Molecules

For more details, see Machine Learning with Apache Beam and TensorFlow in the docs.

This sample shows how to create, train, evaluate, and make predictions on a machine learning model, using Apache Beam, Google Cloud Dataflow, TensorFlow, and Google Cloud ML Engine.

The dataset for this sample is extracted from the National Center for Biotechnology Information (FTP source). The file format is SDF. Here's a more detailed description of the MDL/SDF file format.

These are the general steps:

Data extraction
Preprocessing the data
Training the model
Doing predictions

Initial setup

NOTE: This requires python2, Apache Beam does not currently support python3.

Getting the source code

You can clone the github repository and then navigate to the molecules sample directory. The rest of the instructions assume that you are in that directory.

git clone https://github.com/GoogleCloudPlatform/cloudml-samples.git
cd cloudml-samples/molecules

Python virtual environment

Using virtualenv to isolate your dependencies is recommended. To set up, make sure you have the virtualenv package installed.

pip install --user virtualenv

To create and activate a new virtual environment, run the following commands:

python -m virtualenv env
source env/bin/activate

To deactivate the virtual environment, run:

deactivate

See virtualenv for details.

Installing requirements

You can use the requirements.txt to install the dependencies.

pip install -U -r requirements.txt

Quickstart

We'll start by running the end-to-end script locally. To run simply run the run-local comand:

./run-local

The script requires a working directory, which is where all the temporary files and other intermediate data will be stored throughout the full run. By default it will use /tmp/cloudml-samples/molecules as the working directory. To specify a different working directory, you can specify it via the --work-dir option.

# To use a different local path
./run-local --work-dir ~/cloudml-samples/molecules

# To use a Google Cloud Storage path
./run-local --work-dir gs://<your bucket name here>/cloudml-samples/molecules

Each SDF file contains data for 25,000 molecules. The script will download only 5 SDF files to the working directory by default. To use a different number of data files, you can use the --max-data-files option.

# To use 10 data files
./run-local --max-data-files 10

To run on Google Cloud Platform, all the files must reside in Google Cloud Storage. To run use the run-cloud command.

NOTE: this will incur charges on your Google Cloud Platform project.

# This will use only 5 data files by default
./run-cloud --work-dir gs://<your bucket name>/cloudml-samples/molecules

Data Extraction

Source code: data-extractor.py

This is a data extraction tool to download SDF files from the specified FTP source. The data files will be stored within a data subdirectory inside the working directory.

To store data files locally:

# The default --work-dir is /tmp/cloudml-samples/molecules
python data-extractor.py --max-data-files 5

To store data files to a Google Cloud Storage location:

NOTE: this will incur charges on your Google Cloud Platform project. See Storage pricing.

WORK_DIR=gs://<your bucket name>/cloudml-samples/molecules
python data-extractor.py --work-dir $WORK_DIR --max-data-files 5

Preprocessing

Source code: preprocess.py

This is an Apache Beam pipeline that will do all the preprocessing necessary to train a Machine Learning model. It uses tf.Transform, which is part of TensorFlow Extended, to do any processing that requires a full pass over the dataset.

For this sample, we're doing a very simple feature extraction. It uses Apache Beam to parse the SDF files and count how many Carbon, Hydrogen, Oxygen, and Nitrogen atoms a molecule has. To create more complex models we would need to extract more sophisticated features, such as Coulomb Matrices.

We will eventually train a Neural Network to do the predictions. Neural Networks are more stable when dealing with small values, so it's always a good idea to normalize the inputs to a small range (typically from 0 to 1). Since there's no maximum number of atoms a molecule can have, we have to go through the entire dataset to find the minimum and maximum counts. Fortunately, tf.Transform integrates with our Apache Beam pipeline and does that for us.

After preprocessing our dataset, we also want to split it into a training and evaluation dataset. The training dataset will be used to train the model. The evaluation dataset contains elements that the training has never seen, and since we also know the "answers" (the molecular energy), we'll use these to validate that the training accuracy roughly matches the accuracy on unseen elements.

These are the general steps:

Parse the SDF files
Feature extraction (count atoms)
*Normalization (normalize counts to 0 to 1)
Split into 80% training data and 20% evaluation data

(*) During the normalization step, the Beam pipeline doesn't actually apply the tf.Transform function to our data. It analyzes the whole dataset to find the values it needs (in this case the minimums and maximums), and with that it creates a TensorFlow graph of operations with those values as constants. This graph of operations will be applied by TensorFlow itself, allowing us to pass the unnormalized data as inputs rather than having to normalize them ourselves during prediction.

The preprocess.py script will preprocess all the data files it finds under $WORK_DIR/data/, which is the path where data-extractor.py stores the files.

