Dataparallel training (cpu, single/multi-gpu)

By design, Catalyst tries to use all available GPUs on your machine. Nevertheless, thanks to Nvidia CUDA design, it’s easy to control GPUs visibility with CUDA_VISIBLE_DEVICES flag.

CPU training

If you don’t want to use GPUs at all you could set CUDA_VISIBLE_DEVICES="".

In this case, do the following before your experiment code:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""

Single GPU training

If you would like to use only one specific GPU during your experiments…

For Notebook API case, do the following before your experiment code:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # or "1", "2" - index of the GPU

The same case for Config API:

CUDA_VISIBLE_DEVICES="0" catalyst-dl run -C=/path/to/configs

Multi GPU training

Multi GPU case is quite similar with Single GPU one.

For Notebook API case, do the following before your experiment code:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1" # or "1,2,3"

The same case for Config API:

CUDA_VISIBLE_DEVICES="0,1" catalyst-dl run -C=/path/to/configs

Nvidia SMI

Rather than use GPU indexing, you could also pass their UUID to the CUDA_VISIBLE_DEVICES. To list them, do the following (with example output from my server):

nvidia-smi -L
>>> GPU 0: GeForce GTX 1080 Ti (UUID: GPU-62b307fa-ef1b-c0a8-0bb4-7311cce714a8)
>>> GPU 1: GeForce GTX 1080 Ti (UUID: GPU-2c0d0e85-119e-a260-aed1-49071fc502bc)
>>> GPU 2: GeForce GTX 1080 Ti (UUID: GPU-7269b4ac-2190-762c-dc34-fa144b1751f9)
# Here we could see GPU indices and their UUIDs.

With this info, it’s also valid to specify GPU by their UUIDs:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "GPU-62b307fa-ef1b-c0a8-0bb4-7311cce714a8"

If you haven’t found the answer for your question, feel free to join our slack for the discussion.