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cloudml-sentiment-analysis

README.md

Sentiment analysis using TensorFlow RNNEstimator on Google Cloud Platform.

Overview.

This code aims at providing a simple example of how to train a RNN model using TensorFlow RNNEstimator on Google Cloud Platform. The model is designed to handle raw text files in input without preprocessing needed. A more detailed guide can be found here.

Problem and data.

The problem is a text classification example where we categorize the movie reviews into positive or negative sentiment. We base this example on the IMDb dataset provided from this website: http://ai.stanford.edu/~amaas/data/sentiment/

Set-up environment.

PROJECT_NAME=sentiment_analysis
git clone https://github.com/GoogleCloudPlatform/professional-services.git
cd professional-services/examples/cloudml-sentiment-analysis
python -m virtualenv env
source env/bin/activate
python -m pip install -U pip
python -m pip install -r requirements.txt

Download data.

DATA_PATH=data
INPUT_DATA=${DATA_PATH}/aclImdb/train
TRAINING_INPUT_DATA=${DATA_PATH}/training_data
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz -P $DATA_PATH
tar -xzf ${DATA_PATH}/aclImdb_v1.tar.gz -C $DATA_PATH

Configure GCP.

PROJECT_ID=<...>
BUCKET_PATH=<...>
gcloud config set project $PROJECT_ID

Move data to GCP.

gsutil -m cp -r $DATA_PATH/aclImdb $BUCKET_PATH
GCP_INPUT_DATA=$BUCKET_PATH/aclImdb/train

Preprocess data.

JOB_NAME=training-$(date +"%Y%m%d-%H%M%S")
PROCESSED_DATA=$BUCKET_PATH/processed_data/$JOB_NAME
python run_preprocessing.py \
  --input_dir=$GCP_INPUT_DATA \
  --output_dir=$PROCESSED_DATA \
  --gcp=True \
  --project_id=$PROJECT_ID \
  --job_name=$JOB_NAME \
  --num_workers=8 \
  --worker_machine_type=n1-highcpu-4 \
  --region=us-central1

Train model locally.

MODEL_NAME=${PROJECT_NAME}_$(date +"%Y%m%d_%H%M%S")
TRAINING_OUTPUT_DIR=models/$MODEL_NAME
python -m trainer.task \
  --input_dir=$PROCESSED_DATA \
  --model_dir=$TRAINING_OUTPUT_DIR

Train model on GCP.

MODEL_NAME=${PROJECT_NAME}_$(date +"%Y%m%d_%H%M%S")
TRAINING_OUTPUT_DIR=${BUCKET_PATH}/$MODEL_NAME
gcloud ml-engine jobs submit training $MODEL_NAME \
  --module-name trainer.task \
  --staging-bucket $BUCKET_PATH \
  --package-path $PWD/trainer \
  --region=us-central1 \
  --runtime-version 1.12 \
  --config=config_hp_tuning.yaml \
  --stream-logs \
  -- \
  --input_dir $PROCESSED_DATA \
  --model_dir $TRAINING_OUTPUT_DIR

Train model locally with gcloud.

MODEL_NAME=${PROJECT_NAME}_$(date +"%Y%m%d_%H%M%S")
TRAINING_OUTPUT_DIR=models/$MODEL_NAME
gcloud ml-engine local train \
  --module-name=trainer.task \
  --package-path=$PWD/trainer \
  -- \
  --input_dir=$PROCESSED_DATA \
  --model_dir=$TRAINING_OUTPUT_DIR

Monitor with tensorboard.

tensorboard --logdir=$TRAINING_OUTPUT_DIR

Save model in GCP.

With HP tuning:

TRIAL_NUMBER=''
MODEL_SAVED_NAME=$(gsutil ls ${TRAINING_OUTPUT_DIR}/${TRIAL_NUMBER}/export/exporter/ | tail -1)

Without HP tuning:

MODEL_SAVED_NAME=$(gsutil ls ${TRAINING_OUTPUT_DIR}/export/exporter/ | tail -1)
gcloud ml-engine models create $PROJECT_NAME \
  --regions us-central1
gcloud ml-engine versions create $MODEL_NAME \
  --model $PROJECT_NAME \
  --origin $MODEL_SAVED_NAME \
  --runtime-version 1.12

Make local online predictions.

gcloud ml-engine local predict \
  --model-dir=${TRAINING_OUTPUT_DIR}/export/exporter/$(ls ${TRAINING_OUTPUT_DIR}/export/exporter/ | tail -1) \
  --text-instances=${DATA_PATH}/aclImdb/test/*/*.txt

Make online predictions with GCP.

gcloud ml-engine predict \
  --model=$PROJECT_NAME \
  --version=$MODEL_NAME \
  --text-instances=$DATA_PATH/aclImdb/test/neg/0_2.txt

Move out of sample data to GCS.

PREDICTION_DATA_PATH=${BUCKET_PATH}/prediction_data
gsutil -m cp -r ${DATA_PATH}/aclImdb/test/ $PREDICTION_DATA_PATH

Make batch predictions with GCP.

JOB_NAME=${PROJECT_NAME}_predict_$(date +"%Y%m%d_%H%M%S")
PREDICTIONS_OUTPUT_PATH=${BUCKET_PATH}/predictions/$JOB_NAME
gcloud ml-engine jobs submit prediction $JOB_NAME \
  --model $PROJECT_NAME \
  --input-paths $PREDICTION_DATA_PATH/neg/* \
  --output-path $PREDICTIONS_OUTPUT_PATH \
  --region us-central1 \
  --data-format TEXT \
  --version $MODEL_NAME

Scoring.

python scoring.py \
  --project_name=$PROJECT_ID \
  --model_name=$PROJECT_NAME \
  --input_path=$DATA_PATH/aclImdb/test \
  --size=1000 \
  --batch_size=20