Running Applications over GPUs

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Overview

The platform supports accelerated code execution over NVIDIA graphics processing units (GPUs):

You can use node selection to specify nodes for GPUs. Use labels to identify the nodes as described in Node Selection.
You can run Nuclio serverless functions on GPUs.
You can run GPU applications that use one of the following supported GPU libraries from a platform Jupyter Notebook service with the GPU flavor:
- Horovod
- RAPIDS

Horovod

The platform has a default (pre-deployed) shared single-instance tenant-wide Kubeflow MPI Operator service (mpi-operator), which facilitates Uber's Horovod distributed deep-learning framework. Horovod, which is already preinstalled as part of the platform's Jupyter Notebook service, is widely used for creating machine-learning models that are trained simultaneously over multiple GPUs or CPUs.

You can use Horovod to convert a single-GPU TensorFlow, Keras, or PyTorch model-training program to a distributed multi-GPU program. The objective is to speed up your model training with minimal changes to your existing single-GPU code and without complicating the execution. Note that you can also run Horovod code over CPUs with just minor modification. For an example of using Horovod on the platform, see the image-classification-with-distributed-training demo.

Note

To run Horovod code, ensure that the mpi-operator platform service is enabled. (This service is enabled by default.)
Horovod applications allocate GPUs dynamically from among the available GPUs in the system; they don't use the GPU resources of the parent Jupyter Notebook service. See also the Jupyter GPU resources note.

RAPIDS

You can use NVIDIA's RAPIDS open-source libraries suite to execute end-to-end data science and analytics pipelines entirely on GPUs.

To use the cuDF and cuML RAPIDS libraries, you need to create a RAPIDS Conda environment. For example, you can run the following command from a Jupyter notebook or terminal to create a RAPIDS Conda environment named rapids:

conda create -n rapids -c rapidsai -c nvidia -c anaconda -c conda-forge -c defaults ipykernel rapids=0.17 python=3.7) cudatoolkit=11.0

For more information about using Conda to create Python virtual environments, see the platform' virtual-env.ipynb tutorial Jupyter notebook.

For a comparison of performance benchmarks using the cuDF RAPIDS GPU DataFrame library and pandas DataFrames, see the gpu-cudf-vs-pd.ipynb tutorial notebook.