[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?

• spock

  12 114 7.0 Python

  spock is a framework that helps manage complex parameter configurations during research and development of Python applications (by fidelity)

  

We wrote Spock which actually sits in the middle ground between Hydra and OmegaConf (I’m of the same opinion that Hydra does a little too much feature wise). You can do hierarchical composition within the markdown of any JSON, YAML, or TOML files by simply using the config argument. No code needed to merge. Docs are here if you’re interested.

• dvc

  109 13,116 9.7 Python

  🦉 ML Experiments and Data Management with Git

  

Use guild and pytorch-lightning. Make it easy for new contributors to get your data by using dvc as a data access tool.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• guildai

  16 856 8.8 Python

  Experiment tracking, ML developer tools

  

Use guild and pytorch-lightning. Make it easy for new contributors to get your data by using dvc as a data access tool.

• tune-sklearn

  4 462 0.0 Python

  Discontinued A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

  

If you mainly use scikit-learn, you should consider using tune-sklearn.

• labml

  23 1,863 9.8 Jupyter Notebook

  🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

  

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

Suggest a related project