Metadata-Version: 2.1
Name: mlpug
Version: 0.0.51
Summary: A machine learning library agnostic framework for model training
Home-page: https://github.com/nuhame/mlpug
Author: Freddy Snijder
Author-email: mlpug@visionscapers.com
License: UNKNOWN
Description: # MLPug
        MLPug is a Machine Learning library agnostic framework for model training. A lot of the functionality you need to train your model is independent of the 
        ML library you're using, e.g. PyTorch or Tensorflow. MLPug provides a single framework with a unified API for all such training functionality,
        independent of the ML library you are using. 
        
        **Thus, when switching ML library, you don't have to learn a new training API and you can reuse your own training code with no, or minimal, change! 🤩🎉**
        
        ## Dive right in!
        
        ### Run the repository examples
        
        You can find the example code [here](mlpug/examples/documentation/). 
        How MLPug is used in the examples is explained further [here](#hello-world-with-pytorch).
        
        Clone the MLPug repo:
        
        ```
        git clone https://github.com/nuhame/mlpug.git
        ```
        
        #### MLPug with PyTorch
        To run the PyTorch examples, install PyTorch first, further use Python >= 3.7.
        ```
        cd mlpug
        
        # MLPug Hello World example
        python mlpug/examples/documentation/pytorch/hello_world.py
        
        # MLPug Fashion MNIST example
        # Run `fashion_mnist.py -h` for options
        python mlpug/examples/documentation/pytorch/fashion_mnist.py
        ```
        
        There are similar [examples for using MLPug with PyTorch/XLA](mlpug/examples/documentation/pytorch/xla) (Training with Pytorch on TPUs).
        
        #### MLPug with Tensorflow
        To run the Tensorflow examples, install Tensorflow first, further use Python >= 3.7.
        ```
        cd mlpug
        
        # MLPug Hello World example
        # Run hello_world.py or hello_world_not_eager.py
        python mlpug/examples/documentation/tensorflow/hello_world.py
        
        # MLPug Fashion MNIST example
        # Run `fashion_mnist.py -h` for options
        python mlpug/examples/documentation/pytorch/fashion_mnist.py
        ```
        ### Use MLPug in your own project
        
        ```
        pip install mlpug
        ```
        
        ```Python
        # Using MLPug with PyTorch
        import mlpug.pytorch as mlp
        ```
        
        ```Python
        # Using MLPug with PyTorch/XLA (Training with Pytorch on TPUs)
        import mlpug.pytorch.xla as mlp
        ```
        
        ```Python
        # Using MLPug with Tensorflow
        import mlpug.tensorflow as mlp
        ```
        
        # What is MLPug?
        MLPug is a machine learning library agnostic framework for model training.
        
        A lot of the functionality you need to train your machine learning model is 
        independent of the machine learning library you're using, e.g. PyTorch and Tensorflow.
        For instance, 
        
         * checkpoint management,
         * evaluation of validation set loss and other custom metrics, 
         * progress logging, 
         * progress visualization using Tensorboard, 
         * the use of gradient accumulation to train with large batch sizes using limited GPU memory, etc.. 
        
        You need such functionality no matter what machine learning framework you are using.
        
        MLPug provides a single framework with a unified API for all such training functionality,
        independent of the machine learning library you are using. This also implies that when you switch library
        you can reuse your training code with no, or minimal, changes.
        
        ## Supported deep learning libraries
        Currently, MLPug supports the following deep learning/machine learning libraries:
        
         * PyTorch
         * PyTorch/XLA (Training with Pytorch on TPUs)
         * Tensorflow (in development, some features not available yet)
        
        ## MLPug focus
        Although MLPug should be able to deal with any training job, its functionality is mostly focussed on dealing with  
        training large models on large datasets, using limited hardware (GPU or TPU) resources and memory.
        
