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 waiting in queues. It explains the process of creating a Google Colab notebook, selecting GPU hardware, cloning the Hugging Face Spaces repository, installing necessary libraries, and modifying the script for external access. The tutorial emphasizes the efficiency of using Google Colab's free GPU to run diffusion models without queueing and encourages viewers to explore this method for smoother and faster access to AI models.

Takeaways

  • 🚀 **Popular Hugging Face Spaces:** The video discusses a popular demo in Hugging Face Spaces, highlighting the benefits of running it on your own Google Colab to avoid queues and consume the same compute resources as others.
  • 💡 **Google Colab Advantages:** Running the demo on Google Colab can potentially provide a T4 machine or Tesla T4 GPU, which can significantly speed up the process and allow users to skip queues.
  • 📚 **Setting Up Google Colab:** The script provides a step-by-step guide on creating a new Google Colab notebook, selecting GPU hardware acceleration, and checking for GPU availability using Nvidia SMI or PyTorch.
  • 🔄 **Cloning the Hugging Face Spaces Repository:** It explains the process of cloning the Hugging Face Spaces repo into the Google Colab notebook, which involves using the `!git clone` command.
  • 📂 **Navigating Directories:** The video outlines how to navigate into the cloned repository's directory and check its contents using the `%cd` and `%ls` commands in Google Colab.
  • 🛠️ **Installing Required Libraries:** The importance of installing necessary libraries is emphasized, with instructions on using `pip install -r requirements.txt` and separately installing packages like `radio` or `streamlit` if needed.
  • 🔑 **Handling Hugging Face Tokens:** The script addresses the potential need for Hugging Face tokens if the demo requires authentication, with instructions on using the `notebook login` feature from the Hugging Face Hub.
  • 🌐 **Launching the Demo:** The video instructs on modifying the `demo.launch` function to include `share=True` for generating an external URL that can be shared on the internet.
  • 📋 **Editing the `app.py` File:** It provides guidance on copying, modifying, and saving the `app.py` file within the Google Colab notebook to enable the external sharing of the application.
  • 🚫 **Avoiding Model Download重复:** The video suggests a method to avoid repeated model downloads by separating the model download process from the application run, which can be helpful if the same model is used multiple times.
  • 🎉 **Accessing the Application:** Finally, the script demonstrates how to run the application using `%python app.py`, access the local and public URLs, and interact with the application by uploading images and selecting various options.

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 your own Google Colab to avoid waiting in queues and to utilize the GPU resources efficiently.

  • 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 queues 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 for the demo.

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

    -You can check if your Google Colab has a GPU by clicking on 'Runtime', then 'Change runtime type', and selecting 'GPU' under 'Hardware-accelerated'. Alternatively, you can run 'import torch' and check if CUDA is available, which would indicate that a GPU is accessible.

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

    -The first step is to create a new Google Colab notebook by clicking on 'New File' and then 'New Notebook'.

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

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

  • 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 in a Google Colab cell, which will install all the libraries listed in the 'requirements.txt' file.

  • What additional step is needed if the demo requires a Hugging Face token?

    -If the demo requires a Hugging Face token, you need to run 'from huggingface_hub import notebook_login' and then 'notebook_login()' to authenticate and use the token.

  • How can you ensure that the demo runs with an external URL that can be shared?

    -To run the demo 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 instead of a local one.

  • What is a potential enhancement to the process described in the video?

    -A potential enhancement is to separate the model downloading process from the running of the application. This way, the model is downloaded only once and not each time the application is run, saving time and resources.

  • What is the main advantage of running the model on a local machine or Google Colab?

    -The main advantage is that you can avoid waiting in queues and have access to GPU resources, which can significantly speed up the processing time, especially for large models and complex tasks.

  • How does the video help users who may have questions?

    -The video encourages users to leave comments with questions, offering a platform for interaction and clarification, and ensuring that users can get the most out of the tutorial.

Outlines

00:00

🚀 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 highlights the benefits of using Google Colab, such as accessing a T4 GPU and potentially skipping the queue for compute resources. The speaker guides the audience on creating a new Google Colab notebook, selecting GPU hardware acceleration, and checking for GPU availability using Nvidia SMI or PyTorch. The paragraph emphasizes the efficiency of running fine-tuned models on personal computing resources rather than sharing with others in a queue.

