How to Run Accelerate Hugging Face for Faster Inference

To run Accelerate Hugging Face for faster inference, you need to have a good understanding of the underlying architecture. Hugging Face's Accelerate is a powerful tool that can significantly speed up your model's inference time.

First, make sure you have the latest version of the Hugging Face library installed. This will ensure you have access to the latest features and improvements. Accelerate can be used with various frameworks, including PyTorch and TensorFlow.

To get started, you'll need to import the Accelerate library and load your model. This can be done using the `from accelerate import Accelerator` line of code. Once you've loaded your model, you can use the `accelerator` object to accelerate your inference.

To get started with running Accelerate Hugging Face, you'll need to install the necessary packages, including Accelerate and Hugging Face Transformers.

First, make sure you have Python 3.7 or higher installed on your system. This is because Accelerate is compatible with Python 3.7 and later versions.

Explore further: Random Shuffle Dataset Python Huggingface

Next, install the Accelerate package using pip: `pip install accelerate`. This will download and install the Accelerate library and its dependencies.

Then, install the Hugging Face Transformers package using pip: `pip install transformers`. This will download and install the Hugging Face Transformers library and its dependencies.

Finally, verify that Accelerate and Hugging Face Transformers are installed correctly by running the command `accelerate --version` and `transformers --version` in your terminal.

Additional reading: Download Hugging Face Model

Hugging Face models are a popular choice for many developers, and for good reason - they're widely used and well-maintained. You can directly download a pretrained model from the Hugging Face hub and optimize it for use with PyTorch 2.0.

If you remove the to(device="cuda:0") from the model and encoded_input, PyTorch 2.0 will generate C++ kernels that will be optimized for running on your CPU. This allows you to run your model on your CPU without having to manually optimize it.

Explore further: Fine Tune Llama 2 Huggingface

The Hugging Face Hub is an extremely valuable benchmarking tool for PyTorch, ensuring that any optimization work actually helps accelerate models people want to run. The code for downloading and optimizing a Hugging Face model is straightforward and works seamlessly with PyTorch 2.0.

Here are the steps to download and optimize a Hugging Face model:

By following these steps, you can take advantage of the optimized performance of PyTorch 2.0 and get the most out of your Hugging Face model.

Hugging Face models are widely used in the community, and PyTorch 2.0 is designed to work seamlessly with them. You can directly download a pre-trained model from the Hugging Face hub and optimize it.

The Hugging Face Hub is an extremely valuable benchmarking tool for PyTorch, ensuring that any optimization helps accelerate models people want to run. Our goal with PyTorch was to build a breadth-first compiler that speeds up the vast majority of actual models people run in open source.

Broaden your view: How to Use Huggingface Models

If you remove the `to(device="cuda:0")` from the model and encoded_input, PyTorch 2.0 will generate C++ kernels that are optimized for running on your CPU. You can inspect both Triton or C++ kernels for BERT, and they're obviously more complex than the trigonometry example, but you can similarly skim it and understand if you understand PyTorch.

Our community frequently uses pre-trained models from transformers or TIMM, and PyTorch 2.0 aims to work out of the box with the vast majority of models people actually run. The same code also works just fine if used with the Hugging Face Accelerate and DDP.

For another approach, see: Llama 2 Huggingface

Downloading Deployment Files from SparseZoo is a crucial step in deploying Hugging Face models. You'll need to create a model repository on the Models Hub and clone it to your local machine.

To download the deployment directory of the Sparse Transfer 80% VNNI Pruned DistilBERT model, you'll make an API call to Neural Magic's SparseZoo. This model is the result of pruning the DistilBERT model to 80% using the VNNI blocking (semi-structured), followed by fine-tuning and quantization on the SST2 dataset.

Curious to learn more? Check out: How to Use Huggingface Models in Python

The Python script to download the model's weights and configuration files is straightforward. Simply include your download path in the script and run it. After the download is complete, the deployment directory should appear in your environment.

The deployment directory will contain four files: ONNX file, model config file, tokenizer file, and tokenizer config file. Here's what you can expect:

To push your deployment files to the Hub, you'll need to set up a User Access Token from your Settings page. This token is used to authenticate your identity to the Hub.

The User Access Token is required to run the necessary Git commands. You can obtain this token by following the instructions in your Settings page.

Once you have your User Access Token, you can run a specific command in your terminal to authenticate your identity. This command is not specified in the article, but it's a crucial step in the process.

With your token in hand, you can now run the following Git commands to add, commit, and push your files: add, commit, and push. These commands are essential for uploading your deployment files to the Hub.

Here's an interesting read: Open Webui Add Tools from Huggingface

You'll need to pick an AWS instance with two vCPUs and 4GB of RAM in the us-east-1 region to get started.

This is because DeepSparse runs on CPUs with GPU speeds, and this specific configuration is recommended for optimal performance.

To stage your endpoint configuration, follow the instructions in the Hugging Face Endpoints UI Platform.

After your endpoint configuration is staged successfully, you'll see the green Running instance logo and your endpoint URL will be displayed.

You can then use the endpoint URL to make an inference invocation using either the cURL command or a Python script.

To get the cURL command, select Copy as cURL at the bottom of the endpoint page UI, which will give you the necessary command to make a prediction.

Alternatively, you can use the requests library in Python to make an inference invocation, adding the endpoint URL, bearer token, and sample input text to the script.

The expected output in the response will be displayed once you've made the inference invocation.