Vertex AI#
RAPIDS can be deployed on Vertex AI Workbench.
Create a new Notebook Instance#
From the Google Cloud UI, navigate to Vertex AI -> Notebook -> Workbench
Select Instances and select + CREATE NEW.
In the Details section give the instance a name.
Check the “Attach 1 NVIDIA T4 GPU” option.
After customizing any other aspects of the machine you wish, click CREATE.
Tip
If you want to select a different GPU or select other hardware options you can select “Advanced Options” at the bottom and then make changes in the “Machine type” section.
Install RAPIDS#
Once the instance has started select OPEN JUPYTER LAB and at the top of a notebook install the RAPIDS libraries you wish to use.
Warning
Installing RAPIDS via pip in the default environment is not currently possible, for now you must create a new conda environment.
Vertex AI currently ships with CUDA Toolkit 11 system packages as of the Jan 2025 Vertex AI release.
The default Python environment also contains the cupy-cuda12x package. This means it’s not possible to install RAPIDS package like cudf via pip as cudf-cu12 will conflict with the CUDA Toolkit version but cudf-cu11 will conflict with the cupy version.
You can find out your current system CUDA Toolkit version by running ls -ld /usr/local/cuda*.
You can create a new RAPIDS conda environment and register it with ipykernel for use in Jupyter Lab. Open a new terminal in Jupyter and run the following commands.
# Create a new environment
conda create -y -n rapids \
-c rapidsai -c conda-forge -c nvidia \
rapids=25.02 python=3.12 cuda-version=12.8 \
ipykernel
# Activate the environment
conda activate rapids
# Register the environment with Jupyter
python -m ipykernel install --prefix "${DL_ANACONDA_HOME}/envs/rapids" --name rapids --display-name rapids
Then refresh the Jupyter Lab page and open the launcher. You will see a new “rapids” kernel available.
Tip
If you don’t see the new kernel wait a minute and refresh the page again, it can take a little while to show up.
Test RAPIDS#
You should now be able to open a notebook and use RAPIDS.
For example we could import and use RAPIDS libraries like cudf.
In [1]: import cudf
In [2]: df = cudf.datasets.timeseries()
In [3]: df.head()
Out[3]:
id name x y
timestamp
2000-01-01 00:00:00 1020 Kevin 0.091536 0.664482
2000-01-01 00:00:01 974 Frank 0.683788 -0.467281
2000-01-01 00:00:02 1000 Charlie 0.419740 -0.796866
2000-01-01 00:00:03 1019 Edith 0.488411 0.731661
2000-01-01 00:00:04 998 Quinn 0.651381 -0.525398