Achieve Faster and Cheaper Deep Learning with MosaicML Composer

Table of Contents

1. Introduction
2. Limitations of deep learning-Based AI
3. The need for efficient deep learning models
4. Introducing the Composer Python library
5. Composer's goal: Faster and cost-effective training
6. Implemented ideas in efficient deep learning
   • Ghost batch normalization
   • Layer freezing
   • Rand augment
   • Mix-up
   • Squeeze and excite
   • And many others
7. How Composer works with PyTorch and Hugging Face workflows
8. Examples of using Composer in different workflows
   • Functional API
   • Trainer API
   • NLP fine-tuning with Hugging Face models
9. Benefits of using Composer for training neural networks
10. Overview of Composer's algorithms and methods
    • Column Out
    • Blur Pool
    • Progressive Resizing
    • Alib Replace Attention
    • Alib Augmix
    • And more
11. The role of Composer's optimizer and learning rate
12. Insights from the Mosaic ML Chief Scientist
13. Conclusion

Introducing the Composer Python Library

Deep learning-based artificial intelligence has revolutionized various industries, but it comes with limitations. Training algorithms in large artificial neural networks are time-consuming and expensive, hindering the pace of development and restricting important experiments to big labs. To address this challenge, Mosaic ML has launched the Composer Python library. Composer is a collection of implemented ideas in efficient deep learning, aimed at making training faster and cost-effective.

With Composer, You can benefit from various algorithms and techniques that accelerate neural network training and improve generalization. One such algorithm is Ghost batch normalization, which utilizes smaller samples to compute batch normalization, saving computational resources and achieving faster and cheaper training. Composer also implements methods like layer freezing, rand augment, mix-up, squeeze and excite, and many more.

The Composer library integrates seamlessly with popular deep learning frameworks like PyTorch and Hugging Face. Whether you prefer the functional API or the trainer API, Composer provides examples and tutorials to guide you through the process of incorporating it into your workflows. Additionally, Composer allows you to fine-tune NLP models from Hugging Face's Transformers, expanding the capabilities of your models.

By using Composer, you can build efficient neural networks and reduce training costs significantly. In fact, Composer's performance has been demonstrated by training ResNet50 on ImageNet, achieving the standard 76.6% top-one accuracy for only $40 in 1.2 hours, compared to $116 in 3.8 hours using vanilla PyTorch on AWS computing. Similarly, training a GPT-2 model with 125 million parameters to a standard perplexity of 24.11 costs only $145 in 4.5 hours, compared to $255 in 7.8 hours on AWS.

Overall, Composer unleashes the potential of efficient deep learning, making it accessible to developers and researchers. With a wide range of algorithms and methods to choose from, Composer empowers you to train neural networks more effectively and expedite AI development. In the following sections, we will Delve deeper into the specific algorithms and techniques offered by Composer and explore how they can be integrated into different workflows.

Highlights:

• Mosaic ML introduces the Composer Python library for efficient deep learning.
• Composer accelerates neural network training and improves generalization.
• Ghost batch normalization, layer freezing, rand augment, mix-up, squeeze and excite, and more algorithms are implemented in Composer.
• Composer seamlessly integrates with PyTorch and Hugging Face frameworks.
• Composer allows for NLP fine-tuning with Hugging Face models.
• With Composer, training costs and time can be reduced significantly.
• Composer empowers developers and researchers to enhance AI development.