Supercharge Your Models with Determined AI and Hugging Face

Table of Contents

1. Introduction
2. The Model Hub Platform
3. Interface with Hugging Face Transformers, Tokenizers, and Data Sets APIs
4. Pre-Trained Transformers and Tokenizers
5. Datasets Packages
6. Running Experiments with Determined AI Model Hub
7. Learning Rate Searches
8. Batch Size Searches
9. Different Optimizers
10. Training Language Models with Determined AI
11. Distributed Training with Determined AI
12. Cheaper Preemptable Spot Instances from AWS
13. Overhead Reduction with Determined AI
14. Adaptive Asynchronous Hyper Parameter Search (ASHA)
15. Dynamic Resource Allocation
16. Hugging Face Transformers Library
17. Natural Language Processing and Computer Vision Applications
18. Pre-Trained Models Integration
19. Hugging Face Model Hub
20. Determined Model Hub
21. Glue Benchmark
22. Machine Reading Paraphrase Corpus
23. Semantic Similarity
24. Glue Configuration
25. Swag Benchmark
26. Language Modeling with Determined AI
27. Pushing Pre-Trained Checkpoints to the Model Hub
28. Downloading Pre-Trained Checkpoints

The Model Hub Platform: Interface with Hugging Face Transformers, Tokenizers, and Data Sets APIs

The determined AI platform has introduced a new feature called the Model Hub Platform. This platform allows users to interface with Hugging Face Transformers, Tokenizers, and Data Sets APIs. With this feature, users can use pre-trained transformers, tokenizers, and just the transformer implementations overall, as well as the now implemented datasets packages where they have, as of now, about 1065 different data sets that are in the Hugging Face datasets API. They can easily interface with this determined AI model hub syntax.

Pre-Trained Transformers and Tokenizers

The Hugging Face Transformers library greatly facilitates using these transformers for natural language processing and lately increasingly computer vision applications as well. We can imagine all these pre-trained models are going to be integrated into Hugging Face for all different kinds of data domains. As of now, we have 12,233 models hosted on Hugging Face's model hub. The determined model hub now is going to be letting us interface the determined training platform with these pre-trained models. If You don't have to bother with exactly implementing the correct transformer architecture and say PyTorch or Keras or something like that, and you also want to have these pre-trained weights because it's expensive to pre-train the GBT-2 model on all that language modeling with these big data sets and so on, it helps a lot to have these pre-trained checkpoints, and now with the determined model hub, it's easy to interface these things with the determined platform.

Datasets Packages

Another exciting feature of Hugging Face is that it hosts 1065 data sets. In this example, we're going to be seeing how to access the Hugging Face data set loader within the model hub determined interface so we can use the glue benchmark, the swag benchmark, as well as the wiki text for training a language model. You can look through these 1065 data sets for all sorts of really exciting things for looking for a potential natural language processing research project, and I think they do have some computer vision data sets in here as well.

Running Experiments with Determined AI Model Hub

This video will explain how to use the determined AI model hub to run experiments like learning rate searches or say batch size searches, different optimizers, and then also how to train language models using the determined AI platform, which helps you avoid babysitting your experiments. And then really importantly, for training language models where you have these big models, you can use the distributed training of determined AI, which is easily interfaced on top of the preemptable spot instances from AWS, which is cheaper than other ways of doing it, and determined AI saves you a lot of overhead with trying to do this.

Learning Rate Searches

Here's an example of a learning rate search on the glue benchmark with the machine reading paraphrase corpus. You can see this feature of ASHA, the adaptive asynchronous hyper parameter search, where it has this dynamic resource allocation between different learning rate configurations. We see the most promising configurations are allocated more computation than these other configurations that weren't performing as well. We see how we run through all these different trials and search through these different configurations to find the best learning rate.

Batch Size Searches

Batch size searches are another Type of experiment that can be run with the determined AI model hub. This is where you can search through different batch sizes to find the optimal batch size for your model.

Different Optimizers

Different optimizers can also be searched through with the determined AI model hub. This is where you can search through different optimizers to find the optimal optimizer for your model.

Training Language Models with Determined AI

Training language models with determined AI is a great way to avoid babysitting your experiments. With determined AI, you can train your language models without having to worry about the overhead of managing your experiments.

