Image classification task with Tensorflow at scale on Google cloud platform along with model deployment and versioning

Step 1 Create a VM instance(lets say Ubuntu 16.04 LTS) on the cloud(a small machine with 1 core and 3.75 GB ram would work)

Step 2 SSH into the machine from the browser

Step3

Install docker on the machine(in our case Ubuntu 16.04 LTS) You can have a look at the commands(https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-on-ubuntu-16-04)

1. curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

2. sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

3. sudo apt-get update

4. apt-cache policy docker-ce

5. sudo apt-get install -y docker-ce

6. sudo systemctl status docker

Step 4 Clone the repository

git clone https://github.com/DataScienceDelhi/dsdevent1.git

Step 5 Start a container with tensorflow tag 1.2.0

sudo docker run -it -v `pwd`:/notebooks tensorflow/tensorflow:1.2.0

After you get a running jupyter and a token screen close this window directly

Step 6 List containers

sudo docker ps

sudo docker exec -it /bin/bash

This command will take you inside container with root permissions where we will maintain the environment and so that you can package it and take it to any other machine(be it your local machine)

Step 7

apt-get update

apt-get install vim

Step 8

cd dsdevent1

Step 9 Install Gcloud sdk

export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)"

echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list

curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -

apt-get update

apt-get install google-cloud-sdk

gcloud init

Step 10

Also, we use Apache Beam (running on Cloud Dataflow) and PIL to preprocess the images into embeddings, so make sure to install the required packages:

pip install -r requirements.txt

Step 11 Create a bucket in your GCS as per the below instructions Upload dict.txt, train_set.csv, eval_set.csv and preprocessed files to directly jump to Cloud ML Training step declare USER='puneet' so bucket name should be flower_${USER}

gsutil cp flower1/dict.txt gs://flower_${USER}/
gsutil cp flower1/train_set.csv gs://flower_${USER}/
gsutil cp flower1/eval_set.csv gs://flower_${USER}/

Step 12

declare PROJECT=$(gcloud config list project --format "value(core.project)")

declare JOB_ID="flowers_puneet_$(date +%Y%m%d_%H%M%S)"

declare BUCKET="gs://flower_${USER}"

declare DICT_FILE="${BUCKET}/dict.txt"

declare MODEL_NAME=flower

declare VERSION_NAME=v1

Step 13

python trainer/preprocess.py --input_dict "$DICT_FILE" --input_path "${BUCKET}/train_set.csv" --output_path "${BUCKET}/preproc/train" --cloud

Step 14

python trainer/preprocess.py --input_dict "$DICT_FILE" --input_path "${BUCKET}/eval_set.csv" --output_path "${BUCKET}/preproc/eval" --cloud

Step 15 Upload preprocessed data to skip above steps gsutil cp -r flower1/* gs://flower_${USER}/

Step 16

# Training on CloudML
gcloud ml-engine jobs submit training "$JOB_ID" --stream-logs --module-name trainer.task --package-path trainer --staging-bucket "${BUCKET}" --region us-central1 --runtime-version=1.2 -- --output_path "${BUCKET}/training" --eval_data_paths "${BUCKET}/preproc/eval*" --train_data_paths "${BUCKET}/preproc/train*"

Step 17

gcloud ml-engine models create "$MODEL_NAME" --regions us-central1

gcloud ml-engine versions create "$VERSION_NAME" --model "$MODEL_NAME" --origin "${BUCKET}/training/model" --runtime-version=1.2

gcloud ml-engine versions set-default "$VERSION_NAME" --model "$MODEL_NAME"

gsutil cp gs://cloud-ml-data/img/flower_photos/daisy/100080576_f52e8ee070_n.jpg daisy.jpg

python -c 'import base64, sys, json; img = base64.b64encode(open(sys.argv[1], "rb").read()); print json.dumps({"key":"0", "image_bytes": {"b64": img}})' daisy.jpg &> request.json

Step 18

gcloud ml-engine predict --model ${MODEL_NAME} --json-instances request.json

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