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

This version

2.2.3

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-2.2.3-py3-none-any.whl (8.5 kB view details)

Uploaded Python 3

File details

Details for the file flyteplugins_pytorch-2.2.3-py3-none-any.whl.

File metadata

File hashes

Hashes for flyteplugins_pytorch-2.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 f66cc7f16a54917b666f20bf3659c9d09a76b35b10c4c8fbe9f4fcea74c9524f
MD5 b62a1db18368121c56438043ffe3685b
BLAKE2b-256 a71d032f40e5b7471b3dd60a08085b40b66928cea7b96e4bd0275da51e5d077a

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