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-2.0.0b59-py3-none-any.whl (4.7 kB view details)

Uploaded Python 3

File details

Details for the file flyteplugins_pytorch-2.0.0b59-py3-none-any.whl.

File metadata

File hashes

Hashes for flyteplugins_pytorch-2.0.0b59-py3-none-any.whl
Algorithm Hash digest
SHA256 bcb90a3d49fc644c93e2b6a63c1b78f87dc5385335c9eb2425069f9a972afcfc
MD5 2052fc11e11a4a10f9a012cb1735a552
BLAKE2b-256 69c5b526d7461a2edc7af5377e1b30bcd6aba4ef0be0722ab588cda0b03f30f2

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