        ## Almost at version 0.1!
        MLPug is still in development. If you are having trouble using MLPug for your use case, or 
        when you have found a bug, please file an issue.
        
        ## Contents
        [Installing MLPug](#installing-mlpug) \
        \
        [Hello World](#hello-world) ([PT](#hello-world-with-pytorch) | 
        [XLA](#hello-world-with-pytorchxla) | 
        [TF](#hello-world-with-tensorflow)) 
        
        [Feature parity list](#feature-parity-list)
        \
        \
        \
        The following sections are documentation **ToDo's**, but provide insight in to MLPug's features: \
        [The `logs` object](#the-logs-object) \
        \
        [Callbacks and the training life cycle](#callbacks-and-the-training-life-cycle) \
        \
        [Progress Logging](#progress-logging) \
        \
        [Model components vs Training model](#model-components-vs-training-model) \
        \
        [Distributed training](#distributed-training) \
        \
        [Checkpoint management](#checkpoint-management) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Using the CheckpointManager](#using-the-checkpointmanager) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Using training checkpoints](#using-training-checkpoints) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Using model checkpoints](#using-model-checkpoints) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Checkpointing on error or interrupt](#checkpointing-on-error-or-interrupt) \
        \
        [MLPug metric evaluators](#mlpug-metric-evaluators) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Auxiliary batch training results](#auxiliary-batch-training-results) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Calculating custom metrics](#calculating-custom-metrics) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Conditional computation of metrics](#conditional-computation-of-metrics) \
        \
        [Batch chunking, dealing with GPU memory limits](#batch-chunking-dealing-with-gpu-memory-limits) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Gradient Accumulation](#gradient-accumulation) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Chunked Metric Computation](#chunked-metric-computation) \
        \
        [Using Tensorboard](#using-tensorboard) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Tensorboard made easy with AutoTensorboard](#tensorboard-made-easy-with-auto-tensorboard) \
        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[More fine grained control](#more-fine-grained-control) \
        \
        [Learning Rate Scheduling](#learning-rate-scheduling) \
        \
        [Multi GPU training](#multi-gpu-training) \
        \
        [Mixed Precision Training](#mixed-precision-training) \
        \
        [CUDA Memory tools](#cuda-memory-tools) \
        \
        [Using multiple optimizers](#using-multiple-optimizers)
        
        ## Installing MLPug
        Please ensure that you are using Python3.7+.
        
        Install as follows:
        ```
        pip install mlpug
        ```
        
        ### Usage with PyTorch
        When you want to use MLPug with PyTorch, you will need to install it:
        ```
        pip install torch torchvision
        ```
        
        ### Usage with Tensorflow
        When you want to use MLPug with Tensorflow, you will need to install it:
        ```
        pip install tensorflow
        ```
        
        ## Hello World!
        This is the Hello World of training with MLPug. You will see that the usage of MLPug with Pytorch, 
        Pytorch/XLA and Tensorflow is very similar.
        
        For details please see :
        
         * [pytorch/hello_world.py](mlpug/examples/documentation/pytorch/hello_world.py),
        
         * [pytorch/xla/hello_world.py](mlpug/examples/documentation/pytorch/xla/hello_world.py), 
        
         * [tensorflow/hello_world.py](mlpug/examples/documentation/tensorflow/hello_world.py) and 
        [tensorflow/hello_world_not_eager.py](mlpug/examples/documentation/tensorflow/hello_world_not_eager.py)
           
        You can download and run these examples (for XLA you need to use a TPU on Google Cloud, or use Google Colab).
        
        When reading through the explanation below it might be that you still have a lot of questions about the why and how of
        training with MLPug, however I will expand the MLPug documentation soon, so you will get better insight.
        
        ### 'Hello World' with PyTorch
        To use MLPug with Pytorch
        ```python
        import mlpug.pytorch as mlp
        ```
        
        Before we can start training we need an iterable dataset that can provide our training batches.
        
