PyTorch Lightning helps pretrain and fine-tune AI models across one or many GPUs with minimal code changes.
Open source
24 alternativesDetermined
Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.
About Determined
Determined is an open-source machine-learning platform for deep learning workflows, compatible with PyTorch and TensorFlow. It covers distributed training, hyperparameter tuning, resource management and experiment tracking, configured through YAML files. The platform is made up of a Python library, a command-line interface and a Web UI.
The Python library adapts existing PyTorch or TensorFlow code through class-based APIs such as PyTorchTrial or through a Core API for using only selected functions. The CLI installs with pip install determined and can start a cluster locally or deploy to AWS, GCP, Kubernetes, or Slurm/PBS; experiments are then submitted from the command line. The Web UI shows loss curves, hyperparameter plots, code and configuration snapshots, model registries, cluster utilization, debugging logs and performance profiling reports. The project is licensed under Apache V2 and the source does not list pricing.
Key features
- Distributed training configured per trial via YAML resource settings
- Hyperparameter tuning with adaptive ASHA searcher and numeric ranges
- Experiment tracking for loss curves, config snapshots and model registries
- Web UI for cluster utilization, debugging logs and profiling reports
- CLI deployment to local, AWS, GCP, Kubernetes, or Slurm/PBS
- Python APIs: PyTorchTrial, Keras, DeepSpeed and a Core API
Best for: Machine-learning teams running PyTorch or TensorFlow training who need distributed training, hyperparameter tuning and cluster resource management on their own infrastructure.
Good for
- Open source under the Apache V2 license
- Documented deployment paths for on-prem, AWS, GCP, Kubernetes and Slurm/PBS
- Existing PyTorch or TensorFlow code can be adapted through class-based or Core APIs
Watch out for
- Training runs through a Determined cluster, which must first be deployed and managed
- Documented deployment targets are limited to the local, AWS, GCP, Kubernetes and Slurm/PBS environments listed
Determined at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Determined alternatives
Tools and sites you could use instead, ranked by how closely they match.
doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.
Open source
24 alternatives
AutoGluon provides tools for automated machine learning and model development.
Open source
24 alternatives
EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.
Open source
24 alternatives
This metric-learn repository contains Python implementations of metric learning algorithms.
Open source
24 alternatives
metric-learn provides Python implementations of metric learning algorithms.
Open source
24 alternativesPopular in ML Training & Optimization
Well-known listings from the same topic.
Edwardedwardlib.orgAerosolveaerosolve.airbnb.techDataturedatature.ioTogether AItogether.aiOptunaoptuna.orgCatBoostcatboost.ai
Determined FAQ
How is the Determined CLI installed?
Install it with pip install determined, then use det deploy to start a cluster locally or on supported cloud services.
Which deep learning frameworks does Determined support?
It is compatible with PyTorch and TensorFlow, and the documentation includes Core, PyTorch, Keras and DeepSpeed APIs.
Where can a Determined cluster be deployed?
Deployment guides cover local on-premises, AWS, GCP, Kubernetes and Slurm/PBS environments.
Is something wrong or out of date?
Tell us and an editor will check it.
Own this website?
Claim this listing
Claim the listing and suggest updates yourself. An editor checks every claim first.