Anyscale

Introduction

Imports & Loading the Dataset

Defining the Model

Train Loop Per Worker

Defining the Training Loop Configuration

Configuring the Scaling Config

Defining the Model Wrapper

Building the Dataloader

Reporting Metrics and Checkpointing

Persistent Storage

Putting it all together with TorchTrainer

Inspecting the Training Results

Inference with Your Trained Model

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Introduction

Train Loop Using Ray Data

Building a Ray Data-Backed Dataloader

Preparing and Loading the Dataset for Ray Data

Transformations with Ray Data

Configuring TorchTrainer and Launching Training

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Introduction

Checkpoint Loading for Fault Tolerance

Saving Fault Tolerant Checkpoints

Launching Fault Tolerant Training

Manually Restoring from Checkpoints

Cleaning up Cluster Storage and Conclusion

Concluding the Intro Tutorials and Next Steps

Full Chapter Notebook

Introduction

Imports and Dataloading

Building the Dataloader and Preprocessing

Distributing Data

Setting up the Distributed Training Loop

Running Distributed Training

Analyzing the Training Results

Demonstrating Fault Tolerance

Predicting with a Trained Model

Full Chapter Notebook

Introduction

Imports and the Dataset

Distributing Training

Evaluating the Trained Model and Inference

Further Training From a Checkpoint

Full Chapter Notebook

Introduction

Imports and the Dataset

Defining the Model

Distributed Training

Demonstrating Fault Tolerance

Inference and Conclusion

Full Chapter Notebook