Ant Group

license

Apache-2.0(OSI

source

public(intelligent-machine-learning/dlrover is the product you run)

core-gated

ungated(a 1,514-entry recursive tree carries no path matching enterprise, ee, commercial or proprietary)

Apache-2.0, read from the LICENSE body rather than the API label. The 1,514-entry recursive tree carries no enterprise, ee or commercial path.

• https://github.com/intelligent-machine-learning/dlrover recorded 2026-09-16

  The dlrover repository page. The repository carries the system itself and names no commercial edition; cited for the license, the public source, and for there being no paid tier declared anywhere the project publishes.

• https://api.github.com/repos/intelligent-machine-learning/dlrover/git/trees/master?recursive=1 recorded 2026-09-16

  Full untruncated recursive tree of the default branch, 1,514 entries. Licenses are the root LICENSE and vendored dependencies. No path matches enterprise, ee, commercial or proprietary. A tree lists paths; it is cited for that and for nothing about the vendor's offerings.

The PyPI download API reports 295 downloads of dlrover in the trailing 30 days, inside the <10K band for level 1. The package is the product's own, published from the same repository, so it is the declared channel. What it counts is pip installs; how many clusters run DLRover is not something this or any other figure here measures.

• https://pypistats.org/api/packages/dlrover/recent recorded 2026-09-16

  Trailing-window download counts for the declared PyPI artifact: 295 downloads in the trailing month.

Admitted on the ML-job-model limb: a training run is a first-class type it schedules, tracks and repairs. Band 3 - DLRover decides when workers start, stop and restart, and the Kubernetes scheduler places the pods it creates. Level with kubeflow-trainer, which does the same thing for the same substrate.

• https://github.com/intelligent-machine-learning/dlrover recorded 2026-09-16

  Repository page and README for dlrover, describing elastic distributed training on Kubernetes with automatic scaling, fault detection and resume.