Installing Dask-CHTC¶

Dask-CHTC is a Python package that allows you to run Dask clusters and Jupyter notebook servers on CHTC submit nodes. To run Dask on CHTC, you will need to

Install a “personal” Python on a CHTC submit node.
Install whatever other packages you want, like numpy, matplotlib, dask-ml, or jupyterlab.
Install dask-chtc itself.

Attention

We currently only support the Dask-CHTC workflow on the submit3.chtc.wisc.edu submit node. If you do not have an account on submit3.chtc.wisc.edu, you will need to request one.

Install a Personal Python¶

You will need a Python installation on a CHTC submit node to run your code. You will be able to manage packages in this Python installation just like you would on your local machine, without needing to work with the CHTC system administrators.

Since you do not have permission to install packages in the “system” Python on CHTC machines (and since you should never do that anyway), you will need to make a “personal” Python installation. We highly recommend doing this using Miniconda, a minimal installation of Python and the conda package manager.

To create a Miniconda installation, first log in to a CHTC submit node (via ssh). Then, download the latest version of the Miniconda installer using wget:

$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Run the installer using bash:

$ bash Miniconda3-latest-Linux-x86_64.sh

The installer will ask you to accept a license agreement, and then ask you several questions about how to install Miniconda itself. We recommend that you do “initialize Miniconda3 by running conda init” when prompted; this will cause Python you just installed to be your default Python in future shell sessions (instead of the system Python).

You may need to begin a new shell session for the commands in the next sections to work as expected. To check that everything is hooked up correctly, try running these commands:

$ which python
$ which conda
$ which pip

They should all resolve to paths inside the Miniconda installation you just created.

Install Packages¶

Your personal Python installation will be used to run all of your code, so you will need to install any packages that you depend on, either inside your code (like numpy) or to do things like run Jupyter notebooks (provided by the jupyter package).

To install packages in your new personal Python installation, use the conda install command. For example, to install numpy, run

$ conda install numpy

You may occasionally need a package that isn’t in the standard Anaconda channel. Many more packages, and more up-to-date versions of packages, are often available in the community-created conda-forge channel channel. For example, to install Dask from conda-forge instead of the default channel, you would run

$ conda install -c conda-forge dask

where -c is short for --channel.

Some packages are provided by other channels. For example, PyTorch asks you to install from their own conda channel:

$ conda install -c pytorch pytorch

conda is mostly compatible with pip; if a package is not available via conda at all, you can install it with pip as usual.

Install Dask-CHTC¶

Attention

These instructions will change in the future as Dask-CHTC stabilizes.

To install Dask-CHTC itself, run

$ pip install --upgrade git+https://github.com/CHTC/dask-chtc.git

To check that installation worked correctly, try running

$ dask-chtc --version
Dask-CHTC version x.y.z

If you don’t see the version message or some error occurs, try re-installing. If that fails, please let us know.

What’s Next?¶

If you like working inside a Jupyter environment, you should read the next two pages: jupyter and Networking and Port Forwarding.

If you are going to run Dask non-interactively (i.e., through a normal Python script, not a notebook), then you’re almost ready to go. Pull the CHTCCluster and Dask client creation code from Dask Cluster Creation and start computing!