Coiled#
You can deploy RAPIDS on cloud VMs with GPUs using Coiled. Coiled is a software platform that manages Cloud VMs on your behalf. It manages software environments and can launch Python scripts, Jupyter Notebook servers, Dask clusters or even just individual Python functions. Remote machines are booted just in time and shut down when not in use or idle.
By using the coiled Python library, you can setup and manage Dask clusters with GPUs and RAPIDs on cloud computing environments such as GCP or AWS.
Setup#
Head over to Coiled and register for an account.
Once your account is set up, install the coiled Python library/CLI tool.
pip install coiled
Then you can authenticate with your Coiled account.
coiled login
For more information see the Coiled Getting Started documentation.
Notebook Quickstart#
The simplest way to get up and running with RAPIDS on Coiled is to launch a Jupyter notebook server using the RAPIDS notebook container.
coiled notebook start --gpu --container nvcr.io/nvidia/rapidsai/notebooks:25.02-cuda12.8-py3.12
Software Environments#
By default when running remote operations Coiled will attempt to create a copy of your local software environment which can be loaded onto the remote VMs. While this is an excellent feature it’s likely that you do not have all of the GPU software libraries you wish to use installed locally. In this case we need to tell Coiled which software environment to use.
Container images#
All Coiled commands can be passed a container image to use. This container will be pulled onto the remote VM at launch time.
coiled notebook start --gpu --container nvcr.io/nvidia/rapidsai/notebooks:25.02-cuda12.8-py3.12
This is often the most convenient way to try out existing software environments, but is often not the most performant due to the way container images are unpacked.
Coiled Software Environments#
You can also created Coiled software environments ahead of time. These environments are built and cached on the cloud and can be pulled onto new VMs very quickly.
You can create a RAPIDS software environment using a conda environment.yaml file or a pip requirements.txt file.
Conda example#
Create an environment file containing the RAPIDS packages
# rapids-environment.yaml name: rapidsai-notebooks channels: - rapidsai - conda-forge - nvidia dependencies: # RAPIDS packages - rapids=25.02 - python=3.12 - cuda-version>=12.0,<=12.5 # (optional) Jupyter packages, necessary for Coiled Notebooks and Dask clusters with Jupyter enabled - jupyterlab - jupyterlab-nvdashboard - dask-labextension
coiled env create --name rapids --gpu-enabled --conda rapids-environment.yaml
Then you can specify this software environment when starting new Coiled resources.
coiled notebook start --gpu --software rapidsai-notebooks
CLI Jobs#
You can execute a script in a container on an ephemeral VM with Coiled CLI Jobs.
coiled run python my_code.py # Boots a VM on the cloud, runs the scripts, then shuts down again
We can use this to run GPU code on a remote environment using the RAPIDS container. You can set the coiled CLI to keep the VM around for a few minutes after execution is complete just in case you want to run it again and reuse the same hardware.
$ coiled run --gpu --name rapids-demo --keepalive 5m --container nvcr.io/nvidia/rapidsai/base:25.02-cuda12.8-py3.12 -- python my_code.py ...
This works very nicely when paired with the cudf.pandas CLI tool. For example we can run python -m cudf.pandas my_script to GPU accelerate our Pandas code without having to rewrite anything. For example this script processes some open NYC parking data. With pandas it takes around a minute, but with cudf.pandas it only takes a few seconds.
$ coiled run --gpu --name rapids-demo --keepalive 5m --container nvcr.io/nvidia/rapidsai/base:25.02-cuda12.8-py3.12 -- python -m cudf.pandas cudf_pandas_coiled_demo.py Output ------ This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.download.nvidia.com/licenses/NVIDIA_Deep_Learning_Container_License.pdf Calculate violations by state took: 3.470 seconds Calculate violations by vehicle type took: 0.145 seconds Calculate violations by day of week took: 1.238 seconds
Notebooks#
To start an interactive Jupyter notebook session with Coiled Notebooks run the RAPIDS notebook container via the notebook service.
coiled notebook start --gpu --container nvcr.io/nvidia/rapidsai/notebooks:25.02-cuda12.8-py3.12
Note that the --gpu flag will automatically select a g4dn.xlarge instance with a T4 GPU on AWS. You could additionally add the --vm-type flag to explicitly choose another machine type with different GPU configuration. For example to choose a machine with 4 L4 GPUs you would run the following.
coiled notebook start --gpu --vm-type g6.24xlarge --container nvcr.io/nvidia/rapidsai/notebooks:24.12-cuda12.5-py3.12
Dask Clusters#
Coiled’s managed Dask clusters can also provision clusters using dask-cuda to enable using RAPIDS in a distributed way.
cluster = coiled.Cluster(
container="nvcr.io/nvidia/rapidsai/notebooks:25.02-cuda12.8-py3.12", # specify the software env to use
jupyter=True, # run Jupyter server on scheduler
scheduler_gpu=True, # add GPU to scheduler
n_workers=4,
worker_gpu=1, # single T4 per worker
worker_class="dask_cuda.CUDAWorker", # recommended
)
Once the cluster has started you can also get the Jupyter URL and navigate to Jupyter Lab running on the Dask Scheduler node.
>>> print(cluster.jupyter_link)
https://cluster-abc123.dask.host/jupyter/lab?token=dddeeefff444555666
We can run !nvidia-smi in our notebook to see information on the GPU available to Jupyter.
We can also connect a Dask client to see that information for the workers too.
from dask.distributed import Client
client = Client()
client
From this Jupyter session we can see that our notebook server has a GPU and we can connect to the Dask cluster with no configuration and see all the Dask Workers have GPUs too.