Use GPU Jupyter Images¶

This section will guide you through creating a new notebook server using one of our GPU-enabled images.

Overview

What You’ll Need
Procedure
Verify
Summary
What’s Next

What You’ll Need ¶

An Arrikto EKF or MiniKF deployment.

Procedure ¶

Create a new notebook server.
1. Use the following Kale Docker image:
  
  gcr.io/arrikto/jupyter-kale-gpu-py38:<IMAGE_TAG>
  
  Note
  
  The <IMAGE_TAG> varies based on the MiniKF or EKF release.
  
  This image comes with the CUDA toolkit pre-installed.
2. Enter the number of GPU devices you need in the GPUs section of the Jupyter Web App:

Create a new notebook server.
1. Use the following Kale Docker image:
  
  gcr.io/arrikto/jupyter-kale-gpu-tf-py38:<IMAGE_TAG>
  
  Note
  
  The <IMAGE_TAG> varies based on the MiniKF or EKF release.
  
  This image is using the GPU generic one as its base image, adding the following libraries:
  - cuDNN
  - cuBLAS
  - cuFFT
  - cuSPARSE
  - cuRAND
  - cuSOLVER
  - NVRTC
2. Enter the number of GPU devices you need in the GPUs section of the Jupyter Web App:

Create a new notebook server.
1. Use any of the Jupyter Kale images. For example:
  
  gcr.io/arrikto/jupyter-kale-py38:<IMAGE_TAG>
  
  Note
  
  The <IMAGE_TAG> varies based on the MiniKF or EKF release.
2. Enter the number of GPU devices you need in the GPUs section of the Jupyter Web App:
Start a new terminal inside the notebook server.
Install the CUDA version of PyTorch:

jovyan@gpu-0:~$ pip3 install torch==1.10.0+cu113 \ > -f https://download.pytorch.org/whl/cu113/torch_stable.html

PyTorch bundles all the CUDA libraries as part of its PyPI package, so no system-level libraries are required.

Note

Please head to the PyTorch website for the latest releases.

Verify ¶

Start a new terminal inside your notebook server.
Verify that the notebook can consume the GPU devices you requested:

jovyan@gpu-0:~$ nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 460.73.01 Driver Version: 460.73.01 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 | | N/A 33C P8 26W / 149W | 0MiB / 11441MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+

You should get an output similar to the above.

Start a new notebook (IPYNB file) from inside your notebook server.
Verify that TensorFlow can consume the GPU devices:

import tensorflow as tf tf.config.list_physical_devices('GPU')

You should see an output similar to:

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Start a new notebook (IPYNB file) from inside your notebook server.
Verify that PyTorch can consume the GPU devices:

import torch torch.cuda.is_available()

Which should return True.

Summary ¶

You have successfully launched a notebook server using one of our GPU-enabled images.

What’s Next ¶

Check out the rest of the Kale user guides.

Kale