This page describes various ways to set up Dask on different hardware, either locally on your own machine or on a distributed cluster. If you are just getting started then this page is unnecessary. Dask does not require any setup if you only want to use it on a single computer.

Dask has two families of task schedulers:

  1. Single machine scheduler: This scheduler provides basic features on a local process or thread pool. This scheduler was made first and is the default. It is simple and cheap to use. It can only be used on a single machine and does not scale.
  2. Distributed scheduler: This scheduler is more sophisticated, offers more features, but also requires a bit more effort to set up. It can run locally or distributed across a cluster.

If you import Dask, set up a computation, and then call compute then you will use the single-machine scheduler by default. To use the dask.distributed scheduler you must set up a Client

import dask.dataframe as dd
df = dd.read_csv(...)
df.x.sum().compute()  # This uses the single-machine scheduler by default
from dask.distributed import Client
client = Client(...)  # Connect to distributed cluster and override default
df.x.sum().compute()  # This now runs on the distributed system

Note that the newer dask.distributed scheduler is often preferable even on single workstations. It contains many diagnostics and features not found in the older single-machine scheduler. The following pages explain in more detail how to set up Dask on a variety of local and distributed hardware.

  • Single Machine:
    • Default Scheduler: The no-setup default. Uses local threads or processes for larger-than-memory processing
    • Dask.distributed: The sophistication of the newer system on a single machine. This provides more advanced features while still requiring almost no setup.
  • Distributed computing:
    • Manual Setup: The command line interface to set up dask-scheduler and dask-worker processes. Useful for IT or anyone building a deployment solution.
    • SSH: Use SSH to set up Dask across an un-managed cluster
    • High Performance Computers: How to run Dask on traditional HPC environments using tools like MPI, or job schedulers like SLURM, SGE, TORQUE, LSF, and so on
    • Kubernetes: Deploy Dask with the popular Kubernetes resource manager using either Helm or a native deployment.
    • YARN / Hadoop: Deploy Dask on YARN clusters, such as are found in traditional Hadoop installations.
    • Python API (advanced): Create Scheduler and Worker objects from Python as part of a distributed Tornado TCP application. This page is useful for those building custom frameworks.
    • Docker containers are available and may be useful in some of the solutions above.
    • Cloud for current recommendations on how to deploy Dask and Jupyter on common cloud providers like Amazon, Google, or Microsoft Azure.