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Machine Translation Model Training and Performance Comparison

This repository contains code and results for training and evaluating a machine translation model using different frameworks and setups. The experiments compare the performance of training models using PyTorch and JAX/Flax frameworks, both on single and multiple devices.

Table of Contents

  1. Introduction
  2. Setup
  3. Experiments
  4. Results
  5. Conclusion
  6. Contact

Introduction

This project aims to evaluate the performance of machine translation models trained using different frameworks and hardware configurations. The model used is the MarianMTModel with the Helsinki-NLP/opus-mt-en-hu checkpoint.

Setup

Common Settings

  • Pretrained Model: MarianMTModel
  • Pretrained Tokenizer: MarianMTModel
  • Model Checkpoint: Helsinki-NLP/opus-mt-en-hu
  • Number of Model Parameters: 76,149,760
  • Batch Size per Machine: 64
  • Learning Rate: 1e-5
  • Epochs: 10
  • Tracking Metrics Tool: Wandb

Experiments

Experiment 1: PyTorch on Single GPU

  • Notebook: test1-pytorch-single-machine
  • Device: GPU T4
  • Task: Machine Translation
  • Framework: PyTorch

Results

  • Training Loss: 0.7915
  • Validation Loss: 0.7646
  • Test Loss: 0.7576
  • BLEU Score: 10.14
  • Total Time: 8829.48 seconds

Experiment 2: JAX/Flax on Single GPU

  • Notebook: test2-jax-single-machine
  • Device: GPU T4
  • Task: Machine Translation
  • Framework: JAX/Flax

Results

  • Total Time: 5586.22 seconds
  • Speed-up Compared to PyTorch: 36.73%

Experiment 3: PyTorch on Multiple GPUs

  • Notebook: pytorch-multiple-machine
  • Device: GPU T4 x2
  • Task: Machine Translation
  • Framework: PyTorch

Results

  • Total Time: 5034.03 seconds
  • Speed-up Compared to JAX/Flax Single GPU: 9.88%

Experiment 4: JAX/Flax on TPU

  • Notebook: test3-jax-multiple-machine
  • Device: TPU-v3
  • Task: Machine Translation
  • Framework: JAX/Flax

Results

  • Total Time: 372 seconds
  • Significant Speed-up Compared to All Previous Tests

Results

The results indicate that training with JAX/Flax on a TPU provides the fastest training time, significantly outperforming PyTorch on both single and multiple GPU setups.

Conclusion

This series of experiments demonstrates the advantages of using JAX/Flax on TPU for machine translation tasks, achieving significant speed-ups in training times. The results also highlight the efficiency gains from using multiple GPUs in PyTorch.

Contact

For any inquiries or further information, please contact:

  • Name: Nguyễn Ngô Thành Đạt (Sonny)
  • Email: [email protected]
  • Phone: +84 868 781 774

© 2024 Sonny. All rights reserved.