Run Hugging Faces Spaces Demo on your own Colab GPU or Locally

1littlecoder
28 Oct 202209:29

TLDRThe video tutorial guides viewers on how to run popular Hugging Face Spaces demos on Google Colab to avoid queues and utilize the provided GPU resources. It explains the process of creating a Colab notebook, checking for GPU availability, cloning the Hugging Face Spaces repository, installing necessary libraries, and launching the demo with modifications for external access. The tutorial emphasizes the efficiency of using Google Colab for a seamless experience without waiting times and encourages users to explore further customizations for their projects.

Takeaways

  • πŸš€ Running popular Hugging Face Spaces demos can involve waiting in a queue due to high demand and shared compute resources.
  • πŸ’‘ By running the demo on your own Google Colab, you can avoid queues and potentially get access to a Tesla T4 GPU for faster processing.
  • πŸ“š Start by creating a new Google Colab notebook and, if required, selecting GPU as your hardware accelerator.
  • πŸ” Verify the availability of GPU by using Nvidia SMI or checking if PyTorch CUDA is available.
  • πŸ“‚ Clone the Hugging Face Spaces repository into your Google Colab notebook using the provided URL and git clone command.
  • πŸ“ Navigate to the specific folder within the cloned repository that corresponds to the demo you want to run.
  • πŸ› οΈ Install the necessary libraries by using the requirements.txt file or by installing the demo-specific libraries like Radio or Streamlit.
  • πŸ”‘ If the demo requires a Hugging Face token, use the notebook login feature from the Hugging Face Hub library.
  • 🌐 To run the demo with an external URL that can be shared, modify the app.py file to include 'share=True' as a parameter in the demo.launch() function.
  • 🚫 Be aware of the nuances and specific instructions mentioned in the demo's documentation to ensure proper setup and execution.
  • πŸ’» After setup, running the app.py file will download the necessary models, which might take considerable time depending on your internet connection.
  • πŸŽ‰ Once completed, you can enjoy using the demo with minimal wait times and potentially share the external URL with others if desired.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about running a popular Hugging Face Spaces demo on Google Colab to avoid waiting in queues and utilize the GPU resource effectively.

  • Why might running the demo on Google Colab be beneficial?

    -Running the demo on Google Colab can be beneficial because it allows users to skip the queue and potentially use a Tesla T4 GPU, which can significantly speed up the process and provide a better experience similar to being first in line.

  • What is the first step in setting up the demo on Google Colab?

    -The first step is to create a new Google Colab notebook and ensure that it has GPU hardware acceleration if the Hugging Face Spaces demo requires a GPU.

  • How can you verify if your Google Colab notebook has a GPU?

    -You can verify if your Google Colab notebook has a GPU by running 'Nvidia SMI' to check the GPU mission or by using 'import torch' and seeing if CUDA is available, which indicates that the GPU is accessible.

  • What is the purpose of cloning the Hugging Face Spaces repository?

    -Cloning the Hugging Face Spaces repository allows you to access the code and files necessary for the demo, which can then be run on your Google Colab notebook.

  • How do you install the required libraries for the demo?

    -You can install the required libraries by using the 'pip install -r requirements.txt' command, and if the demo uses a specific framework like Grad-CAM or Streamlit, you would install it separately using 'pip install' followed by the framework's name.

  • What should you do if the Hugging Face Spaces demo requires a token?

    -If the demo requires a token, you should run 'from huggingface_hub import notebook_login' and then perform 'notebook_login()' to authenticate and use the resources.

  • How can you make the demo accessible with an external URL?

    -To make the demo accessible with an external URL, you need to modify the 'demo.launch' command by adding 'share=True' as a parameter, which will generate a shareable URL for the application.

  • What is the advantage of separating the model download process?

    -Separating the model download process allows you to avoid downloading the model multiple times, which can save time and resources, especially when rerunning the notebook.

  • What is the final outcome after setting up and running the demo on Google Colab?

    -After setting up and running the demo on Google Colab, you should be able to use the demo without waiting in a queue, utilize the GPU resource effectively, and see the results of the diffusion model on the uploaded images.

Outlines

00:00

πŸš€ Running Hugging Face Demos on Google Colab

This paragraph discusses the process of running popular Hugging Face demos on Google Colab to avoid waiting in queues. It emphasizes the benefits of using Google Colab, such as the availability of a T4 GPU and the potential to skip the queue by running the code independently. The speaker introduces a method to clone the Hugging Face Spaces repo and run the demo using a Google Colab notebook, providing a link to a specific tutorial for this purpose. The paragraph also covers the necessary steps to set up a Google Colab notebook with GPU support and the importance of checking for GPU availability using Nvidia SMI or PyTorch.

05:02

πŸ“š Installation and Configuration for Hugging Face Demos

The second paragraph delves into the details of installing and configuring the required libraries for running Hugging Face demos. It explains the importance of having a 'requirements.txt' file and the use of 'pip' to install necessary packages. The paragraph also addresses the potential need for a Hugging Face token and the use of 'notebook login' from the Hugging Face Hub library if the demo requires authentication. Additionally, it provides guidance on modifying the 'app.py' file to enable sharing the application via an external URL, which is essential for sharing the demo with others or using it without manual tunneling handling.

