Some common deployment options you may want to consider are:

  -   A commercial Dask deployment option like `Coiled <https://coiled.io?utm_source=dask-docs&utm_medium=deploying-cloud>`_
      to handle the creation and management of Dask clusters on AWS, GCP, and Azure.
  -   A managed Kubernetes service and Dask's
      :doc:`Kubernetes integration <deploying-kubernetes>`.
  -   Directly launching cloud resources such as VMs or containers via a cluster manager with
      `Dask Cloud Provider <https://cloudprovider.dask.org/en/latest/>`_.
  -   A managed Yarn service,
      like `Amazon EMR <https://aws.amazon.com/emr/>`_
      or `Google Cloud DataProc <https://cloud.google.com/dataproc/>`_
      and `Dask-Yarn <https://yarn.dask.org>`_
      (specific documentation for the popular Amazon EMR service can be found
      `here <https://yarn.dask.org/en/latest/aws-emr.html>`_.)

  .. figure:: images/cloud-provider-logos.svg

    `Coiled <https://coiled.io?utm_source=dask-docs&utm_medium=deploying-cloud>`_
    deploys managed Dask clusters on AWS, GCP, and Azure. It's free for most users and
    has several features that address common
    :doc:`deployment pain points <deployment-considerations>` like:

    - Easy to use API
    - Automatic software synchronization
    - Easy access to any cloud hardware (like GPUs) in any region
    - Robust logging, cost controls, and metrics collection

    .. code-block:: python

        >>> import coiled
        >>> cluster = coiled.Cluster(
        ...     n_workers=100,             # Size of cluster
        ...     region="us-west-2",        # Same region as data
        ...     vm_type="m6i.xlarge",      # Hardware of your choosing
        ... )
        >>> client = cluster.get_client()
    
    Coiled is recommended for deploying Dask on the cloud.
    Though there are non-commercial, open source options like
    Dask Cloud Provider, Dask-Gateway, and Dask-Yarn that are also available
    (see :ref:`cloud deployment options <cloud-deployment-options>`
    for additional options.)

    Using `Dask Cloud Provider <https://cloudprovider.dask.org/en/latest/>`_ to launch a cluster of
    VMs on a platform like `DigitalOcean <https://www.digitalocean.com/>`_ can be as convenient as
    launching a local cluster.

    .. code-block:: python

        >>> import dask.config
        >>> dask.config.set({"cloudprovider.digitalocean.token": "yourAPItoken"})
        >>> from dask_cloudprovider.digitalocean import DropletCluster
        >>> cluster = DropletCluster(n_workers=1)
        Creating scheduler instance
        Created droplet dask-38b817c1-scheduler
        Waiting for scheduler to run
        Scheduler is running
        Creating worker instance
        Created droplet dask-38b817c1-worker-dc95260d

    Many of the cluster managers in Dask Cloud Provider work by launching VMs with a startup script
    that pulls down the :doc:`Dask Docker image <deploying-docker>` and runs Dask components within that container.
    As with all cluster managers the VM resources, Docker image, etc are all configurable.

    You can then connect a client and work with the cluster as if it were on your local machine.

    .. code-block:: python

        >>> client = cluster.get_client()

Use `s3fs <https://s3fs.readthedocs.io/>`_ for accessing data on Amazon's S3.

.. code-block:: bash

    pip install s3fs

Use `gcsfs <https://gcsfs.readthedocs.io/>`_ for accessing data on Google's GCS.

.. code-block:: bash

    pip install gcsfs

Use `adlfs <https://github.com/dask/adlfs/>`_ for accessing data on Microsoft's Data Lake or Blob Storage.

.. code-block:: bash

    pip install adlfs