amakelov/mandala
A simple & elegant experiment tracking framework that integrates persistence logic & best practices directly into Python
This project helps machine learning engineers and data scientists efficiently track and manage their experiments. It automatically saves the inputs, outputs, and code of Python function calls, preventing redundant computations. The result is a searchable record of every step of your machine learning workflow, making it easier to iterate and understand your model's development.
538 stars. No commits in the last 6 months.
Use this if you are an ML engineer or data scientist who needs to keep a detailed, queryable record of your model training runs and data transformations without manually logging every detail.
Not ideal if you need a production-ready solution with extensive versioning features, as the API is still in alpha and may have performance bottlenecks with very large datasets.
Stars
538
Forks
16
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jan 14, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/amakelov/mandala"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
treeverse/dvc
🦉 Data Versioning and ML Experiments
runpod/runpod-python
🐍 | Python library for RunPod API and serverless worker SDK.
microsoft/vscode-jupyter
VS Code Jupyter extension
4paradigm/OpenMLDB
OpenMLDB is an open-source machine learning database that provides a feature platform computing...
uber/petastorm
Petastorm library enables single machine or distributed training and evaluation of deep learning...