Hello HN! We're Ben & Andreas, and we made Replicate. It's a lightweight open-source tool for tracking and analyzing your machine learning experiments: https:/

Hello HN! We're Ben & Andreas, and we made Replicate. It's a lightweight open-source tool for tracking and analyzing your machine learning experiments: https://replicate.ai/ https://replicate.ai/ Andreas used to do machine learning at Spotify. He built a lot of ML infrastructure there (versioning, training, deployment, etc). I used to be product manager for Docker's open source projects, and created Docker Compose. We built https://www.arxiv-vanity.com/ https://www.arxiv-vanity.com/ together for fun, which led to us teaming up to build more tools for ML. We spent a year talking to lots of people in the ML community and building all sorts of prototypes, but we kept on coming back to a foundational problem: not many people in machine learning use version control. This causes all sorts of problems: people are manually keeping track of things in spreadsheets, model weights are scattered on S3, and results can’t be reproduced. So why isn’t everyone using Git? Git doesn’t work well with machine learning. It can’t store trained machine learning models, it can’t handle key/value metadata, and it’s not designed to record information automatically from a training script. There are some solutions for these things, but they feel like band-aids. We came to the conclusion that we need a native version control system for ML. It’s sufficiently different to normal software that we can’t just put band-aids on Git. We believe the tool should be small, easy to use, and extensible. We found people struggling to migrate to “AI Platforms”. A tool should do one thing well and combine with other tools to produce the system you need. Finally, we also believe it should be open source. There are a number of proprietary solutions, but something so foundational needs to be built by and for the ML community. Replicate is a first cut at something we think is useful: It is a Python library that uploads your files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get back to any point in time using the command-line interface, analyze your results inside a notebook using the Python API, and load your models in production systems. We’d love to hear your feedback, and hear your stories about how you’ve done this before. Also – building a version control system is rather complex, and to make this a reality we need your help. Join us in Discord if you want to be involved in the early design and help build it: https://discord.gg/QmzJApGjyE https://discord.gg/QmzJApGjyE