Deep Learning Training and Evaluation with DeepSpeed and PyTorch

This project demonstrates how to use DeepSpeed and PyTorch for training and evaluating a deep learning model on the FashionMNIST dataset. DeepSpeed is an open-source deep learning training acceleration framework designed to optimize training efficiency, especially for memory management and distributed training.

Project Structure

• ds_config.json: DeepSpeed configuration file that defines hyperparameters for the training process (e.g., learning rate, batch size). This file is essential for configuring DeepSpeed's performance optimization during training.

• ds_train.py: Training script responsible for loading the FashionMNIST dataset, initializing the model, and performing model training. DeepSpeed accelerates the training process and optimizes resource utilization during training.

• ds_eval.py: Evaluation script that loads the trained model and performs inference to evaluate the model's performance on a test set.

• model.py: Contains the model definition. FashionModel is a simple fully connected neural network for classifying the FashionMNIST dataset.

Key Technologies

1. DeepSpeed Training Acceleration

• DeepSpeed is a deep learning training acceleration library that optimizes memory usage and computation efficiency during training, especially for large-scale models.
• In ds_train.py, the model is initialized with DeepSpeed using deepspeed.initialize(), which optimizes the training process, reduces memory usage, and accelerates training.

2. PyTorch Neural Network Construction

• In model.py, a simple neural network model, FashionModel, is built using PyTorch.
• The model architecture includes fully connected layers (Linear), activation functions (ReLU), etc., suitable for classification tasks like FashionMNIST.

3. Distributed Training

• In ds_train.py and ds_eval.py, PyTorch and DeepSpeed are used for distributed training and evaluation.
• Using torch.distributed.get_rank(), the rank of the training node is obtained, and only the main node (rank==0) performs data loading and model inference, preventing redundant operations.

4. Model Evaluation

• ds_eval.py provides the code for model evaluation, including loading the trained model and performing inference. It demonstrates how to evaluate the model both in single-node and distributed mode.

5. Model Saving and Loading

• In ds_eval.py, the model parameters are saved and loaded using torch.save() and torch.load().
• The model is stored in a .pt file format, which can later be used for inference or further training.

Key Learning Points

• Using DeepSpeed: Learned how to optimize the training process with DeepSpeed, especially in distributed training and memory management.
• PyTorch Neural Network Definition and Training: Understood how to define a simple neural network model using PyTorch and train it using a data loader.
• Distributed Training and Inference: Learned how to perform distributed training across multiple nodes and ensure tasks are not duplicated.
• Model Storage and Recovery: Learned how to save a trained model and load it for later use in inference or continued training.

Conclusion

This project demonstrates how to combine DeepSpeed and PyTorch to complete a classification task on the FashionMNIST dataset. By leveraging DeepSpeed, you can efficiently accelerate the training process and reduce memory consumption, making it especially suitable for large-scale model training. This workflow can be easily applied in real-world projects using PyTorch and DeepSpeed for deep learning training, evaluation, and model management.