--- title: README emoji: 💻 colorFrom: indigo colorTo: gray sdk: static pinned: false ---
Few have done as much as the fast.ai ecosystem to make Deep Learning accessible. Let's make exclusivity in access to Machine Learning, including pre-trained models, a thing of the past, and let's push this unique field even further.
1 month (June 15 to July 15) group to share Vision and Text pre-trained fastai Learners with the community for further community usage and reproducibility.
We believe in openly sharing knowledge and resources to democratize AI for all. At Hugging Face, we encourage all practitioners who train models to contribute by sharing them with the community. Even when trained on particular data sets, sharing Learners will help others save time and computing resources, and give them access to valuable trained artifacts. In turn, you can benefit from the work that others have done. Additionally, shared Learners can be replicated by other community members through, for example, the inference API or repository cloning.
This tutorial shows how to share and load Learners (including those created with blurr) to and from the Hugging Face Hub.
Spaces are a simple way to host ML demo apps directly on your profile or your organization’s profile on Hugging Face. This allows you to create your ML portfolio, showcase your projects at conferences or to stakeholders, and work collaboratively with other people in the ML ecosystem. Learn more about spaces here.
We will be building demos using the new Gradio Blocks API. Blocks allows you to build web-based demos in a flexible way using the Gradio library. Gradio is a popular choice for building demos for machine learning models, as it allows you to create web-based UIs all in Python. For example, here is a UI for Dall-E Mini using Gradio Blocks:
Gradio is a Python library that allows you to quickly build web-based machine learning demos, data science dashboards, or other kinds of web apps, entirely in Python. These web apps can be launched from wherever you use Python (jupyter notebooks, colab notebooks, Python terminal, etc.) and shared with anyone instantly using Gradio's auto-generated share links. To learn more about Gradio see the Getting Started Guide: https://gradio.app/getting_started/ and the new Course on Huggingface about Gradio: Gradio Course.
Gradio can be installed via pip and comes preinstalled in Hugging Face Spaces, the latest version of Gradio can be set in the README in spaces by setting the sdk_version, for example sdk_version: 3.0.2
To install gradio locally, simply run: pip install gradio
gradio.Blocks
is a low-level API that allows you to have full control over the data flows and layout of your application. You can build very complex, multi-step applications using Blocks.
If you have already used gradio.Interface, you know that you can easily create fully-fledged machine learning demos with just a few lines of code. The Interface API is very convenient but in some cases may not be sufficiently flexible for your needs. For example, you might want to:
to learn more about Blocks see the guide https://www.gradio.app/introduction_to_blocks/
Spaces are a simple way to host ML demo apps directly on your profile or your organization’s profile on Hugging Face. This allows you to create your ML portfolio, showcase your projects at conferences or to stakeholders, and work collaboratively with other people in the ML ecosystem. Learn more about spaces here.
Hugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ Gradio spaces currently on Hugging Face. Learn more about spaces here: https://huggingface.co/docs/hub/spaces
Once a model has been picked from the choices above, you can share a model in a Space using Gradio. Read more about how to add Gradio spaces: https://huggingface.co/blog/gradio-spaces
Steps to add Gradio Spaces to the Gradio Blocks Party org
See the Live Blocks Party Leaderboard