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Copy file name to clipboardExpand all lines: tags.yaml
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- dir: batch
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name: Batch
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mission_statement: |
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To enhance collaboration among projects, improve interoperability, and empower users to efficiently leverage batch systems in cloud-native environments.
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In scope:
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To reduce fragmentation in the k8s batch ecosystem: congregate leads and users from different external and internal projects and user groups (CNCF TAGs, k8s sub-projects focused on batch-related features such as topology-aware scheduling) in the batch ecosystem to gather requirements, validate designs and encourage reutilization of core K8s APIs.
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The following recommendations for enhancements:
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* Additions to the batch API group, currently including Job and CronJob resources that benefit batch use cases such as HPC, AI/ML, data analytics and CI.
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* Primitives for job-level queueing, not limited to the k8s Job resource. Long-term, this could include multi-cluster support.
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* Primitives to control and maximize utilization of resources in fixed-size clusters (on-prem) and elastic clusters (cloud).
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* Benchmarking models for Batch systems
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* Data Locality
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* User Stories
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* Scheduling support for specialized hardware (Accelerators, NUMA, Networking, etc.)
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Out of scope:
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* Addition of new API kinds that serve a specialized type of workload. The focus should be on general APIs that specialized controllers can build on top of.
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* Uses of the batch APIs as support for serving workloads (eg. backups, upgrades, migrations). These can be served by existing SIGs.
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* Proposals that duplicate the functionality of core kubernetes components (job-controller, kube-scheduler, cluster-autoscaler).
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* Job workflows or pipelines. Mature third party frameworks serve these use cases with the current kubernetes primitives. But additional primitives to support these frameworks could be in scope.
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Deliverable(s) or exit criteria:
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* Maintaining a landscape document for currently available projects (already published-relocated and maintained)
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* Data Locality project-deliverables TBD, but something that helps in this space (already in process)
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* Benchmarking suite for Batch systems (already in process)
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* User stories published doc for Batch systems (already in process)
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contact:
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slack: C08K71W9HAS # Using parent TAG's contact
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mailing_list: https://lists.cncf.io/g/cncf-tag-workloads-foundation # Using parent TAG's contact
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The cloud-native batch scheduling ecosystem is fragmented — different projects tackle job scheduling, queueing, and resource management in incompatible ways. The Batch subproject brings together maintainers and users across the ecosystem to reduce that fragmentation: aligning on common Kubernetes APIs and primitives, developing best practices, and improving outcomes for batch workloads — whether HPC, AI/ML, data analytics, or CI — in cloud-native environments.
Copy file name to clipboardExpand all lines: tags/tag-workloads-foundation/README.md
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## Subprojects
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### Batch
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To enhance collaboration among projects, improve interoperability, and empower users to efficiently leverage batch systems in cloud-native environments.
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In scope:
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-
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To reduce fragmentation in the k8s batch ecosystem: congregate leads and users from different external and internal projects and user groups (CNCF TAGs, k8s sub-projects focused on batch-related features such as topology-aware scheduling) in the batch ecosystem to gather requirements, validate designs and encourage reutilization of core K8s APIs.
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The following recommendations for enhancements:
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* Additions to the batch API group, currently including Job and CronJob resources that benefit batch use cases such as HPC, AI/ML, data analytics and CI.
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* Primitives for job-level queueing, not limited to the k8s Job resource. Long-term, this could include multi-cluster support.
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* Primitives to control and maximize utilization of resources in fixed-size clusters (on-prem) and elastic clusters (cloud).
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* Benchmarking models for Batch systems
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* Data Locality
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* User Stories
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* Scheduling support for specialized hardware (Accelerators, NUMA, Networking, etc.)
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Out of scope:
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-
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* Addition of new API kinds that serve a specialized type of workload. The focus should be on general APIs that specialized controllers can build on top of.
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* Uses of the batch APIs as support for serving workloads (eg. backups, upgrades, migrations). These can be served by existing SIGs.
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* Proposals that duplicate the functionality of core kubernetes components (job-controller, kube-scheduler, cluster-autoscaler).
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* Job workflows or pipelines. Mature third party frameworks serve these use cases with the current kubernetes primitives. But additional primitives to support these frameworks could be in scope.
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Deliverable(s) or exit criteria:
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* Maintaining a landscape document for currently available projects (already published-relocated and maintained)
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* Data Locality project-deliverables TBD, but something that helps in this space (already in process)
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* Benchmarking suite for Batch systems (already in process)
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* User stories published doc for Batch systems (already in process)
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The cloud-native batch scheduling ecosystem is fragmented — different projects tackle job scheduling, queueing, and resource management in incompatible ways. The Batch subproject brings together maintainers and users across the ecosystem to reduce that fragmentation: aligning on common Kubernetes APIs and primitives, developing best practices, and improving outcomes for batch workloads — whether HPC, AI/ML, data analytics, or CI — in cloud-native environments.
[TAG Workloads Foundation Initiatives](https://github.com/cncf/toc/issues?q=state%3Aopen%20label%3Atag%2Fworkloads-foundation%20label%3Akind%2Finitiative)
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<!-- THIS FILE IS AUTO-GENERATED FROM /tags.yaml -->
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## Mission Statement
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To enhance collaboration among projects, improve interoperability, and empower users to efficiently leverage batch systems in cloud-native environments.
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-
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In scope:
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-
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To reduce fragmentation in the k8s batch ecosystem: congregate leads and users from different external and internal projects and user groups (CNCF TAGs, k8s sub-projects focused on batch-related features such as topology-aware scheduling) in the batch ecosystem to gather requirements, validate designs and encourage reutilization of core K8s APIs.
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The following recommendations for enhancements:
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-
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* Additions to the batch API group, currently including Job and CronJob resources that benefit batch use cases such as HPC, AI/ML, data analytics and CI.
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* Primitives for job-level queueing, not limited to the k8s Job resource. Long-term, this could include multi-cluster support.
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* Primitives to control and maximize utilization of resources in fixed-size clusters (on-prem) and elastic clusters (cloud).
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* Benchmarking models for Batch systems
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* Data Locality
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* User Stories
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* Scheduling support for specialized hardware (Accelerators, NUMA, Networking, etc.)
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Out of scope:
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-
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* Addition of new API kinds that serve a specialized type of workload. The focus should be on general APIs that specialized controllers can build on top of.
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* Uses of the batch APIs as support for serving workloads (eg. backups, upgrades, migrations). These can be served by existing SIGs.
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* Proposals that duplicate the functionality of core kubernetes components (job-controller, kube-scheduler, cluster-autoscaler).
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* Job workflows or pipelines. Mature third party frameworks serve these use cases with the current kubernetes primitives. But additional primitives to support these frameworks could be in scope.
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Deliverable(s) or exit criteria:
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* Maintaining a landscape document for currently available projects (already published-relocated and maintained)
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* Data Locality project-deliverables TBD, but something that helps in this space (already in process)
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* Benchmarking suite for Batch systems (already in process)
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* User stories published doc for Batch systems (already in process)
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The cloud-native batch scheduling ecosystem is fragmented — different projects tackle job scheduling, queueing, and resource management in incompatible ways. The Batch subproject brings together maintainers and users across the ecosystem to reduce that fragmentation: aligning on common Kubernetes APIs and primitives, developing best practices, and improving outcomes for batch workloads — whether HPC, AI/ML, data analytics, or CI — in cloud-native environments.
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[Charter](./charter.md)
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## Leadership
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### Subproject Leads
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- Alex Scammon (**[@](https://github.com/)**)
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- Alex Scammon (**[@stackedsax](https://github.com/stackedsax)**)
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