If your training data is small enough, it will be faster to run locally.

# The default --work-dir is /tmp/cloudml-samples/molecules
python preprocess.py

As you want to preprocess a larger amount of data files, it will scale better using Cloud Dataflow.

NOTE: this will incur charges on your Google Cloud Platform project. See Dataflow pricing.

PROJECT=$(gcloud config get-value project)
WORK_DIR=gs://<your bucket name>/cloudml-samples/molecules
python preprocess.py \
  --project $PROJECT \
  --runner DataflowRunner \
  --temp_location $WORK_DIR/beam-temp \
  --setup_file ./setup.py \
  --work-dir $WORK_DIR

Training the Model

Source code: trainer/task.py

We'll train a Deep Neural Network Regressor in TensorFlow. This will use the preprocessed data stored within the working directory. During the preprocessing stage, the Apache Beam pipeline transformed extracted all the features (counts of elements) and tf.Transform generated a graph of operations to normalize those features.

The TensorFlow model actually takes the unnormalized inputs (counts of elements), applies the tf.Transform's graph of operations to normalize the data, and then feeds that into our DNN regressor.

For small datasets it will be faster to run locally.

# The default --work-dir is /tmp/cloudml-samples/molecules
python trainer/task.py

# To get the path of the trained model
EXPORT_DIR=/tmp/cloudml-samples/molecules/model/export/final
MODEL_DIR=$(ls -d -1 $EXPORT_DIR/* | sort -r | head -n 1)

If the training dataset is too large, it will scale better to train on Cloud Machine Learning Engine.

NOTE: this will incur charges on your Google Cloud Platform project. See ML Engine pricing.

JOB="cloudml_samples_molecules_$(date +%Y%m%d_%H%M%S)"
BUCKET=gs://<your bucket name>
WORK_DIR=$BUCKET/cloudml-samples/molecules
gcloud ml-engine jobs submit training $JOB \
  --module-name trainer.task \
  --package-path trainer \
  --staging-bucket $BUCKET \
  --runtime-version 1.8 \
  --stream-logs \
  -- \
  --work-dir $WORK_DIR

# To get the path of the trained model
EXPORT_DIR=$WORK_DIR/model/export/final
MODEL_DIR=$(gsutil ls -d $EXPORT_DIR/* | sort -r | head -n 1)

To visualize the training job, we can use TensorBoard.

tensorboard --logdir $WORK_DIR/model

You can access the results at localhost:6006.

Predictions

Option 1: Batch Predictions

Source code: predict.py

Batch predictions are optimized for throughput rather than latency. These work best if there's a large amount of predictions to make and you can wait for all of them to finish before having the results.

For batches with a small number of data files, it will be faster to run locally.

# For simplicity, we'll use the same files we used for training
python predict.py \
  --model-dir $MODEL_DIR \
  batch \
  --inputs-dir /tmp/cloudml-samples/molecules/data \
  --outputs-dir /tmp/cloudml-samples/molecules/predictions

For batches with a large number of data files, it will scale better using Cloud Dataflow.

# For simplicity, we'll use the same files we used for training
PROJECT=$(gcloud config get-value project)
WORK_DIR=gs://<your bucket name>/cloudml-samples/molecules
python predict.py \
  --work-dir $WORK_DIR \
  --model-dir $MODEL_DIR \
  batch \
  --project $PROJECT \
  --runner DataflowRunner \
  --temp_location $WORK_DIR/beam-temp \
  --setup_file ./setup.py \
  --inputs-dir $WORK_DIR/data \
  --outputs-dir $WORK_DIR/predictions

Option 2: Streaming Predictions

Source code: predict.py

Streaming predictions are optimized for latency rather than throughput. These work best if you are sending sporadic predictions, but want to get the results as soon as possible.

This streaming service will receive molecules from a PubSub topic and publish the prediction results to another PubSub topic. We'll have to create the topics first.

# To create the inputs topic
gcloud pubsub topics create molecules-inputs

# To create the outputs topic
gcloud pubsub topics create molecules-predictions

For testing purposes, we can start the online streaming prediction service locally.

# Run on terminal 1
PROJECT=$(gcloud config get-value project)
python predict.py \
  --model-dir $MODEL_DIR \
  stream \
  --project $PROJECT
  --inputs-topic molecules-inputs \
  --outputs-topic molecules-predictions

For a highly available and scalable service, it will scale better using Cloud Dataflow.

# Run on terminal 1
PROJECT=$(gcloud config get-value project)
WORK_DIR=gs://<your bucket name>/cloudml-samples/molecules
python predict.py \
  --work-dir $WORK_DIR \
  --model-dir $MODEL_DIR \
  stream \
  --project $PROJECT
  --runner DataflowRunner \
  --temp_location $WORK_DIR/beam-temp \
  --setup_file ./setup.py \
  --inputs-topic molecules-inputs \
  --outputs-topic molecules-predictions

Now that we have the prediction service running, we want to run a publisher to send molecules to the streaming prediction service, and we also want a subscriber to be listening for the prediction results.

