metric_drop_ths,
+            metric=metric,
+            optimization_time=optimization_time,
+            dynamic_info=dynamic_info,
+            config_file=config_file,
+            ignore_compilers=ignore_compilers,
+            ignore_compressors=ignore_compressors,
+            store_latencies=store_latencies,
+            **kwargs,
+        )
 
     return root_op.get_result()

nebullvm/api/functions.py

@@ -1,4 +1,3 @@
-import logging
 from typing import (
     Union,
     Iterable,
@@ -9,13 +8,13 @@
     Optional,
 )
 
+from loguru import logger
+
 from nebullvm.optional_modules.tensorflow import tensorflow as tf
 from nebullvm.optional_modules.torch import torch
 from nebullvm.tools.base import Device
 from nebullvm.tools.utils import gpu_is_available
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def _check_device(device: Optional[str]) -> Device:
     if device is None:

nebullvm/config.py

@@ -1,7 +1,7 @@
 from nebullvm.optional_modules.torch import torch
 
 
-VERSION = "0.6.1"
+VERSION = "0.7.0"
 LEARNER_METADATA_FILENAME = "metadata.json"
 ONNX_OPSET_VERSION = 13
 NEBULLVM_DEBUG_FILE = "nebullvm_debug.json"

nebullvm/installers/auto_installer.py

@@ -1,7 +1,8 @@
 import argparse
-import logging
 from typing import List, Union
 
+from loguru import logger
+
 from nebullvm.config import (
     ONNX_MODULES,
     TENSORFLOW_MODULES,
@@ -16,8 +17,6 @@
 )
 
 
-logger = logging.getLogger("nebullvm_logger")
-
 SUPPORTED_BACKENDS = [
     "torch-full",
     "torch-base",

nebullvm/installers/installers.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import platform
 import subprocess
@@ -8,6 +7,7 @@
 from typing import List, Union
 
 import cpuinfo
+from loguru import logger
 
 from nebullvm.config import (
     LIBRARIES_GPU,
@@ -25,8 +25,6 @@
     check_module_version,
 )
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def get_cpu_arch():
     arch = cpuinfo.get_cpu_info()["arch"].lower()

nebullvm/operations/base.py

@@ -1,13 +1,12 @@
 import abc
-import logging
 from typing import Dict, Any, Optional, Union
 
+from loguru import logger
+
 from nebullvm.tools.base import Device
 from nebullvm.tools.feedback_collector import FeedbackCollector
 from nebullvm.tools.utils import gpu_is_available
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def _check_device(device: Optional[str]) -> Device:
     if device is None:

nebullvm/operations/conversions/pytorch.py

@@ -1,6 +1,7 @@
-import logging
 from pathlib import Path
 
+from loguru import logger
+
 from nebullvm.config import ONNX_OPSET_VERSION
 from nebullvm.optional_modules.torch import torch, Module
 from nebullvm.tools.base import ModelParams, Device
@@ -10,8 +11,6 @@
     create_model_inputs_torch,
 )
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def convert_torch_to_onnx(
     torch_model: Module,

nebullvm/operations/conversions/tensorflow.py

@@ -1,17 +1,16 @@
-import logging
 import subprocess
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Union
 
+from loguru import logger
+
 from nebullvm.config import ONNX_OPSET_VERSION
 from nebullvm.optional_modules.tensorflow import tensorflow as tf, tf2onnx
 from nebullvm.optional_modules.onnx import onnx
 from nebullvm.tools.base import ModelParams
 from nebullvm.tools.huggingface import TensorFlowTransformerWrapper
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def convert_tf_to_onnx(
     model: Union[tf.Module, tf.keras.Model],

nebullvm/operations/inference_learners/deepsparse.py

@@ -1,11 +1,11 @@
-import logging
 import os
 import shutil
 from abc import ABC
 from pathlib import Path
 from typing import Union, List, Generator, Tuple, Dict, Type
 
 import numpy as np
+from loguru import logger
 
 from nebullvm.config import ONNX_FILENAMES
 from nebullvm.operations.inference_learners.base import (
@@ -18,8 +18,6 @@
 from nebullvm.tools.base import ModelParams, DeepLearningFramework
 from nebullvm.tools.transformations import MultiStageTransformation
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 class DeepSparseInferenceLearner(BaseInferenceLearner, ABC):
     """Model optimized on CPU using DeepSparse. DeepSparse is an engine

nebullvm/operations/inference_learners/neural_compressor.py

@@ -1,9 +1,10 @@
-import logging
 import pickle
 from abc import ABC
 from pathlib import Path
 from typing import Union, Tuple, Dict, Type
 
