From the course: MLOps Tools: MLflow and Hugging Face
Join today to access over 24,700 courses taught by industry experts.
Deploying to Hugging Face Spaces - Hugging Face Tutorial
From the course: MLOps Tools: MLflow and Hugging Face
Deploying to Hugging Face Spaces
- [Instructor] Let's take a look at the lifecycle of how to do a continuous delivery of a Hugging Face Spaces application using GitHub. You can see here that I could grab a token from Hugging Face, right? That's one of the key things to be aware of. Once I take that token, I would put this into the GitHub Codespace environment, as well as the GitHub Actions environment. From here, this allows me to do anything I need to do, like, for example, push a model back into Hugging Face, as well as interact with the Spaces Application. In this case, I could test first the Gradio Application here. Let's say, for example, Text Summarization, and then later, automatically push that change directly to Spaces. So, this is continuous delivery of machine learning applications. Now, let's go ahead and take a look at how that would work in practice. What I would do is I would go to GitHub to the workflow here, and notice, I would say Sync to Hugging Face. Under the main branch, I would run this on…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.
