Version 1.1.2

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here

Install python SDK (python 3.6 above) by running:

python3 -m pip install kfp kfp-server-api --upgrade

See the Change Log

Version 1.1.2-rc.1

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.

Install python SDK (python 3.6 above) by running:

python3 -m pip install kfp kfp-server-api --pre --upgrade

See the Change Log

Version 1.1.1-beta.1

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.

Install python SDK (python 3.6 above) by running:

python3 -m pip install kfp kfp-server-api --pre --upgrade

See the Change Log

Version 1.1.0-alpha.1

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.

Install python SDK (python 3.6 above) by running:

python3 -m pip install kfp kfp-server-api --pre --upgrade

See the Change Log

Version 1.0.4

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here

Install python SDK (python 3.6 above) by running:

python3 -m pip install kfp kfp-server-api --upgrade

See the Change Log

Changelog

1.0.4 (2020-10-22)

Bug Fixes

• backend: Caching - Fixed deployer failure on Kubernetes v1.16+. Fixes #4627 (#4632) (1792103)
• frontend: fix parsing large workflow graph. Fixes #4179 (#4180) (0475368)

Version 1.0.3

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here

Install python SDK (python 3.5 above) by running:

python3 -m pip install kfp kfp-server-api --upgrade

See the Change Log

Version 1.0.3-rc.1

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.

Install python SDK (python 3.5 above) by running:

python3 -m pip install kfp kfp-server-api --pre --upgrade

See the Change Log

Version 1.0.1

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here

Install python SDK (python 3.5 above) by running:

python3 -m pip install kfp kfp-server-api --upgrade

Features

• manifest: add support for Standalone KFP on AWS. Fixes #4337 (#4350) (668a3ec)
• GCP marketplace - preserve install config in configmap and secret (#4471) (d447355)
• deployment: Add env/platform-agnostic-pns standalone deployment option (#4278) (ee11f9e)
• deployment: KFP standalone should keep user data when application deleted (#4332) (6e1ceb7)
• sdk: support HTTP/S PROXY for SDK client (#4215) (9225b75)

Bug Fixes

• backend: add MaxCallRecvMsgSize(math.MaxInt32) to proxy server (#4402) (62bed1b)
• backend: Add a permission to create events to argo-role (#4449) (2b48697)
• backend: Backend - Cache - Fixed reinstallation. Fixes #4299 (#4320) (f5e9901)
• backend: Caching - Only send cache-enabled pods to the caching webhook (#4429) (f2ed52d)
• backend: Caching - Reduced the cache webhook timeout (#4428) (eb86087)
• backend: fix typo in reference key type (#4376) (e5f8209)
• backend: logs error when failing to init mysql. Fixes #4334 (#4335) (64c377e)
• backend: persistence agent - workflow not found error should be a permanent error (#4486) (c31a346)
• backend: prevent seg fault if workflow manifest is deleted. Fixes #4389 (#4439) (9f124d6)
• backend: reduce confusing ReadArtifact errors for metrics in api server. Fixes #3699 (#4338) (44cd005)
• backend: skip reporting native Argo workflows which do not have Run ID label. Fixes #3584 (#4438) (f39a98d)
• sdk/client: reserve the host protocal (http or https) so that http host can work. Fixes #4277 (#4285) (522977f)
• all big enough ui proxy requests fail with error proxying with partial data (#4266) (3a93080)
• cache: adds certificate approver permission to kubeflow-pipelines-cache-deployer-role. Fixes #4138 (#4246) (0ba88b0)

Other Pull Requests

• Reduce ttl of persisted final workflow to 1 day (#4005) (135715d)
• Manifests - Added permissions for certificate approval (#4385) (1445576)

See the Change Log

Version 1.0.0

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here

Install python SDK (python 3.5 above) by running:

python3 -m pip install kfp kfp-server-api --upgrade

See the Change Log

Version 1.0.0-rc.5

To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.

Install python SDK (python 3.5 above) by running:

python3 -m pip install kfp kfp-server-api --pre --upgrade

See the Change Log