b0noI

# You can use any branch but this article been tested with 0.1.2 only
git clone https://github.com/gclouduniverse/gcp-notebook-executor.git --branch v0.1.2 

cd gcp-notebook-executor
source utils.sh

INPUT_NOTEBOOK="./demo.ipynb"
# Should be existing bucket
GCP_BUCKET="gs://b0noi-tmp/test-execution/"
IMAGE_FAMILY_NAME="tf-latest-gpu"

b0noI

export IMAGE_FAMILY="tf-latest-gpu-experimental" # or tf-latest-cpu-experimental
export ZONE="us-west1-b"
export INSTANCE_NAME="my-instance"
export INSTANCE_TYPE="n1-standard-8"
gcloud compute instances create $INSTANCE_NAME \
        --zone=$ZONE \
        --image-family=$IMAGE_FAMILY \
        --image-project=deeplearning-platform-release \
        --maintenance-policy=TERMINATE \
        --accelerator='type=nvidia-tesla-p100,count=1' \

b0noI

export INSTANCE_NAME="my-instance"
gcloud compute instances describe "${INSTANCE_NAME}" --format json | jq '.metadata.items[] | select(.key=="proxy-url") | .value'

b0noI

export IMAGE_FAMILY="tf-latest-gpu" # or put any required
export ZONE="us-west1-b"
export INSTANCE_NAME="my-instance"
export INSTANCE_TYPE="n1-standard-8"
gcloud compute instances create $INSTANCE_NAME \
        --zone=$ZONE \
        --image-family=$IMAGE_FAMILY \
        --image-project=deeplearning-platform-release \
        --maintenance-policy=TERMINATE \
        --accelerator='type=nvidia-tesla-v100,count=8' \

b0noI

export IMAGE_FAMILY="tf-2-0-cu100-experimental" # or tf-2-0-cpu-experimental if no GPU is needed
export ZONE="us-west1-b"
export INSTANCE_NAME="my-instance"
export INSTANCE_TYPE="n1-standard-8"
gcloud compute instances create $INSTANCE_NAME \
        --zone=$ZONE \
        --image-family=$IMAGE_FAMILY \
        --image-project=deeplearning-platform-release \
        --maintenance-policy=TERMINATE \
        --accelerator='type=nvidia-tesla-p100,count=1' \

b0noI

# usage: start_dlvm_for_user tf-latest-cpu [email protected] my-awesome-instance my-project us-west1-b
function start_dlvm_for_user() {
    export IMAGE_FAMILY=$1
    export USER_GOOGLE_ACCOUNT_MAIL=$2
    export INSTANCE_NAME=$3
    export PROJECT_ID=$4
    export ZONE=$5
    export INSTANCE_TYPE="n1-standard-8"
    SERVICE_ACCOUNT_NAME="${INSTANCE_NAME}-sa"
    

b0noI

export IMAGE_FAMILY="tf-latest-cpu" # or put any required
export ZONE="us-west1-b"
export INSTANCE_NAME="my-instance-wtih-limited-access"
export INSTANCE_TYPE="n1-standard-8"
gcloud compute instances create $INSTANCE_NAME \
        --zone=$ZONE \
        --image-family=$IMAGE_FAMILY \
        --image-project=deeplearning-platform-release \
        --machine-type=$INSTANCE_TYPE \
        --boot-disk-size=120GB \

b0noI

PROJECT_ID="" # PUT YOUR PROJECT ID
SERVICE_ACCOUNT_NAME="" # Service account name
GOOGLE_ACCOUNT_MAIL="" # user mail
gcloud iam service-accounts add-iam-policy-binding \
    "${SERVICE_ACCOUNT_NAME}@${PROJECT_ID}.iam.gserviceaccount.com" \
    --member="user:${GOOGLE_ACCOUNT_MAIL}" --role="roles/iam.serviceAccountUser"
    
# Replace osAdminLogin with osLogin if you do NOT want the user to have an
# ability to act as root.
gcloud projects add-iam-policy-binding ${PROJECT_ID} \

b0noI

gcloud iam service-accounts create slava-dlvm-access-account

b0noI

from PIL import Image

import numpy as np

import json
import codecs

img = Image.open("image.jpg").resize((240, 240))
img_array=np.array(img)
result = {