+from loguru import logger
+
 from nebullvm.operations.inference_learners.base import (
     BaseInferenceLearner,
     LearnerMetadata,
@@ -29,8 +30,6 @@
 from nebullvm.tools.transformations import MultiStageTransformation
 from nebullvm.tools.utils import check_module_version
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 class NeuralCompressorInferenceLearner(BaseInferenceLearner, ABC):
     """Model optimized on CPU using IntelNeuralCompressor.

nebullvm/operations/inference_learners/onnx.py

@@ -1,4 +1,3 @@
-import logging
 import multiprocessing
 import os
 import shutil
@@ -8,6 +7,7 @@
 
 import cpuinfo
 import numpy as np
+from loguru import logger
 
 from nebullvm.config import (
     ONNX_FILENAMES,
@@ -27,8 +27,6 @@
 from nebullvm.tools.base import DeepLearningFramework, Device, ModelParams
 from nebullvm.tools.transformations import MultiStageTransformation
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def _running_on_intel_cpu(use_gpu):
     if use_gpu:

nebullvm/operations/inference_learners/openvino.py

@@ -1,11 +1,11 @@
 import json
-import logging
 import shutil
 from abc import ABC
 from pathlib import Path
 from typing import Dict, Union, Type, Generator, Tuple, List, Optional
 
 import numpy as np
+from loguru import logger
 
 from nebullvm.config import OPENVINO_FILENAMES
 from nebullvm.operations.inference_learners.base import (
@@ -27,8 +27,6 @@
 from nebullvm.tools.data import DataManager
 from nebullvm.tools.transformations import MultiStageTransformation
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 class OpenVinoInferenceLearner(BaseInferenceLearner, ABC):
     """Model optimized using OpenVINO.

nebullvm/operations/inference_learners/tensor_rt.py

@@ -1,12 +1,12 @@
 import json
-import logging
 import os
 from abc import ABC
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Any, Union, Dict, Type, List, Tuple, Generator, Optional
 
 import numpy as np
+from loguru import logger
 
 from nebullvm.config import NVIDIA_FILENAMES
 from nebullvm.operations.inference_learners.base import (
@@ -26,8 +26,6 @@
     VerifyContiguity,
 )
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 class ONNXTensorRTInferenceLearner(BaseInferenceLearner, ABC):
     """Model optimized using TensorRT.

nebullvm/operations/measures/utils.py

@@ -1,8 +1,8 @@
-import logging
 import time
 from typing import Tuple, List, Union, Any
 
 import numpy as np
+from loguru import logger
 
 from nebullvm.config import ONNX_PROVIDERS
 from nebullvm.operations.inference_learners.base import BaseInferenceLearner
@@ -16,8 +16,6 @@
     get_output_names,
 )
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def compute_torch_latency(
     xs: List[Tuple[torch.Tensor]],

nebullvm/operations/optimizations/compilers/onnxruntime.py

@@ -14,12 +14,6 @@
 )
 from nebullvm.tools.base import QuantizationType
 from nebullvm.tools.data import DataManager
-from nebullvm.tools.logger import (
-    debug_mode_enabled,
-    save_root_logger_state,
-    raise_logger_level,
-    load_root_logger_state,
-)
 from nebullvm.tools.transformations import MultiStageTransformation
 
 
@@ -80,18 +74,11 @@ def execute(
             QUANTIZATION_DATA_NUM
         )
 
-        if not debug_mode_enabled():
-            logger_state = save_root_logger_state()
-            raise_logger_level()
-
         if quantization_type is not None:
             model = self._quantize_model(
                 model, train_input_data, quantization_type, input_tfms
             )
 
-        if not debug_mode_enabled():
-            load_root_logger_state(logger_state)
-
         self.compiled_model = self._compile_model(model)
 
     def _compile_model(self, model: Union[str, Path]):

nebullvm/operations/optimizations/compilers/quantizations/pytorch.py

@@ -1,7 +1,8 @@
 import copy
-import logging
 from typing import List, Tuple, Union
 
+from loguru import logger
+
 from nebullvm.optional_modules.torch import (
     torch,
     Module,
@@ -21,8 +22,6 @@
 )
 from nebullvm.tools.utils import check_module_version
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 class _QuantWrapper(Module):
     def __init__(self, model: Module):

nebullvm/operations/optimizations/compilers/quantizations/utils.py

@@ -1,9 +1,7 @@
-import logging
+from loguru import logger
 
 from nebullvm.tools.base import QuantizationType
 
-logger = logging.getLogger("nebullvm_logger")
-
 
 def check_quantization(
     quantization_type: QuantizationType, perf_loss_ths: float