docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
Important
This learning sample is for educational purposes only and should not be used in any production use case. It is intended to make Semantic Kernel features more accessible for scenarios that do not require an OpenAI or Azure OpenAI endpoint.
This application provides an API service for interacting with models available through Hugging Face. The request bodies and responses are modeled after OpenAI and Azure OpenAI for smooth transition to more capable LLMs.
docker image build -t hf_model_server .
This step will take some minutes to download Docker image dependencies.
docker run -p 5000:5000 -d hf_model_server
docker run -p 5000:5000 -d --volume //d/Repos/Labs/ChatGPT/hugging-face-http-server-main/misc:/app/misc hf_model_server
This will run the service at http://localhost:5000. Navigating to
http://localhost:5000 in a browser window will provide instruction on how
to construct requests to the service.
Important
If the model has not been cached (ex: first time calling it) the response can take some time due to the model being downloaded. Using this service to generate images can also take a very long time - a factor that scales with your hardware.
Alternatively, the service can be started on bare-metal. To do this, you will need to have Python 3.9 installed.
Before proceeding, it is highly recommended that you create a Python 3.9 virtual environment.
Example: python -m venv myvenv or python3 -m venv myvenv.
Make sure your environment is activated:
For Windows, run in PowerShell: ./myvenv/Scripts/Activate.
For Linux/macOS, run: source myvenv/bin/activate.
Then, run pip install -r requirements.txt.
Once all the required dependencies have been installed, you can run the service
using python inference_app.py. Navigating to http://localhost:5000 in a
browser window will provide instruction on how to construct requests to the service.