Supercharge Your ML Skills with HuggingFace Trainer

Table of Contents

1. Introduction
2. Background of Hugging Face and Determined AI
3. Hugging Face and Determined Integration
   1. Overview of Hugging Face
   2. The Transformer Library
   3. The Model Hub and Data Set Hub
   4. Hugging Face Spaces
   5. The Evaluate Library
   6. Introduction to the Trainer API
4. Demo: Using Hugging Face and Determined Together
   1. Setting up the Environment
   2. Implementing the Callback for Metric Reporting
   3. Connecting the Callback to the Image Classification Code
   4. Running the Code on Determined
   5. Modifying the Configuration for Multiple GPUs
5. Conclusion

Introduction

In this article, we will explore the integration of Hugging Face and Determined AI for powerful machine learning (ML) training solutions. The combination of these two platforms provides a comprehensive environment for building and training ML models at Scale. We will begin by providing an overview of Hugging Face and Determined AI, followed by a detailed explanation of their integration. Then, we will walk through a demo of using Hugging Face and Determined together, focusing on metric reporting. By the end of this article, You will have a clear understanding of how to leverage the capabilities of Hugging Face and Determined AI for your ML projects.

Background of Hugging Face and Determined AI

Hugging Face

Hugging Face is an AI company that aims to democratize AI through open-source collaboration. They offer a wide range of tools and resources for natural language processing (NLP) and computer vision applications. Hugging Face is best known for their Transformers library, which provides access to state-of-the-art pre-trained NLP models. They also have a Model Hub and a Data Set Hub, where users can find and contribute to a vast collection of models and data sets. Additionally, Hugging Face offers the Trainer API, which simplifies the training process and allows for easier experimentation with different models and architectures.

Determined AI

Determined AI is a complete machine learning platform that provides all the necessary tools for training ML models at scale. The platform offers features such as distributed training, hyperparameter search, experiment tracking, and metric visualization. Determined AI simplifies the process of training complex models by providing a unified interface and easy integration with popular ML libraries and frameworks. With Determined AI, users can easily train models using multiple GPUs or distributed systems, greatly improving the efficiency and speed of the training process.

Hugging Face and Determined Integration

Overview of Hugging Face

Hugging Face is a comprehensive AI platform that offers a wide range of tools and resources for building and training ML models. The platform's flagship offering is the Transformers library, which provides access to state-of-the-art NLP models. The library allows users to easily load pre-trained models and perform inference tasks using the Hugging Face Pipelines feature. The Transformers library is known for its simplicity and ease of use, making it a popular choice among ML practitioners.

The Transformer Library

The Transformer library from Hugging Face is the go-to solution for all applied NLP needs. It consists of a collection of state-of-the-art NLP models that are pre-trained and ready to use. The library offers a wide variety of models, including those for text classification, named entity recognition, question answering, and language translation. With the Transformer library, users can easily perform inference tasks using just a single line of code. The library provides an extensive collection of models, making it a valuable resource for any NLP project.

The Model Hub and Data Set Hub

Hugging Face provides a Model Hub and a Data Set Hub, which are online repositories that host a vast collection of models and data sets contributed by the community. The Model Hub contains over 120,000 models, including both official Hugging Face models and models contributed by the community. Users can easily access and download these models for use in their own projects. Similarly, the Data Set Hub allows users to find and download a wide variety of data sets for training and evaluation purposes. These hubs are a testament to the vibrant and collaborative community that Hugging Face has built.

Hugging Face Spaces

Hugging Face Spaces is a platform where machine learning engineers can showcase and share their projects. It allows users to upload and host interactive demos of their machine learning solutions. This feature is particularly useful for sharing and exploring innovative ML projects. By visiting Hugging Face Spaces, users can discover and learn from the work of other machine learning practitioners, further expanding their knowledge and expertise in the field.

The Evaluate Library

Hugging Face also offers the Evaluate library, a collection of flexible evaluation functions. These functions can be used with Hugging Face models as well as community-contributed models. The Evaluate library provides a convenient way to evaluate the performance of models, compare different models, and validate their effectiveness. With this library, users can easily assess the quality and accuracy of their models, helping them make informed decisions in their ML projects.

Introduction to the Trainer API

The Trainer API is a high-level API provided by Hugging Face that simplifies the training process. It is built on top of the lower-level APIs commonly used in machine learning frameworks like PyTorch. The Trainer API abstracts away the complexities of training a model and provides a consistent interface for training different models. It allows users to define their trainers and handle the training loop with minimal code. The Trainer API is widely used in Hugging Face's training examples and is recommended for users who are new to NLP or want a simpler ML training interface.

Demo: Using Hugging Face and Determined Together

In this demo, we will walk through the process of using Hugging Face and Determined AI together to train an image classification model. We will focus on the implementation of a callback for metric reporting, which will enable us to track the training progress and evaluate the model's performance. This demo assumes basic familiarity with Hugging Face and Determined AI.

Setting up the Environment

To begin, we need to set up the environment for our training code. We can do this by importing the required libraries and defining the necessary input parameters. The input parameters include the training arguments, model arguments, and data training arguments. These parameters will be used to configure our training process and load the required models and data sets. It is recommended to use the Hugging Face examples repository as a reference for setting up the environment.

Implementing the Callback for Metric Reporting

Next, we will implement a callback for metric reporting, which will allow us to track the training metrics and evaluate the model's performance. We can achieve this by defining the on_log method in our callback class. In this method, we will use the provided TrainingArguments and TrainerState objects to access the necessary information for reporting metrics. We will also use the provided TrainerControl object to control the training process, such as saving checkpoints and stopping training. Additionally, we will use the CoreContext object to ensure that metric reporting is only done by the chief worker or rank 0 in a distributed system.

Connecting the Callback to the Image Classification Code

Once the callback is implemented, we need to connect it to our image classification code. We can do this by initializing the DistributedContext object, creating an instance of our callback class, and adding the callback to the Trainer's callback list. This will ensure that our callback is called during the training process and that the metrics are reported correctly.

Running the Code on Determined

Finally, we can run our code on the Determined AI platform. We can do this by creating an experiment using the Determined API and passing our code and configuration files. The API will handle the execution of our code on the allocated resources, such as GPUs. We can monitor the progress of the experiment and access the reported metrics through the Determined UI.

Modifying the Configuration for Multiple GPUs

If we want to leverage multiple GPUs for our training, we can easily modify the configuration file. By adding the "resources: slots_per_trial" parameter and specifying the desired number of GPUs, we can allocate more resources for our experiment. This will enable us to take AdVantage of the Parallel processing power of multiple GPUs and accelerate the training process.

Conclusion

In this article, we have explored the integration of Hugging Face and Determined AI for ML training. We have discussed the background of both platforms and their key features. We have also walked through a demo of using Hugging Face and Determined together, focusing on metric reporting. By leveraging the capabilities of Hugging Face and Determined AI, users can build and train powerful ML models at scale. The combination of these platforms provides a comprehensive environment for developing, training, and deploying ML solutions.