sdks/python/apache_beam/examples/inference/multi_language_inference/README.md

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For a detailed explanation of this inference example, visit the [documentation](https://beam.apache.org/documentation/ml/multi-language-inference/).
## Set up Python virtual environment
Make sure to set up a virtual environment for Python with all the required dependencies.
More details on how to do this can be found [here](https://beam.apache.org/get-started/quickstart-py/#set-up-your-environment).
## Running the Java pipeline
Make sure you have Maven installed and added to PATH. Also make sure that JAVA_HOME
points to the correct Java version.

First we need to download the Maven archetype for Beam. Run the following command:

```bash
export BEAM_VERSION=<Beam version>

mvn archetype:generate \
    -DarchetypeGroupId=org.apache.beam \
    -DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \
    -DarchetypeVersion=$BEAM_VERSION \
    -DgroupId=org.example \
    -DartifactId=multi-language-beam \
    -Dversion="0.1" \
    -Dpackage=org.apache.beam.examples \
    -DinteractiveMode=false
```
This will set up all the required dependencies for the Java pipeline. Next the pipeline needs to be
implemented. The logic of this pipeline is written in the `MultiLangRunInference.java` file. After that,
run the following command to start the Java pipeline:

```bash
export GCP_PROJECT=<your gcp project>
export GCP_BUCKET=<your gcp bucker>
export GCP_REGION=<region of bucket>
export MODEL_NAME=bert-base-uncased
export LOCAL_PACKAGE=<path to tarball>

cd last_word_prediction
mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MultiLangRunInference \
    -Dexec.args="--runner=DataflowRunner \
                 --project=$GCP_PROJECT\
                 --region=$GCP_REGION \
                 --gcpTempLocation=gs://$GCP_BUCKET/temp/ \
                 --inputFile=gs://$GCP_BUCKET/input/imdb_reviews.csv \
                 --outputFile=gs://$GCP_BUCKET/output/ouput.txt \
                 --modelPath=gs://$GCP_BUCKET/input/bert-model/bert-base-uncased.pth \
                 --modelName=$MODEL_NAME \
                 --localPackage=$LOCAL_PACKAGE" \
    -Pdataflow-runner
```

The `localPackage` argument is the path to a locally available package compiled as a tarball. This package must be created by the user and contain the python transforms used in the pipeline.
Make sure to run this in the [`last_word_prediction`](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference/multi_language_inference/last_word_prediction) directory. This will start the Java pipeline.