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| Some datasets are too large to be processed on a single machine. Instead, you can process them with [Apache Beam](https://beam.apache.org/), a library for parallel data processing. The processing pipeline is executed on a distributed processing backend such as [Apache Flink](https://flink.apache.org/), [Apache Spark](https://spark.apache.org/), or [Google Cloud Dataflow](https://cloud.google.com/dataflow). |
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| We have already created Beam pipelines for some of the larger datasets like [wikipedia](https://huggingface.co/datasets/wikipedia), and [wiki40b](https://huggingface.co/datasets/wiki40b). You can load these normally with [`load_dataset`]. But if you want to run your own Beam pipeline with Dataflow, here is how: |
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| 1. Specify the dataset and configuration you want to process: |
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| ``` |
| DATASET_NAME=your_dataset_name |
| CONFIG_NAME=your_config_name |
| ``` |
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| 2. Input your Google Cloud Platform information: |
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| ``` |
| PROJECT=your_project |
| BUCKET=your_bucket |
| REGION=your_region |
| ``` |
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| 3. Specify your Python requirements: |
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| ``` |
| echo "datasets" > /tmp/beam_requirements.txt |
| echo "apache_beam" >> /tmp/beam_requirements.txt |
| ``` |
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| 4. Run the pipeline: |
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| ``` |
| datasets-cli run_beam datasets/$DATASET_NAME \ |
| --name $CONFIG_NAME \ |
| --save_info \ |
| --cache_dir gs://$BUCKET/cache/datasets \ |
| --beam_pipeline_options=\ |
| "runner=DataflowRunner,project=$PROJECT,job_name=$DATASET_NAME-gen,"\ |
| "staging_location=gs://$BUCKET/binaries,temp_location=gs://$BUCKET/temp,"\ |
| "region=$REGION,requirements_file=/tmp/beam_requirements.txt" |
| ``` |
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| <Tip> |
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| When you run your pipeline, you can adjust the parameters to change the runner (Flink or Spark), output location (S3 bucket or HDFS), and the number of workers. |
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| </Tip> |
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