(integration) deepspeed_mpi specific container, deepspeed_config for MPI with nodetaints #567
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[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: The full list of commands accepted by this bot can be found here.
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@tenzen-y fyi! |
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Containers now able to carry the DS library and DS applied model application with the patched mpioperator/base.
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tenzen-y
reviewed
Jun 14, 2023
| && sed -i 's/#\(StrictModes \).*/\1no/g' /etc/ssh/sshd_config | ||
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| # Install DeepSpeed library and Torch with cu11.8 wheels | ||
| RUN pip3 install deepspeed |
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Can we pin the deepspeed version?
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| # Install DeepSpeed library and Torch with cu11.8 wheels | ||
| RUN pip3 install deepspeed | ||
| RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 |
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Why do we need to create a separate container image?
ghost
closed this by deleting the head repository
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This MR replication of #549 and introduces an integration example of DeepSpeed, a distributed training library, with Kubeflow to the main mpi-operator examples. The objective of this example is to enhance the efficiency and performance of distributed training jobs by harnessing the combined capabilities of DeepSpeed and MPI. Comments in configuration explains the use of taints and tolerations in the Kubernetes configuration to ensure the proper scheduling of DeepSpeed worker pods on nodes with specific resources, such as GPUs.
By following further discussions at the #549 this PR will be implemented with mpioperator/base sooner.