Running Hugging Face LayoutLM Model with PyCharm and Docker

This tutorial explains how to run Hugging Face LayoutLM model locally with PyCharm remote interpreter. This is cool, because a remote interpreter allows you to run and debug your custom logic, while running Hugging Face model and its dependencies in Docker container. I share Dockerfile, which helps to setup all dependencies. Enjoy!

 

Table Query with Hugging Face ML

Yes, you can do a search through a table data with Hugging Face model called TAPAS. I show how it works with sample CSV and example queries. The app runs on Hugging Face Spaces and you can play and upload your own CSV files for a test. Give it a try, maybe ML can replace SQL?

 

Hugging Face Gradio Python UI and CSV Processing

Explaining how to process CSV file uploaded through Gradio UI in Python. Gradio is part of Hugging Face. You will also learn how to define inputs and outputs for Gradio, to render UI components out of the box. Towards the end of the video, I will share a tip on how to read an error message, if the error happens during app development.

 

Hugging Face Gradio Python UI for ML

I dive into Gradio UI with Python, which is now part of Hugging Face. This is a very cool and simple to use library, it helps to build UI for ML models quickly. This is useful to share ML models with the community and run quick demos to showcase your ML model capabilities. I explain app code structure and how you can map ML model inputs and outputs with Gradio.

 

TensorFlow.js Node on Docker and Kubernetes

I explain how to dockerize TensorFlow.js Node app and also run it on Kubernetes. This work was done as part of our open-source MLOps solution Skipper.

 

TensorFlow.js Microservice

Do you know you could run TensorFlow.js on backend with Node.js? I explain how to build microservice with TensorFlow.js based on sample MobileNet model for image classification. I'm using Skipper Lib JS lib to communicate to RabbitMQ and receive/deliver events. You will learn about the code structure, which can be used as a blueprint for your own JS microservice implementations.

 

Building Bigger Applications with FastAPI

I share a few tips and tricks on how to build a clean and readable REST API for larger apps with FastAPI framework. You will learn how to split endpoints into separate Python scripts and assign tags with prefixes for all endpoints from the given router at once.