        ```python
        training_dataset = torch.utils.data.DataLoader(training_data,
                                                       batch_size=batch_size,
                                                       shuffle=False,
                                                       num_workers=3)
        ```
        
        ... and a model we want to train
        ```python
        classifier = torch.nn.Sequential(
            torch.nn.Flatten(),
            torch.nn.Linear(784, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 10))
        ```
        
        MLPug needs a way to evaluate the loss of the model. One way to do that is to define a `TrainModel` that 
        outputs the loss 
        ```python
        class TrainModel(torch.nn.Module):
            def __init__(self, classifier):
                super(TrainModel, self).__init__()
        
                self.classifier = classifier
                self.loss_func = torch.nn.CrossEntropyLoss()
        
            def forward(self, batch_data, evaluate_settings, inference_mode=None):
                images, true_labels = batch_data
        
                logits = self.classifier(images)
                return self.loss_func(logits, true_labels)
        
        train_model = TrainModel(classifier)
        ```
        
        To train the model we will also need an optimizer
        ```python
        optimizer = torch.optim.Adam(classifier.parameters(), eps=1e-7)
        ```
        
        To now use MLPug to start training, we need to create a `Trainer` which will be used by a `TrainingManager`.
        ```python
        trainer = mlp.trainers.DefaultTrainer(optimizers=optimizer, model_components=classifier)
        ```
        
        MLPug uses a callback system allowing you to customize and extend the training functionality. 
        The list of callback instances you provide the `TrainingManager` will be called using hooks at different stages of the 
        training process.
        ```python
        # At minimum you want to log the loss in the training progress
        # By default the batch loss and the moving average of the loss are calculated and logged
        loss_evaluator = mlp.evaluation.MetricEvaluator(trainer=trainer)
        callbacks = [
            mlp.callbacks.TrainingMetricsLogger(metric_evaluator=loss_evaluator),
            # Calculate validation loss only once per epoch over the whole dataset
            mlp.callbacks.TestMetricsLogger(validation_dataset,
                                            'validation',
                                            metric_evaluator=loss_evaluator,
                                            batch_level=False),
            mlp.callbacks.LogProgress(log_period=progress_log_period, set_names=['training', 'validation']),
        ]
        ```
        
        The `TrainingMetricsLogger` and the `TestMetricsLogger` callback instances log training and validation set loss values 
        in a `logs` object that is passed through all callbacks during training. The `LogProgress` callback instance logs the 
        metric values stored in the received `logs` object.
        
        We can now instantiate the `TrainingManager` and pass it the `trainer`. 
        ```python
        manager = mlp.trainers.TrainingManager(trainer,
                                               training_dataset,
                                               num_epochs=num_epochs,
                                               callbacks=callbacks)
        ```
        
        Before we can start training we still have to provide the `train_model` to the trainer.
        ```python
        trainer.set_training_model(train_model)
        ```
        
        The final step is to actually start training:
        ```python
        manager.start_training()
        ```
        
        Running `pytorch/hello_world.py` finishes like this:
        ```text
        ###############################################################################
        Epoch 9/9	READY - Duration 0:00:08
        Moving average:
        training       : loss          0.238.
        
        Computed over dataset:
        validation     : loss          0.346.
        
        
        
        INFO    : TrainingManager::_train : Training completed. All good! ❤️
        
        Using the classifier ...
        real label = 9, predicted label = 9
        ```
        
        ### 'Hello World' with PyTorch/XLA
        
        The Hello World example with PyTorch/XLA, is largely the same as with [PyTorch](#hello-world-with-pytorch). There are only
        two small differences.
        
        To use MLPug with Pytorch/XLA, load the correct backend
        ```python
        import mlpug.pytorch.xla as mlp
        ```
        
        Load your model on a TPU core:
        ```python
        import torch_xla.core.xla_model as xm
        
        ...
        
        device = xm.xla_device()
        
        train_model = TrainModel(classifier, device)
        classifier.to(device)
        ```
        
        ### 'Hello World' with Tensorflow
        Below we will focus only on the minor differences between using MLPug with [PyTorch](#hello-world-with-pytorch) and Tensorflow.
        
