Vendor Name | Determined AI |
Stand-alone vs. Platform | Stand-alone |
Delivery Model | Open-source with a paid Enterprise edition that contains additional features and commercial support. |
Clouds Supported | On-prem and self-hosted private cloud. |
Pricing Model | GPU-based pricing. |
Service Level Guarantees | Open-source: best effort. |
Support | For Enterprise. |
SSO, ACL | Enterprise: yes. Full integration with SAML/SCIM. |
Security and Compliance | Open-source: basic user management system. |
Setup | Determined CLI can be pip-installed. A Determined cloud or on-premise environment can be provisioned via one "det deploy" command. Arbitrary workloads can be run on Determined, but getting features like distributed training, HPO, and experiment tracking requires making model code adhere to Determined APIs that look similar to PyTorch lightning (but support TensorFlow, Keras, PyTorch). |
Flexibility, Speed, and Accessibility | Determined provides a metadata store that is structured in a relational database which tracks experiment metadata, and exposed to users via a WebUI, Rest API, and Python API. Metadata includes: inputs (code, config, data, hyperparameters), intermediate data (training/validation metrics, logs, checkpoints, optimizer state), and outputs (versioned, trained model weights). |
Model Versioning, Lineage, and Packaging | Supports: |
Log and Display of Metadata | -Experiment information |
Comparing Experiments and Models | Extensive visualizations provided in webUI All of the above can be compared across experiments as well. Also offers full integration with TensorBoard. |
Organizing and Searching Experiments and Models | Experiment organization is built into the tool and a natural consequence of adhering to the Determined APIs. Post-hoc organization via labels is supported, as is search. |
Model Review, Collaboration, and Sharing | Models can be versioned/named and locked, and all experiments/models are tied to a user. |
CI/CD/CT Compatibility | Determined jobs can be kicked off as part of a CI/CD pipeline, and the programmatic REST and Python APIs (as well as the CLI) provide a natural way to pull data into a CI/CD system. |
Integrations | Integrations are provided for: Delta/Spark, DVC, Pachyderm for upstream data processing. Seldon/Algorithmia downstream. |
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Vendor |
Demo link |
Stand-alone vs. Platform |
Delivery Model |
Clouds Supported |
Pricing Model |
Service Level Guarantees |
Support |
SSO, ACL |
Security and Compliance |
Setup |
Flexibility, Speed, and Accessibility |
Model Versioning, Lineage, and Packaging |
Log and Display of Metadata |
Comparing Experiments and Models |
Organizing and Searching Experiments and Models |
Model Review, Collaboration, and Sharing |
CI/CD/CT Compatibility |
Integrations |