github.com/apache/beam/sdks/v2@v2.48.2/python/apache_beam/examples/inference/multi_language_inference/README.md (about)

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    19  For a detailed explanation of this inference example, visit the [documentation](https://beam.apache.org/documentation/ml/multi-language-inference/).
    20  ## Set up Python virtual environment
    21  Make sure to set up a virtual environment for Python with all the required dependencies.
    22  More details on how to do this can be found [here](https://beam.apache.org/get-started/quickstart-py/#set-up-your-environment).
    23  ## Running the Java pipeline
    24  Make sure you have Maven installed and added to PATH. Also make sure that JAVA_HOME
    25  points to the correct Java version.
    26  
    27  First we need to download the Maven archetype for Beam. Run the following command:
    28  
    29  ```bash
    30  export BEAM_VERSION=<Beam version>
    31  
    32  mvn archetype:generate \
    33      -DarchetypeGroupId=org.apache.beam \
    34      -DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \
    35      -DarchetypeVersion=$BEAM_VERSION \
    36      -DgroupId=org.example \
    37      -DartifactId=multi-language-beam \
    38      -Dversion="0.1" \
    39      -Dpackage=org.apache.beam.examples \
    40      -DinteractiveMode=false
    41  ```
    42  This will set up all the required dependencies for the Java pipeline. Next the pipeline needs to be
    43  implemented. The logic of this pipeline is written in the `MultiLangRunInference.java` file. After that,
    44  run the following command to start the Java pipeline:
    45  
    46  ```bash
    47  export GCP_PROJECT=<your gcp project>
    48  export GCP_BUCKET=<your gcp bucker>
    49  export GCP_REGION=<region of bucket>
    50  export MODEL_NAME=bert-base-uncased
    51  export LOCAL_PACKAGE=<path to tarball>
    52  
    53  cd last_word_prediction
    54  mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MultiLangRunInference \
    55      -Dexec.args="--runner=DataflowRunner \
    56                   --project=$GCP_PROJECT\
    57                   --region=$GCP_REGION \
    58                   --gcpTempLocation=gs://$GCP_BUCKET/temp/ \
    59                   --inputFile=gs://$GCP_BUCKET/input/imdb_reviews.csv \
    60                   --outputFile=gs://$GCP_BUCKET/output/ouput.txt \
    61                   --modelPath=gs://$GCP_BUCKET/input/bert-model/bert-base-uncased.pth \
    62                   --modelName=$MODEL_NAME \
    63                   --localPackage=$LOCAL_PACKAGE" \
    64      -Pdataflow-runner
    65  ```
    66  
    67  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.
    68  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.
    69