Deep learning & ML frameworks
Deep learning & ML frameworks covers core libraries to define, train and run models: tensor/autograd frameworks and classical or gradient-boosting ML libraries.
In brief
Deep learning & ML frameworks, in brief.
What it isDeep learning & ML frameworks covers core libraries to define, train and run models: tensor/autograd frameworks and classical or gradient-boosting ML libraries.
Where it sitsOne of 12 categories in the Training and fine-tuning layer. Training and fine-tuning covers the frameworks, libraries and platforms used to train a model from scratch or adapt an existing one to a task, and the tools that track those experiments.
What we track40 products, including Apache MXNet (retired to Attic 2023; legacy), Burn, Candle, CatBoost and Colossal-AI.
The products
40 products in Deep learning & ML frameworks.
Each name opens a search for that product in our index.
ProductMakerIts own site
From our census of 2026-11-08. A product listed here is one that belongs in this category; the search answer says whether we have checked it.
Questions
Deep learning & ML frameworks, answered.
What is Deep learning & ML frameworks in AI infrastructure?Deep learning & ML frameworks covers core libraries to define, train and run models: tensor/autograd frameworks and classical or gradient-boosting ML libraries.
Which layer of AI infrastructure is Deep learning & ML frameworks in?Deep learning & ML frameworks is in the Training and fine-tuning layer. Training and fine-tuning covers the frameworks, libraries and platforms used to train a model from scratch or adapt an existing one to a task, and the tools that track those experiments.
Which products are in Deep learning & ML frameworks?We track 40 products in Deep learning & ML frameworks, including Apache MXNet (retired to Attic 2023; legacy), Burn, Candle, CatBoost, Colossal-AI, Composer, cuML (RAPIDS) and Deep Graph Library (DGL). The full list is on this page.
Training and fine-tuning
The other categories in Training and fine-tuning.
AutoML & hyperparameter optimizationComputer vision toolkits & platformsDistributed training frameworksExperiment trackingFeature storesFederated & privacy-preserving learningFine-tuning platformsML workflow / MLOps platformsOpen-source fine-tuning librariesRL / post-training librariesRL environments & gyms