Merge branch 'master' into sdk-launcher

kubeflow · Feb 5, 2019 · de8e3ca · de8e3ca
2 parents 9c16a08 + 969bb4e
commit de8e3ca
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Showing 3 changed files with 13 additions and 7 deletions.
diff --git a/components/kubeflow/deployer/src/deploy.sh b/components/kubeflow/deployer/src/deploy.sh
@@ -41,6 +41,11 @@ while (($#)); do
        SERVER_NAME="$1"
        shift
        ;;
+     "--pvc-name")
+       shift
+       PVC_NAME="$1"
+       shift
+       ;;
      *)
        echo "Unknown argument: '$1'"
        exit 1
@@ -95,6 +100,14 @@ echo "Generating the TF Serving config..."
 ks generate tf-serving server --name="${SERVER_NAME}"
 ks param set server modelPath "${MODEL_PATH}/export/export"
 
+# support local storage to deploy tf-serving.
+if [ -n "${PVC_NAME}" ];then
+  # TODO: Remove modelStorageType setting after the hard code nfs was removed at
+  # https://github.com/kubeflow/kubeflow/blob/v0.4-branch/kubeflow/tf-serving/tf-serving.libsonnet#L148-L151
+  ks param set server modelStorageType nfs
+  ks param set server nfsPVC "${PVC_NAME}"
+fi
+
 echo "Deploying the TF Serving service..."
 ks apply default -c server
 

diff --git a/test/presubmit-tests-with-pipeline-deployment.sh b/test/presubmit-tests-with-pipeline-deployment.sh
@@ -126,12 +126,6 @@ ${KUBEFLOW_SRC}/scripts/kfctl.sh init ${KFAPP} --platform ${PLATFORM} --project
 
 cd ${KFAPP}
 ${KUBEFLOW_SRC}/scripts/kfctl.sh generate platform
-
-## Add one gpu node for covering gpu sample
-sed -i -e 's|gpu-pool-initialNodeCount:\s*0|gpu-pool-initialNodeCount: 1|g' ./gcp_config/cluster-kubeflow.yaml
-sed -i -e 's|gpu-pool-max-nodes:\s*0|gpu-pool-max-nodes: 1|g' ./gcp_config/cluster-kubeflow.yaml
-sed -i -e 's|gpu-pool-min-nodes:\s*0|gpu-pool-min-nodes: 1|g' ./gcp_config/cluster-kubeflow.yaml
-
 ${KUBEFLOW_SRC}/scripts/kfctl.sh apply platform
 ${KUBEFLOW_SRC}/scripts/kfctl.sh generate k8s
 

diff --git a/test/sample-test/run_test.sh b/test/sample-test/run_test.sh
@@ -152,7 +152,6 @@ if [ "$TEST_NAME" == 'tf-training' ]; then
     sed -i -e "s|gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:\([a-zA-Z0-9_.-]\)\+|${DATAFLOW_PREDICT_IMAGE}|g" kubeflow-training-classification.py
     sed -i -e "s|gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:\([a-zA-Z0-9_.-]\)\+|${LOCAL_CONFUSIONMATRIX_IMAGE}|g" kubeflow-training-classification.py
   fi
-  sed -i -e "s|use_gpu\s*=\s*False|use_gpu = True|g" kubeflow-training-classification.py
 
   dsl-compile --py kubeflow-training-classification.py --output kubeflow-training-classification.tar.gz