> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/datasets/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/datasets/how-to-guides/general-usage/beam-datasets.md).

# Beam Datasets

## Beam Datasets

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).

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()](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset). But if you want to run your own Beam pipeline with Dataflow, here is how:

1. Specify the dataset and configuration you want to process:

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```
DATASET_NAME=your_dataset_name  # ex: wikipedia
CONFIG_NAME=your_config_name    # ex: 20220301.en
```

2. Input your Google Cloud Platform information:

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```
PROJECT=your_project
BUCKET=your_bucket
REGION=your_region
```

3. Specify your Python requirements:

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```
echo "datasets" > /tmp/beam_requirements.txt
echo "apache_beam" >> /tmp/beam_requirements.txt
```

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"
```

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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