MNIST Image Classification with TensorFlow on Cloud ML Engine

This notebook demonstrates how to implement different image models on MNIST using Estimator.

Note the MODEL_TYPE; change it to try out different models



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import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE = "dnn"  # "linear", "dnn", "dnn_dropout", or "cnn"

# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"] = REGION
os.environ["MODEL_TYPE"] = MODEL_TYPE
os.environ["TFVERSION"] = "1.13"  # Tensorflow version



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%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION

Run as a Python module

In the previous notebook (mnist_linear.ipynb) we ran our code directly from the notebook.

Now since we want to run our code on Cloud ML Engine, we've packaged it as a python module.

The model.py and task.py containing the model code is in mnistmodel/trainer

Complete the TODOs in model.py before proceeding!

Once you've completed the TODOs, set MODEL_TYPE and run it locally for a few steps to test the code.



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%%bash
rm -rf mnistmodel.tar.gz mnist_trained
gcloud ai-platform local train \
    --module-name=trainer.task \
    --package-path=${PWD}/mnistmodel/trainer \
    -- \
    --output_dir=${PWD}/mnist_trained \
    --train_steps=100 \
    --learning_rate=0.01 \
    --model=$MODEL_TYPE

Now, let's do it on Cloud ML Engine so we can train on GPU: --scale-tier=BASIC_GPU

Note the GPU speed up depends on the model type. You'll notice the more complex CNN model trains significantly faster on GPU, however the speed up on the simpler models is not as pronounced.



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%%bash
OUTDIR=gs://${BUCKET}/mnist/trained_${MODEL_TYPE}
JOBNAME=mnist_${MODEL_TYPE}_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ai-platform jobs submit training $JOBNAME \
    --region=$REGION \
    --module-name=trainer.task \
    --package-path=${PWD}/mnistmodel/trainer \
    --job-dir=$OUTDIR \
    --staging-bucket=gs://$BUCKET \
    --scale-tier=BASIC_GPU \
    --runtime-version=$TFVERSION \
    -- \
    --output_dir=$OUTDIR \
    --train_steps=10000 --learning_rate=0.01 --train_batch_size=512 \
    --model=$MODEL_TYPE --batch_norm

Monitor training with TensorBoard

To activate TensorBoard within the JupyterLab UI navigate to "File" - "New Launcher". Then double-click the 'Tensorboard' icon on the bottom row.

TensorBoard 1 will appear in the new tab. Navigate through the three tabs to see the active TensorBoard. The 'Graphs' and 'Projector' tabs offer very interesting information including the ability to replay the tests.

You may close the TensorBoard tab when you are finished exploring.

Deploying and predicting with model

Deploy the model:



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%%bash
MODEL_NAME="mnist"
MODEL_VERSION=${MODEL_TYPE}
MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/mnist/trained_${MODEL_TYPE}/export/exporter | tail -1)
echo "Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes"
#gcloud ai-platform versions delete ${MODEL_VERSION} --model ${MODEL_NAME}
#gcloud ai-platform models delete ${MODEL_NAME}
gcloud ai-platform models create ${MODEL_NAME} --regions $REGION
gcloud ai-platform versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version=$TFVERSION

To predict with the model, let's take one of the example images.



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import json, codecs
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

HEIGHT = 28
WIDTH = 28

mnist = input_data.read_data_sets("mnist/data", one_hot = True, reshape = False)
IMGNO = 5 #CHANGE THIS to get different images
jsondata = {"image": mnist.test.images[IMGNO].reshape(HEIGHT, WIDTH).tolist()}
json.dump(jsondata, codecs.open("test.json", "w", encoding = "utf-8"))
plt.imshow(mnist.test.images[IMGNO].reshape(HEIGHT, WIDTH));

Send it to the prediction service



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%%bash
gcloud ai-platform predict \
    --model=mnist \
    --version=${MODEL_TYPE} \
    --json-instances=./test.json

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