RAPIDS: Difference between revisions

** '''base''' contains a RAPIDS environment ready to use. Use this type of image if you want to submit a job to the Slurm scheduler.

** '''runtime''' extends the base image by adding a Jupyter notebook server and example notebooks. Use this type of image if you want to interactively work with RAPIDS through notebooks and examples.     

** '''devel''' contains the full RAPIDS source tree, the compiler toolchain, the debugging tools, the headers and the static libraries for RAPIDS development. Use this type of image if you want to implement customized operations with low-level access to cuda-based processes.

For example, if a Docker pull command for a selected image is given as:

On a computer that has Singularity supported, you can build a Singularity image, e.g. called ''rapids.sif'', with following command based on the given pull tag:  

It usually takes from thirty to sixty minutes to complete the image building process. Since the image size is relatively large, you need to have enough memory and disk spaces on the server for building such an image.

=Work on Clusters with a RAPIDS Singularity image= <!--T:12-->

Once you have a Singularity image for RAPIDS located on Compute Canada clusters, you can work interactively by requesting an interactive session on a GPU node or submit a batch job to the Slurm queue if you have your RAPIDS code ready.

==Explore the contents in RAPIDS==

If simply exploring the contents without doing any computations, you can use following commands to access the container shell of the Singularity image, e.g. called ''rapids.sif''  on any node without requesting any GPUs.

Load the Singularity module first:

Then access the container shell:

The shell prompt is then changed to:

Inside the singularity shell initiate Conda and activate RAPIDS environment:

The shell prompt in the rapids env is then changed to:

Then you can list available packages in the rapids env:

To deactivate rapids env and exit from the container:

If a Singularity image was built based on a ''runtime'' or a ''devel'' type of Docker image, it includes a Jupyter Notebook server and can be used to explore RAPIDS interactively on a compute node with GPU.

To request an interactive session on a compute node with a single GPU, e.g. a T4 type of GPU on Graham:

Once the requested resource is granted, start RAPIDS shell on the GPU node:

Here <tt>--nv</tt> option is to bind the GPU driver on the host to the container, so the GPU device can be accessed from inside the singularity container and the <tt>-B</tt> option is to bind any file system that you would like to access from inside the container.

After the shell prompt changes to <tt>Singularity></tt>, you can check the GPU stats in the container to make sure the GPU device is accessible:

Then to initiate Conda and activate rapids env:

After the shell prompt changes to <tt>(rapids) Singularity></tt>, you can launch the Jupyter Notebook server in the rapids env with following command, and the URL of the Notebook server is displayed after it starts successfully::

As there is no direct Internet connection on a compute node on Graham, you would need to setup an SSH tunnel with port forwarding between your local computer and the GPU node. See [[Jupyter#Connecting_to_Jupyter_Notebook|detailed instructions for connecting to Jupyter Notebook]].

==Submit a RAPIDS job to Slurm scheduler== <!--T:31-->

Here is an example of a job execution script, e.g. ''run_script.sh'', which you would like to run in the container to start the execution of the python code that has been programed with RAPIDS:  

=Helpful Links= <!--T:36-->

* [https://docs.rapids.ai/ RAPIDS Docs]: a collection of all the documentation for RAPIDS, how to stay connected and report issues.

* [https://github.com/rapidsai/notebooks RAPIDS Notebooks]: a collection of example notebooks on GitHub for getting started quickly.