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PyPI · #2756 most downloaded on PyPI
Kubeflow Pipelines API
Last release 2 months ago
09 Jul 2026
Ships fairly regularly
a new release about every 2 months
Most releases are documented
notes for 44 of 49 stable releases
Nothing withdrawn
no release was ever pulled
7 years old
91 releases · first in 2019
fix(deps): Bump Go versions to address CVE CVE-2025-47907 by @mprahl in #12267
This release of KFP introduces several notable changes that users should consider prior to upgrading. Comprehensive upgrade and documentation notes will follow shortly. In the interim, please note the following key modifications:
For a complete overview of new features and associated changes, please consult the official release notes.
actions/checkout@v5 consistently by @hbelmiro in #12121Note truncated.
One column per quarter.
network and psc_interface_config in custom job spec from GCPC SDKactions/checkout@v5 consistently (#12121)Nothing published for this version
fix(sdk): Set spec.description when compiling to Kubernetes manifests (kubeflow#12132)
Full Changelog: https://github.com/kubeflow/pipelines/compare/2.14.0...2.14.3
feat(docs): Guide to report security vulnerabilities by @andreyvelich in https://github.com/kubeflow/pipelines/pull/12044
This release is a version alignment release. This release succeeds the KFP 2.5.0 release. In this 2.14.0 release, KFP backend is aligned in major and minor versions (X.Y) with all the KFP python packages. Please read more about this and the KFP versioning policy here.
--cache_disabled and update test to cover pipelines with outputs by @hbelmiro in https://github.com/kubeflow/pipelines/pull/12001ESC shortcut for closing SidePanel. Fixes #11873 by @EnyMan in https://github.com/kubeflow/pipelines/pull/11874Full Changelog: https://github.com/kubeflow/pipelines/compare/2.5.0...2.14.0
ESC shortcut for closing SidePanel. Fixes #11873 (#11874) (c3d05eb)--cache_disabled and update test to cover pipelines with outputs (#12001) (f240685)fix(backend): upgrade go version to 1.22.12 to fix CVE-2024-45336 by @dandawg in https://github.com/kubeflow/pipelines/pull/11631
comment body by @lociko in https://github.com/kubeflow/pipelines/pull/11772collect-logs.sh path by @hbelmiro in https://github.com/kubeflow/pipelines/pull/11775kubectl get pods to logs when waiting for pods to get ready by @hbelmiro in https://github.com/kubeflow/pipelines/pull/11779set_container_image works with dynamic images by @sradc in https://github.com/kubeflow/pipelines/pull/11795Full Changelog: https://github.com/kubeflow/pipelines/compare/2.4.1...2.5.0
comment body (#11772) (95c3f2c)replaced deprecated image repos with registry.k8s.io by @HumairAK in https://github.com/kubeflow/pipelines/pull/11152
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here
Install python SDK (python 3.9 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
Full Changelog: https://github.com/kubeflow/pipelines/compare/2.3.0...2.4.0
docs(backend): Remove deprecated v2 compatibility mode docs by @droctothorpe in https://github.com/kubeflow/pipelines/pull/10956
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
autorater_prompt_parameters to online_evaluation_pairwise component by @copybara-service in https://github.com/kubeflow/pipelines/pull/10759kfp-runtime-tests to run on master branch by @hbelmiro in https://github.com/kubeflow/pipelines/pull/11158Full Changelog: https://github.com/kubeflow/pipelines/compare/2.2.0...2.3.0
autorater_prompt_parameters to online_evaluation_pairwise component (cf7450b)To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
preview.llm.rlhf_pipeline in real time by @copybara-service in https://github.com/kubeflow/pipelines/pull/10595preview.llm pipelines by @copybara-service in https://github.com/kubeflow/pipelines/pull/10616preview.llm.rlhf_pipeline runs if no tensorboard_id is provided by @copybara-service in https://github.com/kubeflow/pipelines/pull/10626text and chat variants of bison@001 with the preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10663t5-xxl with the preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10665kfp-kubernetes 1.2.0 by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10692kfp-kubernetes release instructions public by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10693preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10710Full Changelog: https://github.com/kubeflow/pipelines/compare/2.1.0...2.2.0
preview.llm.rlhf_pipeline (22a98d9)preview.llm.rlhf_pipeline in real time (3d8069b)t5-xxl with the preview.llm.rlhf_pipeline (ff7f660)text and chat variants of bison@001 with the preview.llm.rlhf_pipeline (ac39931)preview.llm.rlhf_pipeline runs if no tensorboard_id is provided (ff0d0a7)chore(components): update container image of endpoint batch predict component for vulnerability patch by @copybara-service in https://github.com/kubef…
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
preview.llm pipelines by @copybara-service in https://github.com/kubeflow/pipelines/pull/10295num_microbatches to _implementation.llm training components by @copybara-service in https://github.com/kubeflow/pipelines/pull/10248llama-2-7b for the base reward model when tuning llama-2-13 with the preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10249kfp-pipeline-spec from source in kfp sdk tests by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10300large_model_reference as model_reference_name when uploading models from preview.llm.rlhf_pipeline instead of hardcoding value as text-bison@001 by @copybara-service in https://github.com/kubeflow/pipelines/pull/10321kfp-kubernetes execution tests by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10304preview.llm.rlhf_pipeline run instead of reusing cached result by @copybara-service in https://github.com/kubeflow/pipelines/pull/10322preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10323dsl.OutputPath read logic #localexecution by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10334json_escape placeholder util by @copybara-service in https://github.com/kubeflow/pipelines/pull/10351DockerRunner logs #localexecution by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10354None