Adding v2 draft for kubeflow release 1.8 #138
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I think we need to re-add the section about the Kubeflow Notebook images updates from kubeflow/kubeflow#7357, with the key points being:
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chensun
reviewed
Oct 26, 2023
| ## **Selected and Highlighted deliveries** | ||
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| ## Kubeflow Pipelines |
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LGTM on the Kubeflow Pipelines section.
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@DnPlas @kubeflow/release-team Please take a look |
jbottum
reviewed
Oct 30, 2023
Tomcli
reviewed
Oct 30, 2023
Davidnet
commented
Oct 31, 2023
annajung
reviewed
Oct 31, 2023
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| ## Kubeflow v1.8’s powerful Python SDKs simplify Kubernetes-native MLOps, reducing manual yaml configuration | ||
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| Kubeflow v1.8 uniquely delivers Kubernetes-native MLOps via simplified pythonic based workflows, which means less manual yaml, docker and Kubernetes CLI operations. The new workflows simplify ML pipelines building, hyper parameter tuning, distributed model training with GPUs as well as model serving. In addition, many underlying dependencies e.g. Kubernetes, Tensorflow, PyTorch, have been updated, which has improved Kubeflow’s security profile and reduces our enterprise users’ integration work. Kubeflow 1.8 has also added ARM processor support, which addresses a significant portion of the Chinese server market, and also helps adoption by Apple MacBook ARM users. |
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nits
Suggested change
| Kubeflow v1.8 uniquely delivers Kubernetes-native MLOps via simplified pythonic based workflows, which means less manual yaml, docker and Kubernetes CLI operations. The new workflows simplify ML pipelines building, hyper parameter tuning, distributed model training with GPUs as well as model serving. In addition, many underlying dependencies e.g. Kubernetes, Tensorflow, PyTorch, have been updated, which has improved Kubeflow’s security profile and reduces our enterprise users’ integration work. Kubeflow 1.8 has also added ARM processor support, which addresses a significant portion of the Chinese server market, and also helps adoption by Apple MacBook ARM users. | |
| Kubeflow v1.8 uniquely delivers Kubernetes-native MLOps via simplified pythonic based workflows, which means less manual yaml, docker and Kubernetes CLI operations. The new workflows simplify ML pipelines building, hyperparameter tuning, distributed model training with GPUs as well as model serving. In addition, many underlying dependencies e.g. Kubernetes, Tensorflow, and PyTorch, have been updated, which has improved Kubeflow’s security profile and reduced our enterprise users’ integration work. Kubeflow 1.8 has also added ARM processor support, which addresses a significant portion of the Chinese server market, and also helps adoption by Apple MacBook ARM users. |
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| <td><a href="https://knative.dev/docs/reference/relnotes/">Knative</a> |
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nit: instead of having a link to the dependency, would it be better to link the versions like Kubeflow components
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| <td>Tekton | ||
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| <td>Oidc |
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annajung
reviewed
Oct 31, 2023
| author: "Kubeflow 1.7 Release Team, David Cardozo and Josh Bottum" | ||
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| # Kubeflow v1.8 Debuts: Official Support for Pipelines v2, Advanced Security, expanded support in architectures and Storage Exploration |
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Yep, this should be removed:
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| # Kubeflow v1.8 Debuts: Official Support for Pipelines v2, Advanced Security, expanded support in architectures and Storage Exploration |
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| ## Kubeflow v1.8’s powerful Python SDKs simplify Kubernetes-native MLOps, reducing manual yaml configuration | ||
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| Kubeflow v1.8 uniquely delivers Kubernetes-native MLOps via simplified pythonic based workflows, which means less manual yaml, docker and Kubernetes CLI operations. The new workflows simplify ML pipelines building, hyper parameter tuning, distributed model training with GPUs as well as model serving. In addition, many underlying dependencies e.g. Kubernetes, Tensorflow, PyTorch, have been updated, which has improved Kubeflow’s security profile and reduces our enterprise users’ integration work. Kubeflow 1.8 has also added ARM processor support, which addresses a significant portion of the Chinese server market, and also helps adoption by Apple MacBook ARM users. |
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I have rewritten this to be more punchy and descriptive:
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| Kubeflow v1.8 uniquely delivers Kubernetes-native MLOps via simplified pythonic based workflows, which means less manual yaml, docker and Kubernetes CLI operations. The new workflows simplify ML pipelines building, hyper parameter tuning, distributed model training with GPUs as well as model serving. In addition, many underlying dependencies e.g. Kubernetes, Tensorflow, PyTorch, have been updated, which has improved Kubeflow’s security profile and reduces our enterprise users’ integration work. Kubeflow 1.8 has also added ARM processor support, which addresses a significant portion of the Chinese server market, and also helps adoption by Apple MacBook ARM users. | |
| Kubeflow 1.8 delivers leading Kubernetes-native MLOps capabilities. | |
| Kubeflow Pipelines v2 brings simplified and pythonic workflow definitions, meaning less YAML, Docker, and Kubernetes CLI. | |
| Kubeflow Notebooks v1.8 brings significantly updated base images and support for ARM64 processors. | |
| Katib v0.16 brings improved hyperparameter tuning and distributed GPU training capabilities. | |
| Kubeflow 1.8 brings first-class ARM64 support, driving adoption in this growing segment and making Kubeflow easier to use on Apple Silicon devices. | |
| Also, initial support for PPC64 processors has been added. | |
| Many underlying dependencies have been updated, improving Kubeflow’s security posture and making it easier to operate in enterprise environments. |
| ## **Selected and Highlighted deliveries** | ||
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| ## Kubeflow Pipelines |
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This should be H3, not H2:
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| ## Kubeflow Pipelines | |
| ### Kubeflow Pipelines |
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| Kubeflow v1.8 uniquely delivers Kubernetes-native MLOps via simplified pythonic based workflows, which means less manual yaml, docker and Kubernetes CLI operations. The new workflows simplify ML pipelines building, hyper parameter tuning, distributed model training with GPUs as well as model serving. In addition, many underlying dependencies e.g. Kubernetes, Tensorflow, PyTorch, have been updated, which has improved Kubeflow’s security profile and reduces our enterprise users’ integration work. Kubeflow 1.8 has also added ARM processor support, which addresses a significant portion of the Chinese server market, and also helps adoption by Apple MacBook ARM users. | ||
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| In Kubeflow v1.8, ML pipelines are now constructed as modular components, enabling easily chainable and reusable ML workflows. v1.8 also delivers model (training) parallelism for large language models and introduces the PVC Viewer for simplified persistent storage management, eliminating the need for Kubernetes CLI storage commands. The new Katib SDK provides a powerful python based solution that reduces manual configuration and simplifies the delivery of your tuned model. |
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I have improved this paragraph.
