> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-kapa-ai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Cloud Storage

> Connect Google Cloud Storage to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.

First you’ll need to install the Google Cloud Storage dependencies as shown here.

```bash
pip install "unstructured[gcs]"
```

## Run Locally

<CodeGroup>
  ```bash Shell
  #!/usr/bin/env bash

  unstructured-ingest \
    gcs \
    --remote-url gs://utic-test-ingest-fixtures-public/ \
    --output-dir gcs-output \
    --num-processes 2 \
    --recursive \
    --verbose
  ```

  ```python Python
  from unstructured.ingest.connector.fsspec.gcs import GcsAccessConfig, SimpleGcsConfig
  from unstructured.ingest.interfaces import (
      PartitionConfig,
      ProcessorConfig,
      ReadConfig,
  )
  from unstructured.ingest.runner import GCSRunner

  if __name__ == "__main__":
      runner = GCSRunner(
          processor_config=ProcessorConfig(
              verbose=True,
              output_dir="gcs-output",
              num_processes=2,
          ),
          read_config=ReadConfig(),
          partition_config=PartitionConfig(),
          connector_config=SimpleGcsConfig(
              access_config=GcsAccessConfig(),
              remote_url="gs://utic-test-ingest-fixtures-public/",
              recursive=True,
          ),
      )
      runner.run()
  ```
</CodeGroup>

## Run via the API

You can also use upstream connectors with the `unstructured` API. For this you’ll need to use the `--partition-by-api` flag and pass in your API key with `--api-key`.

<CodeGroup>
  ```bash Shell
  #!/usr/bin/env bash

  unstructured-ingest \
    gcs \
    --remote-url gs://utic-test-ingest-fixtures-public/ \
    --output-dir gcs-output \
    --num-processes 2 \
    --recursive \
    --verbose \
    --partition-by-api \
    --api-key "$UNSTRUCTURED_API_KEY"
  ```

  ```python Python
  import os

  from unstructured.ingest.connector.fsspec.gcs import GcsAccessConfig, SimpleGcsConfig
  from unstructured.ingest.interfaces import (
      PartitionConfig,
      ProcessorConfig,
      ReadConfig,
  )
  from unstructured.ingest.runner import GCSRunner

  if __name__ == "__main__":
      runner = GCSRunner(
          processor_config=ProcessorConfig(
              verbose=True,
              output_dir="gcs-output",
              num_processes=2,
          ),
          read_config=ReadConfig(),
          partition_config=PartitionConfig(
              partition_by_api=True,
              api_key=os.getenv("UNSTRUCTURED_API_KEY"),
          ),
          connector_config=SimpleGcsConfig(
              access_config=GcsAccessConfig(),
              remote_url="gs://utic-test-ingest-fixtures-public/",
              recursive=True,
          ),
      )
      runner.run()
  ```
</CodeGroup>

Additionally, you will need to pass the `--partition-endpoint` if you’re running the API locally. You can find more information about the `unstructured` API [here](https://github.com/Unstructured-IO/unstructured-api).

For a full list of the options the CLI accepts check `unstructured-ingest gcs --help`.

NOTE: Keep in mind that you will need to have all the appropriate extras and dependencies for the file types of the documents contained in your data storage platform if you’re running this locally. You can find more information about this in the [installation guide](/open-source/installation/overview).