GPUs on QUEST

What GPUs are available on QUEST?

There are 18 GPU nodes available to the Quest General Access allocations. These nodes run CUDA version 10.1 and Driver Version 418.39:

• 8 12GB Tesla K40 (two GPUs on each node) (CPU info: 24 cores per node, 128 GB RAM)
• 40 12GB Tesla K80 (four GPUs on each node) (CPU info: 28 cores per node, 128 GB RAM)
• 2 16GB Tesla P100 (two GPUs on each node) (CPU info: 28 cores per node, 128 GB RAM)
• 2 16GB Tesla V100 (two GPUs on each node) (CPU info: 28 cores per node, 192 GB RAM)

Using General Access GPUs

The maximum run time is 48 hours for a job on these nodes. To submit jobs to general access GPU nodes, you should set gengpu as the partition and state the number of GPUs in your job submission command or script. You can also identify the type of GPU you want in your job submission. For instance to request one P100 GPU, you should add the following lines in your job submission script:

#SBATCH -A <allocationID>
#SBATCH -p gengpu
#SBATCH --gres=gpu:p100:1
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -t 1:00:00
#SBATCH --mem=XXG

Note that the memory you request here is for CPU memory. You are automatically given access to the entire memory of the GPU, but you will also need CPU memory as you will be copying memory from the CPU to the GPU.

To schedule another type of GPU, e.g. V100, you should change the p100 designation to the other GPU type, e.g. v100.

Using Genomics Compute Cluster GPUs

#SBATCH -A b1042
#SBATCH -p genomics-gpu
#SBATCH --gres=gpu:a100:1
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -t 1:00:00
#SBATCH --mem=XXG

Note that the memory you request here is for CPU memory. You are automatically given access to the entire memory of the GPU, but you will also need CPU memory as you will be copying memory from the CPU to the GPU.

Interactive GPU jobs

What GPU software is available on QUEST?

CUDA

To see which versions of CUDA are available on Quest, run the command:

module spider cuda

We recommend loading the  CUDA module appropriate for the GPU you're using, listed at the top of this page.

Anaconda

We strongly encourage people to use anaconda to create virtual environments in order to use software that utilize GPUs, especially when using Python. Please see Using Python on QUEST for more information on anaconda virtual environments. These instructions will create a local anaconda virtual environment that will install the TensorFlow-gpu package. Please run the command that come after the $.

1. Load anaconda on QUEST

$ module load python-anaconda3

2. Create a virtual environment and install TensorFlow and Python into it. We are going to name our environment tensorflow-py37. On QUEST, by default, all anaconda environments go into a folder in your HOME directory
called ~/.conda/envs/. Therefore, once these steps are completed, all of the necessary packages will live in a folder whose PATH is ~/.conda/envs/tensorflow-py37

$ conda create --name tensorflow-py37 -c anaconda tensorflow-gpu python=3.7 --yes

3. Install keras into the virtual environment

$ conda install --name tensorflow-py37 -c conda-forge keras --yes

4. Activate virtual environment