Distributed Training with Determined AI

Distributed training with determined AI is a great way to train your models faster and more efficiently. With distributed training, you can train your models on multiple GPUs at the same time, which can greatly reduce the time it takes to train your models.

Cheaper Preemptable Spot Instances from AWS

Preemptable spot instances from AWS are a great way to save money when training your models. With preemptable spot instances, you can get access to AWS resources at a much lower cost than with other types of instances.

Overhead Reduction with Determined AI

Determined AI saves you a lot of overhead with trying to train your models. With determined AI, you can train your models without having to worry about managing your experiments or babysitting your models.

Adaptive Asynchronous Hyper Parameter Search (ASHA)

ASHA is a great way to search through different hyper parameters to find the optimal hyper parameters for your model. With ASHA, you can dynamically allocate resources to different hyper parameter configurations to find the most promising configurations.

Dynamic Resource Allocation

Dynamic resource allocation is a great way to allocate resources to different hyper parameter configurations dynamically. With dynamic resource allocation, you can allocate more resources to the most promising configurations and less resources to the less promising configurations.

Hugging Face Transformers Library

The Hugging Face Transformers library is a great way to use pre-trained transformers for natural language processing and computer vision applications. With the Hugging Face Transformers library, you can easily access pre-trained transformers and tokenizers for your models.

Natural Language Processing and Computer Vision Applications

Natural language processing and computer vision applications are two of the most exciting areas of AI research today. With the Hugging Face Transformers library and the determined AI model hub, you can easily train models for these applications.

Pre-Trained Models Integration

Pre-trained models integration is a great way to save time and money when training your models. With pre-trained models integration, you can easily access pre-trained models for your applications.

Hugging Face Model Hub

The Hugging Face model hub is a great way to access pre-trained models for your applications. With the Hugging Face model hub, you can easily access pre-trained models for natural language processing and computer vision applications.

Determined Model Hub

The determined model hub is a great way to interface with the determined AI platform. With the determined model hub, you can easily access pre-trained models and datasets for your applications.

Glue Benchmark

The glue benchmark is a great way to test your models for natural language processing applications. With the glue benchmark, you can test your models for tasks like semantic similarity and machine reading paraphrase corpus.

Machine Reading Paraphrase Corpus

The machine reading paraphrase corpus is a great way to test your models for natural language processing applications. With the machine reading paraphrase corpus, you can test your models for tasks like semantic similarity and machine reading paraphrase corpus.

Semantic Similarity

Semantic similarity is a great way to test your models for natural language processing applications. With semantic similarity, you can test your models for tasks like semantic similarity and machine reading paraphrase corpus.

Glue Configuration

The glue configuration is a great way to configure your models for natural language processing applications. With the glue configuration, you can configure your models for tasks like semantic similarity and machine reading paraphrase corpus.

Swag Benchmark

The swag benchmark is a great way to test your models for natural language processing applications. With the swag benchmark, you can test your models for tasks like semantic similarity and machine reading paraphrase corpus.

Language Modeling with Determined AI

Language modeling with determined AI is a great way to train your models for natural language processing applications. With determined AI, you can train your models without having to worry about managing your experiments or babysitting your models.

Pushing Pre-Trained Checkpoints to the Model Hub

Pushing pre-trained checkpoints to the model hub is a great way to save your models for future use. With the determined AI model hub, you can easily push your pre-trained checkpoints to the model hub for future use.

Downloading Pre-Trained Checkpoints

Downloading pre-trained checkpoints is a great way to access pre-trained models for your applications. With the determined AI model hub, you can easily download pre-trained checkpoints for your applications.

Conclusion

The determined AI model hub is a great way to interface with Hugging Face Transformers, Tokenizers, and Data Sets APIs. With the determined AI model hub, you can easily access pre-trained transformers and tokenizers, as well as datasets packages. You can also run experiments like learning rate searches, batch size searches, and different optimizers. Training language models with determined AI is a great way to avoid babysitting your experiments. With determined AI, you can train your models without having to worry about managing your experiments or babysitting your models. The Hugging Face model hub is a great way to access pre-trained models for your applications. With the Hugging Face model hub, you can easily access pre-trained models for natural language processing and computer vision applications.