Skip to main content

pytorch plugin for flyte

Project description

Union PyTorch Plugin

Union can execute PyTorch distributed training jobs natively on a Kubernetes Cluster, which manages the lifecycle of worker pods, rendezvous coordination, spin-up, and tear down. It leverages the open-sourced TorchElastic (torch.distributed.elastic) launcher and the Kubeflow PyTorch Operator, enabling fault-tolerant and elastic training across multiple nodes.

This is like running a transient PyTorch cluster — worker groups are created for the specific job and torn down automatically after completion. Elastic training allows nodes to scale in and out, and failed workers can be restarted without bringing down the entire job.

To install the plugin, run the following command:

pip install --pre flyteplugins-pytorch

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

flyteplugins_pytorch-2.0.0b31-py3-none-any.whl (4.7 kB view details)

Uploaded Python 3

File details

Details for the file flyteplugins_pytorch-2.0.0b31-py3-none-any.whl.

File metadata

File hashes

Hashes for flyteplugins_pytorch-2.0.0b31-py3-none-any.whl
Algorithm Hash digest
SHA256 8ff7e3cc24f7294f5f09c52070f1d485024290d56a8e76c1337e7b7b947e06e6
MD5 1e8f26372d7eaa078e23abd3dbdec7f8
BLAKE2b-256 4525855f7aca50430d7044dae8ab8bd152c2df1cfe281db23c4e4e2412233113

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page