Working with GPU or CPU in data sciences

Calculations in data sciences run on CPUs and/or GPUs. If you're using tools for or write code in this field, you will have to decide where your calculations are executed. The following information is supposed to help with that decision.

The Guided Data Science Resources is a community-sourced data science repo containing open source learning material.

Platform information

The D-ITET infrastructure managed by ISG uses NVIDIA GPUs and Intel CPUs exclusively. Available platforms are either managed Linux workstations with a single GPU or GPU clusters.

Information about these components can be shown by issuing the following commands in a shell:

• lscpu

• Shows information about the CPUs, most relevantly the number of CPU cores available in the line starting with CPU(s):

• nvidia-smi

• Shows the NVIDIA driver version, the CUDA toolkit version and GPUs with their available memory

NVIDIA CUDA Toolkit

The CUDA toolkit provides a development environment for creating high performance GPU-accelerated applications. It is a necessary software dependency for tools used in GPU computing.

Matching toolkit versions to installed driver

The version of the NVIDIA driver installed on a platform limits the version range of CUDA toolkits working with the driver. The driver version is subject to operating update policies and cannot be changed by a user with normal privileges. It is not uniform on servers an desktop clients.

For your projects to work it is crucial to

• check the driver version with nvidia-smi and

• consult NVIDIA's dependency matrix

• to choose the toolkit version matching the driver installed on the platform you use.

Installing a specific toolkit version with conda

The easiest way to install the CUDA toolkit is by using conda. Available versions can be shown with

conda search cudatoolkit

And the version matching the driver can be installed with the following command in an active environment:

conda install cudatoolkit=10.0

Important reminder about working locally

If you're working locally, meaning on a managed Linux desktop or your private machine, always keep in mind the following:

• The local GPU might not have enough memory for your project

• The CUDA version you're using in your project environment might be too new for the driver installed locally

cuDNN library

The cuDNN library is a GPU-accelerated library of primitives for deep neural networks. It is another dependency for GPU computing. In order to use it NVIDIA asks you to read the Software Level Agreement for the library. The library is registered by ISG to be used for research at D-ITET. If you use the library differently you are obliged to register it yourself.

conda automatically installs this library if it's a dependency of another package installed.

pytorch

pytorch is one of the main open source deep learning platforms in use at the time of writing this page. If you haven't done so already, read this installation example.

A good starting point for further information is the official pytorch documentation.

Testing pytorch

To verify the successful installation of pytorch run the following python code in your python interpreter:

import torch
x = torch.rand(5, 3)
print(x)

The output should be similar to the following:

tensor([[0.4813, 0.8839, 0.1568],
        [0.0485, 0.9338, 0.1582],
        [0.1453, 0.5322, 0.8509],
        [0.2104, 0.4154, 0.9658],
        [0.6050, 0.9571, 0.3570]])

Environment and platform information

The following example shows how to gather information which you can use for example to decide whether to run your code on CPU or GPU:

import torch
import sys
print('__Python VERSION:', sys.version)
print('__pyTorch VERSION:', torch.__version__)
print('__CUDA VERSION')
print('__CUDNN VERSION:', torch.backends.cudnn.version())
print('__Number CUDA Devices:', torch.cuda.device_count())
print('__Devices:')
from subprocess import call
call(["nvidia-smi", "--format=csv", "--query-gpu=index,name,driver_version,memory.total,memory.used,memory.free"])
print('Active CUDA Device: GPU', torch.cuda.current_device())
print ('Available devices ', torch.cuda.device_count())
print ('Current cuda device ', torch.cuda.current_device())

tensorflow

tensorflow is another popular open source platform for machine learning. If you haven't done so already, read this installation example.

Choose from the available tutorials to learn how to use it.

Platform information

The following code prints information about the capabilities of the platform you run your environment on:

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Lines containing device:XLA_ show which CPU/GPU devices are available.

A line containing cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version means the NVIDIA driver installed on the system you run the code is not compatible with the CUDA toolkit installed in the environment you run the code from.

An extensive list of device information can be shown with:

from tensorflow.python.client import device_lib
device_lib.list_local_devices()

The module tf.test contains helpful functions to gather platform information:

• tf.test.is_gpu_available

• tf.test.gpu_device_name

Managing GPU resources

If your code is going to run on a GPU cluster you need to make sure you manage your use of GPU resources and use the following recommended configuration:

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
sess = tf.Session(config=config)