Install Dask

You can install dask with conda, with pip, or by installing from source.


Dask is installed by default in Anaconda. You can update Dask using the conda command:

conda install dask

This installs Dask and all common dependencies, including Pandas and NumPy. Dask packages are maintained both on the default channel and on conda-forge. Optionally, you can obtain a minimal Dask installation using the following command:

conda install dask-core

This will install a minimal set of dependencies required to run Dask similar to (but not exactly the same as) python -m pip install dask below.


You can install everything required for most common uses of Dask (arrays, dataframes, …) This installs both Dask and dependencies like NumPy, Pandas, and so on that are necessary for different workloads. This is often the right choice for Dask users:

python -m pip install "dask[complete]"    # Install everything

You can also install only the Dask library. Modules like dask.array, dask.dataframe, dask.delayed, or dask.distributed won’t work until you also install NumPy, Pandas, Toolz, or Tornado, respectively. This is common for downstream library maintainers:

python -m pip install dask                # Install only core parts of dask

We also maintain other dependency sets for different subsets of functionality:

python -m pip install "dask[array]"       # Install requirements for dask array
python -m pip install "dask[bag]"         # Install requirements for dask bag
python -m pip install "dask[dataframe]"   # Install requirements for dask dataframe
python -m pip install "dask[delayed]"     # Install requirements for dask delayed
python -m pip install "dask[distributed]" # Install requirements for distributed dask

We have these options so that users of the lightweight core Dask scheduler aren’t required to download the more exotic dependencies of the collections (Numpy, Pandas, Tornado, etc.).

Install from Source

To install Dask from source, clone the repository from github:

git clone
cd dask
python -m pip install .

You can also install all dependencies as well:

python -m pip install ".[complete]"

You can view the list of all dependencies within the extras_require field of

Or do a developer install by using the -e flag:

python -m pip install -e .


Dask is included by default in the Anaconda distribution.

Optional dependencies

Specific functionality in Dask may require additional optional dependencies. For example, reading from Amazon S3 requires s3fs. These optional dependencies and their minimum supported versions are listed below.

Dependency Version Description
bokeh >=1.0.0 Visualizing dask diagnostics
cloudpickle >=0.2.2 Pickling support for Python objects
cityhash   Faster hashing of arrays
distributed >=2.0 Distributed computing in Python
fastparquet   Storing and reading data from parquet files
fsspec >=0.6.0 Used for local, cluster and remote data IO
gcsfs >=0.4.0 File-system interface to Google Cloud Storage
murmurhash   Faster hashing of arrays
numpy >=1.13.0 Required for dask.array
pandas >=0.23.0 Required for dask.dataframe
partd >=0.3.10 Concurrent appendable key-value storage
psutil   Enables a more accurate CPU count
pyarrow >=0.14.0 Python library for Apache Arrow
s3fs >=0.4.0 Reading from Amazon S3
sqlalchemy   Writing and reading from SQL databases
cytoolz/toolz >=0.8.2 Utility functions for iterators, functions, and dictionaries
xxhash   Faster hashing of arrays


Test Dask with py.test:

cd dask
py.test dask

Please be aware that installing Dask naively may not install all requirements by default. Please read the pip section above which discusses requirements. You may choose to install the dask[complete] version which includes all dependencies for all collections. Alternatively, you may choose to test only certain submodules depending on the libraries within your environment. For example, to test only Dask core and Dask array we would run tests as follows:

py.test dask/tests dask/array/tests