        To use MLPug with Tensorflow
        ```python
        import mlpug.tensorflow as mlp
        ```
        
        The only real difference is that, for Tensorflow, you can specify if the trainer needs to run in eager mode or not.
        If not, you need to specify the input `batch_data_signature`.
        ```python
        trainer = mlp.trainers.DefaultTrainer(optimizers=optimizer,
                                              model_components=classifier,
                                              eager_mode=True)
        ```
        
        ```python
        trainer = mlp.trainers.DefaultTrainer(optimizers=optimizer,
                                              model_components=classifier,
                                              batch_data_signature=(tf.TensorSpec(shape=(None, 28, 28), dtype=tf.float64),
                                                                    tf.TensorSpec(shape=(None,), dtype=tf.uint8),))
        ```
        When you run [tensorflow/hello_world.py](mlpug/examples/documentation/tensorflow/hello_world.py) and 
        [tensorflow/hello_world_not_eager.py](mlpug/examples/documentation/tensorflow/hello_world_not_eager.py) you will see
        that when not running in eager mode, training is much faster.
        
        Running `tensorflow/hello_world.py` finishes like this:
        ```text
        ###############################################################################
        Epoch 9/9	READY - Duration 0:00:15
        Moving average:
        training       : loss          0.229.
        
        Computed over dataset:
        validation     : loss          0.370.
        
        
        
        INFO    : TrainingManager::_train : Training completed. All good! ❤️
        
        Using the classifier ...
        real label = 9, predicted label = 9
        ```
        
        Running `tensorflow/hello_world_not_eager.py` finishes like this:
        ```text
        ###############################################################################
        Epoch 9/9	READY - Duration 0:00:06
        Moving average:
        training       : loss          0.229.
        
        Computed over dataset:
        validation     : loss          0.370.
        
        
        
        INFO    : TrainingManager::_train : Training completed. All good! ❤️
        
        Using the classifier ...
        real label = 9, predicted label = 9
        ```
        
        Note the difference in epoch duration!
        
        
        ## Feature parity list
        
        
        |              Feature                          |   PyTorch   | PyTorch/XLA | Tensorflow  |     JAX     |           Comments               |
        |-----------------------------------------------|-------------|-------------|-------------|-------------|----------------------------------|
        | Callbacks and training life cycle             |      ✓      |      ✓      |      ✓      |             | |
        | Progress Logging                              |      ✓      |      ✓      |      ✓      |             | |
        | Distributed training                          |      ✓      |      ✓      |      ✓      |             | Both multi-GPU and multi-TPU support for PyTorch and TF.  TPU training with TF is untested |
        | Model and training checkpoint management      |      ✓      |      ✓      |      ✓      |             | |
        | Custom  metric evaluation                     |      ✓      |      ✓      |      ✓      |             | |
        | Conditional evaluation of metrics             |      ✓      |      ✓      |      ✓      |             | |
        | Batch Chunking: gradient accumulation         |      ✓      |      ✓      |      ❌     |             | TF ToDo |
        | Batch Chunking: chunked evaluation of metrics |      ✓      |      ✓      |      ✓      |             | |
        | Tensorboard support                           |      ✓      |      ✓      |      ✓      |             | Might be refactored |
        | Learning Rate scheduling                      |      ✓      |      ✓      |      ✓      |             | Might be refactored |
        | Mixed Precision Training                      |      ✓      |      ❌     |      ❌     |             | Should work with TF, but no specific support |
        | Using multiple optimizers                     |      ✓      |      ✓      |      ✓      |             | |
        | Multi-task training                           |      ❌     |     ❌      |     ❌      |             | ToDo |
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
