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- Once the ``kubectl port-forward deployment/metaflow-metadata-service 8080:8080`` to port forward metatdata service for accesss on localmachine. Please note that because this is directly port forwarding to the pod were are taking the 8080 port for the service.
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@@ -25,19 +25,18 @@ kubectl exec -it tf-gpu -- \
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## Specs
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- Kops using the [gpu_setup/gpu_instance.yml](gpu_setup/gpu_instance.yml) file to Configure the GPU Instances on AWS joininig the Cluster.
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- Constraints :
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- Cuda Libraries v9.1
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- Docker 18.x on Machine
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- Kubernetes Version 1.15.x, 1.16.x
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- NO CUDA 10.2 Support :
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- KOPS Currently Only Support Kubernetes v1.16
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- K8s v1.16 which uses Docker v18.03.
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- K8s v1.17 Support Docker 19.03.
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-[NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker#quickstart) Requires Docker 19.03 and supports CUDA 10.2.
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- The Older version of this was [nvidia-docker2](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)) which supported Docker 18.03 and 19.03
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- KOPS Supports NVIDIA-Device-Plugin deployments with [nvidia-docker2](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)) and hence currently has ongoing issues on support for New CUDA Versions.
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- KOPS Needs to move to v1.17 of kubernetes to start quick deployments Kubernetes versions which can support Docker 19.03 which inturn will support Latest Nvidia CUDA Toolkit.
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- Tested ON :
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- Kubernetes Version 1.15.x, 1.16.x
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- Cuda Libraries v10.2. [Credits](https://github.com/elevate/nvidia-device-plugin)
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- To use Cuda 9.1 change the below in [gpu_setup/gpu_instance.yml](gpu_setup/gpu_instance.yml)
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```yml
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hooks:
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- execContainer:
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image: dcwangmit01/nvidia-device-plugin:0.1.0
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```
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## Cleanup Tasks
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## TODO
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-[] Test the Base AMI for KOPS deployment with NVIDIA Provided AMI.
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