You can install dask with
pip, or by installing from source.
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)
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:
pip install "dask[complete]" # Install everything
You can also install only the Dask library. Modules like
dask.distributed won’t work until you also install NumPy,
Pandas, Toolz, or Tornado, respectively. This is common for downstream library
pip install dask # Install only core parts of dask
We also maintain other dependency sets for different subsets of functionality:
pip install "dask[array]" # Install requirements for dask array pip install "dask[bag]" # Install requirements for dask bag pip install "dask[dataframe]" # Install requirements for dask dataframe pip install "dask[delayed]" # Install requirements for dask delayed 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 https://github.com/dask/dask.git cd dask pip install .
You can also install all dependencies as well:
pip install ".[complete]"
You can view the list of all dependencies within the
Or do a developer install by using the
pip install -e .
Specific functionality in Dask may require additional optional dependencies.
For example, reading from Amazon S3 requires
These optional dependencies and their minimum supported versions are listed below.
|bokeh||>=1.0.0||Visualizing dask diagnostics|
|cloudpickle||>=0.2.1||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||>=0.5.1||Used for local, cluster and remote data IO|
|gcsfs||File-system interface to Google Cloud Storage|
|murmurhash||Faster hashing of arrays|
|numpy||>=1.13.0||Required for dask.array|
|pandas||>=0.21.0||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||Reading from Amazon S3|
|sqlalchemy||Writing and reading from SQL databases|
|toolz||>=0.7.3||Utility functions for iterators, functions, and dictionaries|
|xxhash||Faster hashing of arrays|
Test Dask with
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
py.test dask/tests dask/array/tests