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Productionizing and scaling Python ML workloads simply | Ray

Scale your compute-intensive Python workloads. From reinforcement learning to large-scale model serving, Ray makes the power of distributed compute easy and accessible to every engineer. Source: Productionizing and scaling Python ML workloads simply | Ray Read the original story

Notes on better search 8/18/2023

Goal: better, more focused search for www.cali.org. In general the plan is to scrape the site to a vector database, enable embeddings of the vector db in Llama 2, provide API endpoints to search/find things. Hints and pointers. Llama2-webui –… Continue Reading →

Configuring Jupyter Notebook in Windows Subsystem Linux (WSL2) | by Cristian Saavedra Desmoineaux | Towards Data Science

Here’s a great quick start guide to getting Jupyter Notebook and Lab up and running with the Miniconda environment in WSL2 running Ubuntu. When you’re finished walking through the steps you’ll have a great data science space up and running… Continue Reading →

Customizing GPT-3 for Your Application :: OpenAI

Developers can now fine-tune GPT-3 on their own data, creating a custom version tailored to their application. Customizing makes GPT-3 reliable for a wider variety of use cases and makes running the model cheaper and faster. You can use an existing… Continue Reading →

Scraping the Teknoids Mailman PiperMail Archive

Putting this here in case anyone finds themselves in need of something to scrape a Pipermail web archive of a Mailman mailing list. This bit of Python 3 is based on a a bit of Python 2 I found at… Continue Reading →

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