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Arun Gupta edited this page Jul 19, 2019
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Welcome to the machine-learning-using-k8s wiki!
- Explain
kfapp/aws_config/cluster_config.sh
for GPUs - Explain
kfapp/aws_config/cluster_features.sh
for private access, disable endpoint, control/data plane logging - In
kfapp/env.sh
, explainKUBEFLOW_COMPONENTS
and disabling of ALB and ingress controllers
- Do port forward:
kubectl port-forward svc/centraldashboard -n kubeflow 8080:80
- Access localhost:8080 in a browser to show KubeFlow central dashboard
- Click on Notebooks
- Create new server
- Specify the name
- Change the CPU (for faster processing)
- Spawn server
- Wait for it complete
Connect
- Create a new notebook (top right)
- Python3
- Copy the code from https://github.com/aws-samples/machine-learning-using-k8s/blob/master/samples/mnist/training/tensorflow/mnist.py
- Change
args = parser.parse_args()
toargs = parser.parse_args(args=[])
- Run
- Delete last two lines
- Run
- Show the output
- Do port forward
kubectl port-forward -n kubeflow `kubectl get pods -n kubeflow --selector=app=mnist -o jsonpath='{.items[0].metadata.name}' --field-selector=status.phase=Running` 8500:8500
- Run inference:
python samples/mnist/inference/tensorflow/inference_client.py --endpoint http://localhost:8500/v1/models/mnist:predict
- TensorBoard
- Katib
- Fairing
- KFServing