05:02

📚 Cloning and Customizing Hugging Face Spaces

The second paragraph delves into the technical steps of cloning Hugging Face Spaces repositories and customizing them for personal use on Google Colab. It covers the process of entering the specific directory of the fine-tuned diffusion model, checking the contents, and installing necessary libraries from the requirements.txt file. The speaker also addresses the potential need for a Hugging Face token if the demo requires one, and provides instructions for using the 'notebook login' feature from the Hugging Face Hub. Additionally, it explains how to modify the app.py file to launch the demo with an external URL for shareability, rather than just a local URL.

Mindmap

Keywords

💡Hugging Face Spaces

Hugging Face Spaces is a platform where developers can share and explore machine learning models, particularly in the field of natural language processing. In the video, it is mentioned as a place where a popular demo with fine-tuned diffusion models can be found, which many people are trying out at the moment.

💡Google Colab

Google Colab is a cloud-based platform for machine learning and programming that runs entirely in the browser, and it allows users to write and execute Python code, and use pre-built models. In the context of the video, Google Colab is recommended as an alternative to running Hugging Face Spaces demos, as it can provide a personal GPU and eliminate the need to wait in a queue.

💡Diffusion Models

Diffusion models are a class of machine learning models used in the field of generative modeling. They are capable of generating new data samples similar to the training data by learning the underlying data distribution. In the video, these models are fine-tuned and are part of a popular demo that the audience can run on their own Google Colab.

💡Queue

In the context of the video, a queue refers to the waiting list that users might experience when trying to access a popular demo on Hugging Face Spaces. Since many people are using the same compute resources, users have to wait their turn, which can be time-consuming. The video suggests using Google Colab to avoid this queue.

💡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, having access to a GPU, like the Tesla T4, is highlighted as a benefit of running the demo on Google Colab, as it allows for faster processing and avoids waiting in a queue.

💡Clone

To clone, in the context of software development, refers to the action of creating a copy of a repository or codebase. In the video, cloning is used to transfer the content of the Hugging Face Spaces demo to the user's Google Colab environment, so they can run it locally.

💡Requirements.txt

A 'requirements.txt' file is a common way to specify the exact versions of Python packages that a project needs in order to run properly. In the video, the script mentions the importance of this file for Hugging Face Spaces demos, as it lists all the necessary libraries that need to be installed to run the demo.

💡Notebook Login

Notebook login refers to the process of authenticating with a platform like Hugging Face to access specific resources or models that might require authorization. In the video, it is mentioned that for some models, users might need to run 'notebook login' from the Hugging Face Hub library to use the model.

💡Share Parameter

The share parameter, as discussed in the video, is a setting that allows the user to share the URL of the running application externally, enabling others to access it over the internet. This is useful for sharing the application with collaborators or for演示 purposes.

💡App.py

App.py is a Python script that is typically the entry point for a web application in a project. In the context of the video, it is the main file that is executed to run the Hugging Face Spaces demo on Google Colab.

💡Model Download

Model download refers to the process of acquiring the necessary machine learning models required for a particular application. In the video, it is mentioned that the first time the app is run, it will download all the necessary models, which can take a considerable amount of time.

Highlights

The tutorial introduces a method to run popular Hugging Face Spaces demos on Google Colab.

Hugging Face Spaces has a variety of diffusion models that are currently very popular.

Running popular demos on Hugging Face Spaces may require waiting in a queue due to high demand.

Google Colab can provide a T4 machine or Tesla T4, allowing users to potentially skip the queue and get faster compute speeds.

The process of running a demo on Google Colab starts with creating a new notebook and selecting GPU hardware acceleration.

To verify the availability of GPU, one can use the command 'Nvidia SMI' or check if 'torch.cuda.is_available()' returns True.

The Hugging Face Spaces repository can be cloned into the Google Colab notebook using '!git clone'.

Required libraries for the demo can be installed using 'pip install -r requirements.txt'.

For certain demos, additional libraries like 'radio' or 'streamlit' may need to be installed separately.

If a Hugging Face token is required for the demo, 'from huggingface_hub import notebook_login' and 'notebook_login()' should be executed.

The 'demo.launch' function can be modified to include 'share=True' for generating an external URL that can be shared on the internet.

Running the 'app.py' script will download the necessary models, which may take a significant amount of time.

Once the models are downloaded, users can interact with the application using a local or public URL.

The tutorial demonstrates the use of the diffusion model by uploading an image and selecting different styles for image generation.

The process described allows users to avoid waiting in queues and utilize the GPU resources efficiently.

It is suggested to separate the model downloading part from the application running part to avoid repeated downloads.

The tutorial aims to help users effectively run Hugging Face Spaces demos on Google Colab and make the most of the available GPU resources.

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