Also, as we are already talking about the Kubeflow v2 upgrade in the above paragraph, I have removed it from here.
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| In Kubeflow v1.8, ML pipelines are now constructed as modular components, enabling easily chainable and reusable ML workflows. v1.8 also delivers model (training) parallelism for large language models and introduces the PVC Viewer for simplified persistent storage management, eliminating the need for Kubernetes CLI storage commands. The new Katib SDK provides a powerful python based solution that reduces manual configuration and simplifies the delivery of your tuned model. | |
| Kubeflow 1.8 delivers new capabilities and components. | |
| The new PVC Viewer simplifies interacting with Kubernetes Volumes, allowing you to manage the contents of PVCs without leaving the Kubeflow UI. | |
| The upgraded Katib Python SDK is a powerful solution to reduce manual configuration and simplifies the delivery of your tuned model. |
| Additionally the project [kfp-tekton](https://github.com/kubeflow/kfp-tekton) which allows users to run pipelines with a Tekton backend, is also updated version 2.0.3 and the sdk will compile to the same pipeline spec, sdk users can use the same pipeline definition to run on both Argo and Tekton backends. | ||
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| ## Katib: |
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This should be H3, not H2:
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| ## Katib: | |
| ### Katib |
| * Remove a katib-webhook-cert Secret from components [(#2214](https://github.com/kubeflow/katib/pull/2214)) | ||
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| ## Training Operator: |
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This should be H3, not H2:
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| ## Training Operator: | |
| ### Training Operator |
| * Fully consolidate tfjob-operator to training-operator ([#1850](https://github.com/kubeflow/training-operator/pull/1850)) | ||
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| ## Kserve |
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This should be H3, not H2 (and capitalization is wrong):
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| ## Kserve | |
| ### KServe |
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| ## Updated Images, platform dependencies, breaking changes, and add-ons | ||
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| Kubeflow 1.8 includes hundreds of commits. The Kubeflow release process includes several rounds of testing by the Kubeflow working groups and Kubeflow distributions. Kubeflow’s configuration options provide a high degree of flexibility. After considering all of the testing options, the 1.8 Release Team narrowed the critical dependencies for consistent testing and documentation to the following. | ||
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| 1.8 includes new notebook images with updates to support multi-architectures including ARM and Power processors, and updates to Tensorflow, PyTorch and other packages. |
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This section can be removed, as it says nothing that the above sections don't already say.
Also, the first part is actually a direct copy from a few lines up.
Suggested change
| ## Updated Images, platform dependencies, breaking changes, and add-ons | |
| Kubeflow 1.8 includes hundreds of commits. The Kubeflow release process includes several rounds of testing by the Kubeflow working groups and Kubeflow distributions. Kubeflow’s configuration options provide a high degree of flexibility. After considering all of the testing options, the 1.8 Release Team narrowed the critical dependencies for consistent testing and documentation to the following. | |
| 1.8 includes new notebook images with updates to support multi-architectures including ARM and Power processors, and updates to Tensorflow, PyTorch and other packages. |
5 tasks
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/lgtm |
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@thesuperzapper Hey Mathew - Thanks for all the suggestions to the blog post. They are very helpful. That said, I must call a point of order. We are 24 hours from the release and the suggestions above have many majors changes to content, flow and tone requiring analysis, review and approvals. My request is that in the future, edits at the very end of release cycle should be error corrections or minor incremental modifications. Thanks for understanding. |
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thanks @Davidnet ! /lgtm |
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/lgtm |
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/assign @zijianjoy |
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Thanks @Davidnet! |
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/approve |
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[APPROVALNOTIFIER] This PR is APPROVED Approval requirements bypassed by manually added approval. This pull-request has been approved by: chensun, Davidnet The full list of commands accepted by this bot can be found here. The pull request process is described here
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[WIP] Draft of the kubeflow blog release