For convenience, we provided a sample publisher.py and subscriber.py to show how to implement one.

These will have to be run as different processes concurrently, so you'll need to have a different terminal running each command.

NOTE: remember to activate the virtualenv on each terminal.

We'll first run the subscriber, which will listen for prediction results and log them.

# Run on terminal 2
python subscriber.py \
  --project $PROJECT \
  --topic molecules-predictions

We'll then run the publisher, which will parse SDF files from a directory and publish them to the inputs topic. For convenience, we'll use the same SDF files we used for training.

# Run on terminal 3
python publisher.py \
  --project $PROJECT \
  --topic molecules-inputs \
  --inputs-dir $WORK_DIR/data

Once the publisher starts parsing and publishing molecules, we'll start seeing predictions from the subscriber.

Option 3: Cloud ML Engine Predictions

If you have a different way to extract the features (in this case the atom counts) that is not through our existing preprocessing pipeline for SDF files, it might be easier to build a JSON file with one request per line and make the predictions on Cloud ML Engine.

We've included the sample-requests.json file with an example of how these requests look like. Here are the contents of the file:

{"TotalC": 9, "TotalH": 17, "TotalO": 4, "TotalN": 1}
{"TotalC": 9, "TotalH": 18, "TotalO": 4, "TotalN": 1}
{"TotalC": 7, "TotalH": 8, "TotalO": 4, "TotalN": 0}
{"TotalC": 3, "TotalH": 9, "TotalO": 1, "TotalN": 1}

Before creating the model in Cloud ML Engine, it is a good idea to test our model's predictions locally:

# First we have to get the exported model's directory
EXPORT_DIR=$WORK_DIR/model/export/final
if [[ $EXPORT_DIR == gs://* ]]; then
  # If it's a GCS path, use gsutil
  MODEL_DIR=$(gsutil ls -d $EXPORT_DIR/* | sort -r | head -n 1)
else
  # If it's a local path, use ls
  MODEL_DIR=$(ls -d -1 $EXPORT_DIR/* | sort -r | head -n 1)
fi

# To do the local predictions
gcloud ml-engine local predict \
  --model-dir $MODEL_DIR \
  --json-instances sample-requests.json

For reference, these are the real energy values for the sample-requests.json file:

PREDICTIONS
[37.801]
[44.1107]
[19.4085]
[-0.1086]

Once we are happy with our results, we can now upload our model into Cloud ML Engine for online predictions.

# We want the model to reside on GCS and get its path
EXPORT_DIR=$WORK_DIR/model/export/final
if [[ $EXPORT_DIR == gs://* ]]; then
  # If it's a GCS path, use gsutil
  MODEL_DIR=$(gsutil ls -d $EXPORT_DIR/* | sort -r | head -n 1)
else
  # If it's a local path, first upload it to GCS
  LOCAL_MODEL_DIR=$(ls -d -1 $EXPORT_DIR/* | sort -r | head -n 1)
  MODEL_DIR=$BUCKET/cloudml-samples/molecules/model
  gsutil -m cp -r $LOCAL_MODEL_DIR $MODEL_DIR
fi

# Now create the model and a version in Cloud ML Engine and set it as default
MODEL=molecules
REGION=$(gcloud config get-value compute/region)
gcloud ml-engine models create $MODEL \
  --regions $REGION

VERSION="${MODEL}_$(date +%Y%m%d_%H%M%S)"
gcloud ml-engine versions create $VERSION \
  --model $MODEL \
  --origin $MODEL_DIR \
  --runtime-version 1.8

gcloud ml-engine versions set-default $VERSION \
  --model $MODEL

# Finally, we can request predictions via gcloud ml-engine
gcloud ml-engine predict \
  --model $MODEL \
  --version $VERSION \
  --json-instances sample-requests.json

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README.md

README.md

Molecules

Initial setup

Getting the source code

Python virtual environment

Installing requirements

Quickstart

Data Extraction

Preprocessing

Training the Model

Predictions

Option 1: Batch Predictions

Option 2: Streaming Predictions

Option 3: Cloud ML Engine Predictions

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README.md

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README.md

File metadata and controls

Molecules

Initial setup

Getting the source code

Python virtual environment

Installing requirements

Quickstart

Data Extraction

Preprocessing

Training the Model

Predictions

Option 1: Batch Predictions

Option 2: Streaming Predictions

Option 3: Cloud ML Engine Predictions