gcloud auth list gcloud config list project gcloud config set compute/zone us-central1-a gcloud container clusters create awwvision \ –num-nodes 2 \ –scopes cloud-platform
gcloud container clusters get-credentials awwvision
gcloud ai-platform models create flights –regions us-central1 gcloud ai-platform versions create v1 –model flights \ –origin ${MODEL_LOCATION} \ –runtime-version 1.10
kubectl cluster-info kubectl get pods kubectl get deployments -o wide
export PROJECT_ID=$(gcloud info –format=’value(config.project)’) export BUCKET=${PROJECT_ID}
gsutil cp gs://${BUCKET}/flights/chapter9/linear-model.tar.gz ~ MODEL_LOCATION=$(gsutil ls $OUTPUT_DIR/export/exporter | tail -1)
gsutil mv -p gs://gnpqwiklabs_cloudstorageconsole/800px-Ada_Lovelace_portrait.jpg gs://gnpqwiklabs_cloudstorageconsole/ada.jpg gsutil mv -p gs://gnpqwiklabs_cloudstorageconsole/folder1/folder2/800px-Ada_Lovelace_portrait.jpg gs://gnpqwiklabs_cloudstorageconsole/folder1/folder2/
export JOBNAME=dnn_flights_$(date -u +%y%m%d_%H%M%S)
gcloud ml-engine jobs submit training $JOBNAME \ –module-name=trainer.task \ –package-path=$(pwd)/flights/trainer \ –job-dir=$OUTPUT_DIR \ –staging-bucket=gs://$BUCKET \ –region=$REGION \ –scale-tier=STANDARD_1 \ –runtime-version=1.10 \ – \ –output_dir=$OUTPUT_DIR \ –traindata $DATA_DIR/train* –evaldata $DATA_DIR/test*
gcloud ml-engine jobs submit training $JOBNAME \ –module-name=trainer.task \ –package-path=$(pwd)/flights/trainer \ –job-dir=$OUTPUT_DIR \ –staging-bucket=gs://$BUCKET \ –region=$REGION \ –scale-tier=STANDARD_1 \ –runtime-version=1.10 \ – \ –output_dir=$OUTPUT_DIR \ –traindata $DATA_DIR/train* –evaldata $DATA_DIR/test*
WARNING: The gcloud ml-engine commands have been renamed and will soon be removed. Please use gcloud ai-platform instead.
sudo pip install –upgrade google-api-python-client sudo pip install –upgrade oauth2client
gcloud container clusters get-credentials burt-kubeflow –zone us-central1-a –project fermilab-nord-ldrd \ && kubectl port-forward –namespace kubeflow $(kubectl get pod –namespace kubeflow –selector=”service=ambassador” –output jsonpath=’{.items[0].metadata.name}’) 8080:80
export BUCKET_NAME=kubeflow-${PROJECT_ID} gsutil mb gs://${BUCKET_NAME}
gcloud config set core/project $PROJECT_ID gcloud config set compute/zone us-central1-f gsutil mb -c multi_regional -l us gs://gnp-housing-predict datalab create my-datalab –machine-type n1-standard-4
gcloud ai-platform jobs describe housing_190826_224933 gcloud ai-platform jobs stream-logs housing_190826_224933