default parameter #localexecution by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10339kfp-pipeline-spec by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10305kfp and kfp-kubernetes by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10307kfp-kubernetes docs versions and release scripts by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10388kfp-kubernetes docs build error by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10389preview.llm.bulk_inference after tuning third-party models with RLHF by @copybara-service in https://github.com/kubeflow/pipelines/pull/10425text-bison@002 model by default by @copybara-service in https://github.com/kubeflow/pipelines/pull/10428dsl.importer #localexecution by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10431dsl.OneOf with multiple consumers cannot be compiled by @connor-mccarthy in https://github.com/kubeflow/pipelines/pull/10452_implementation.llm components by @copybara-service in https://github.com/kubeflow/pipelines/pull/10474preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10456preview.llm pipelines by @copybara-service in https://github.com/kubeflow/pipelines/pull/10536preview.llm.infer_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10519create_custom_training_job_from_component docs rendering by @copybara-service in https://github.com/kubeflow/pipelines/pull/10541preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10543preview.llm.rlhf_pipeline by @copybara-service in https://github.com/kubeflow/pipelines/pull/10542Full Changelog: https://github.com/kubeflow/pipelines/compare/2.0.5...2.1.0
num_microbatches to _implementation.llm training components (685634d)preview.llm.rlhf_pipeline (3dbf3cf)preview.llm.infer_pipeline (b7ea6e7)preview.llm.rlhf_pipeline (361c16f)preview.llm pipelines (9007fb0)text-bison@002 model by default (83cb88f)dsl.OutputPath read logic #localexecution (#10334) (654bbde)preview.llm.bulk_inference after tuning third-party models with RLHF (b9e08de)preview.llm.rlhf_pipeline run instead of reusing cached result (075d58f)preview.llm.rlhf_pipeline (2e2ba9e)large_model_reference as model_reference_name when uploading models from preview.llm.rlhf_pipeline instead of hardcoding value as text-bison@001 (f51a930)llama-2-7b for the base reward model when tuning llama-2-13 with the preview.llm.rlhf_pipeline (227eab1)dsl.OneOf with multiple consumers cannot be compiled (#10452) (21c5ffe)DockerRunner logs (#10354) (86b7e23)tune-type label when uploading models tuned by preview.llm.rlhf_pipeline (708b8bd)preview.llm.rlhf_pipeline (f67cbfa)preview.llm.infer_pipeline (d8f2c14)create_custom_training_job_from_component (91f50da)preview.llm.rlhf_pipeline components for more readability (c23b720)preview.llm.rlhf_pipeline components for more readability (bcd5922)preview.llm.rlhf_pipeline components for more readability (a927984).after() referencing task in ParallelFor group (#10257) (11f60d8)_implementation.llm.chat_dataset_preprocessor (99fd201)model_checkpoint optional for preview.llm.infer_pipeline (e8fb699)DataflowFlexTemplateJobOp to GA namespace (now v1.dataflow.DataflowFlexTemplateJobOp) (faba922)dsl.OneOf (#10067) (2d3171c)dsl.importer argument is provided by loop variable (#10116) (73d51c8)persistent_resource_id to preview GCPC custom job components/utils (fc1f12b)v1 custom_job and gcp_launcher container code to preview (abf05f4)create_templated_custom_job for Templated Custom Job Launcher (e307545)PipelineTaskFinalStatus in tasks that use .ignore_upstream_failure() (#10010) (e99f270)dsl.Collected (#10069) (fcdff29)To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here or via UI here
Install python SDK (python 3.7 above) by running:
python3 -m pip install kfp kfp-server-api --upgrade
See the Change Log
kfp.client (#7458)dsl.PipelineTaskFinalStatus type (#9082) (7890227)TASK_FINAL_STATUS parameter type (#9080) (c01288d)data-filter-split feature back to the ImageTrainingJOb component which was removed previously (4a4a968)kfp-pipeline-spec package (#8896) (643f421)kfp-kubernetes library (#8950) (0c11cce)kfp-kubernetes (#8982) (c8cb5b5)dsl.ParallelFor (#8631) (b575950)Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-rc.2 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
dsl.PipelineTaskFinalStatus type (#9082) (7890227)StateHistory in task table. Fixes: #9553 (#9554) (0d53de7)gopkg.in/yaml.v3 to fix boolean support. Fixes #9451 (#9473) (4810b7a)Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-rc.1 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
TASK_FINAL_STATUS parameter type (#9080) (c01288d)Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-beta.2 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
client.run_pipeline with pipeline_id and version_id gives error (#9191) (91277b1)display_name and description in @dsl.pipeline decorator (#9153) (91abbea)Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-beta.1 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
data-filter-split feature back to the ImageTrainingJOb component which was removed previously (4a4a968)kfp-pipeline-spec package (#8896) (643f421)kfp-kubernetes library (#8950) (0c11cce)kfp-kubernetes (#8982) (c8cb5b5).dsl attribute on kfp module object (#9048) (0cbcebc)Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-beta.0 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
dsl.ParallelFor (#8631) (b575950)latest label. (#8448) (7138072)kfp dsl compile to compile components (#8371) (4cc0e80)kfp component build only produces empty requirements.txt (#8372) (54922b3)Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-alpha.7 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-alpha.6--upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-alpha.5 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
Upgrade Notes with notices and breaking changes
To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in here.
Install kfp-server-api package (python 3.7 above) by running:
python3 -m pip install kfp-server-api==2.0.0-alpha.4 --upgrade
Refer to:
NOTE, kfp python SDK is NOT included and released separately.
npm run start (#8089) (369e14c)Your coding agent can read these notes before it upgrades. Set up the MCP server →