Mindmap

Keywords

πŸ’‘Hugging Face Spaces

Hugging Face Spaces is a platform where users can find, share, and use a variety of machine learning models, particularly in the field of natural language processing. In the context of the video, it is highlighted as a popular place to access diffusion models, which are used for various AI applications. The video discusses how to run a demo from Hugging Face Spaces on Google Colab to avoid waiting in queues and utilize the compute resources more efficiently.

πŸ’‘Google Colab

Google Colab is a cloud-based platform for machine learning and research, which allows users to run Python code in a Jupyter notebook environment with the ability to use free GPU resources. The video emphasizes the benefits of using Google Colab, such as avoiding queues and accessing powerful computing capabilities like Tesla T4 machines, to run popular demos from Hugging Face Spaces.

πŸ’‘GPU (Graphics Processing Unit)

A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the video, the GPU is important because it provides the computational power needed to run complex machine learning models efficiently. Google Colab offers access to GPUs, which can significantly speed up the process of running AI models.

πŸ’‘Diffusion Models

Diffusion models are a class of machine learning models used in the field of generative models, which are designed to generate new data instances that resemble a given dataset. These models have gained popularity for their ability to create high-quality images and other types of media. In the video, the focus is on running a fine-tuned diffusion model from Hugging Face Spaces on Google Colab to demonstrate how to leverage cloud computing resources for AI model experimentation.

πŸ’‘Clone

In the context of version control and software development, to 'clone' refers to the action of creating a complete copy of a repository, allowing a user to have a local version of the codebase. The video instructs viewers on how to clone the Hugging Face Spaces repository containing the diffusion model they wish to run, which is a crucial step in making the demo accessible on Google Colab.

πŸ’‘Requirements.txt

A 'requirements.txt' file is commonly used in Python projects to list the dependencies of a project, detailing the specific versions of libraries needed for the project to run correctly. In the context of the video, the 'requirements.txt' file from the Hugging Face Spaces demo is used to automatically install all the necessary libraries on the Google Colab environment, ensuring that the user has all the tools needed to execute the diffusion model.

πŸ’‘Notebook Login

In the context of the video, 'Notebook Login' refers to the process of authenticating with a Hugging Face token within a Jupyter notebook to access certain resources or models that may require authorization. While the fine-tuned model demo in the video does not require a login, other Hugging Face Spaces demos might necessitate the use of 'notebook login' from the Hugging Face Hub library to function correctly.

πŸ’‘Share Parameter

The 'share' parameter, as discussed in the video, is a command-line argument used when launching applications through Google Colab. Setting 'share' to 'true' allows the application to be accessible via an external URL, enabling the user to share the link with others or use it outside of the local environment. This is particularly useful for sharing AI model demos that would otherwise only be accessible on the user's personal machine.

πŸ’‘Model Download

Model download refers to the process of acquiring the necessary machine learning models and their associated files from a remote server or repository to a local machine or cloud environment. In the video, this step is important as it guides users on how to download the required diffusion models from Hugging Face Spaces to their Google Colab notebook, which is a prerequisite for running the demo.

πŸ’‘Public URL

A public URL, or Uniform Resource Locator, is a reference to a web resource that provides an access point to specific information on the internet. In the context of the video, obtaining a public URL for the Google Colab notebook means that the user can access the running AI model demo from any device with an internet connection, not just on the machine where the notebook is hosted.

πŸ’‘Gradient Application

A gradient application, as used in the video, refers to the user interface or application layer built on top of the machine learning models, which allows users to interact with the models through a graphical interface. In this case, the gradient application is the front-end of the diffusion model running on Google Colab, enabling users to upload images, select styles, and run the model to generate new outputs.

Highlights

Learning how to run popular Hugging Face Spaces demos on Google Colab to avoid queues.

The popularity of certain Hugging Face Spaces demos leading to queues and shared compute resource consumption.

The possibility of using a Tesla T4 GPU on Google Colab for faster processing speeds.

Creating a new Google Colab notebook and selecting GPU hardware acceleration if required.

Verifying GPU availability using Nvidia SMI or by checking if PyTorch CUDA is available.

Cloning the Hugging Face Spaces repository into the Google Colab notebook for local access.

Entering the specific directory of the fine-tuned diffusion model within the cloned repository.

Checking and installing required libraries using `pip install -r requirements.txt`.

Installing additional libraries not mentioned in `requirements.txt`, such as `radio` or `streamlit`.

The necessity of Hugging Face token for certain models and the use of `notebook login` from `huggingface_hub`.

Modifying the `app.py` file to enable sharing of the application with an external URL.

Running the `app.py` script to download models and launch the application on Google Colab.

Accessing the application through the public URL and using it without being on a queue.

The ability to upload an image and apply various filters and styles to it using the diffusion model.

Enhancing the process by separating model downloading from application running to avoid repeated downloads.

The overall goal of leveraging Google Colab to run Hugging Face Spaces demos without waiting in queues.

The tutorial aims to help users make the most of the free GPU resources provided by Google Colab.