Pluto is an experiment tracking platform. It provides self-hostable superior experimental tracking capabilities and lifecycle management for training ML models. To take an interactive look, try out our demo environment or get an account with us today!
See it in action
https://github.com/user-attachments/assets/6aff6448-00b6-41f2-adf4-4b7aa853ede6
🚀 Getting Started
Install the pluto-ml sdk
pip install -Uq "pluto-ml[full]"
import pluto
pluto.init(project="hello-world")
pluto.log({"e": 2.718})
pluto.finish()
- Self-host your very own Pluto instance using the Pluto Server
You may also learn more about Pluto by checking out our documentation.
Migration
Neptune
Want to move your run data from Neptune to Pluto. Checkout the official docs from the Neptune transition hub here.
Before committing to Pluto, you want to see if there's parity between your Neptune and Pluto views? See our compatibility module documented here. Log to both Neptune and Pluto with a single import statement and no code changes.
🛠️ Development Setup
Want to contribute? Here's the quickest way to get the local toolchain (including the linters used in CI) running:
git clone https://github.com/Trainy-ai/pluto.git
cd pluto
python -m venv .venv && source .venv/bin/activate # or use your preferred environment manager
python -m pip install --upgrade pip
pip install -e ".[full]"
Linting commands (mirrors .github/workflows/lint.yml):
bash format.sh
Run these locally before sending a PR to match the automation that checks on every push and pull request.
Telemetry
The Pluto SDK collects anonymous error and performance telemetry via Sentry to help us improve the library. No user data, model data, or metrics are collected — only SDK-internal errors and diagnostics.
To opt out, set the environment variable before importing pluto:
export PLUTO_DISABLE_TELEMETRY=1
🫡 Vision
Pluto is a platform built for and by ML engineers, supported by our community! We were tired of the current state of the art in ML observability tools, and this tool was born to help mitigate the inefficiencies - specifically, we hope to better inform you about your model performance and training runs; and actually save you, instead of charging you, for your precious compute time!
🌟 Be sure to star our repos if they help you ~
1 maintainer
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