Introduction

This is a brief document on the usage of the A100 GPU nodes on Atlas. The A100 GPUs installed in Atlas are the 80GB Memory versions. There are 5 A100 nodes, each housing 8 A100 GPUs. 4 of these nodes have their A100’s in a MIG (Multi-Instance GPU) mode. This means that each A100 is partitioned into 7 individual GPU instances, with 10GB of memory attached to each instance. That gives each of these nodes 56 GPU instances. One of the nodes does not have its A100s in a MIG configuration, so it will offer all 8 A100s in their non-MIG form. All A100 GPUs are located in the gpu-a100 partiton.

Allocation of a MIG instance

There are 224 total MIG instances between the 4 nodes with A100’s in a MIG configuration. The name of each instance is a100_1g.10gb. In Slurm, GPUs are a type of Generic Resource, or gres in your submission scripts. To allocate a single A100 instance, you would request a gres with that resource type and name.

 #SBATCH --gres=gpu:a100_1g.10gb:1
	

This would allocate 1 of the MIG instances. The format of the Gres allocation section is:

  --gres=(type):(name-of-gres):(Number-requested)
	

General GPU allocation

There is one type of Gres installed in Atlas; that is the GPU type, but there are three different names for these resources, depending on which GPU you intend to use.

v100 -> These are the existing V100 cards in the "gpu" partition.  

a100 -> These are the A100 GPUs (E.G., --gres:gpu:a100:1) 

a100_1g.10gb -> This is the name of 1 A100 MIG instance
	

Here is an example of an interactive allocation of 1 A100 MIG instance, utilizing 2 processor cores, with a time limit of 6 hours:

[joey.jones@atlas-login-2 ~]$ salloc -p gpu-a100 -n 2 --gres=gpu:a100_1g.10gb:1 -A admin -t 6:00:00
salloc: Pending job allocation 12163950
salloc: job 12163950 queued and waiting for resources
salloc: job 12163950 has been allocated resources
salloc: Granted job allocation 12163950
salloc: Waiting for resource configuration
salloc: Nodes atlas-0241 are read for job
[joey.jones@atlas-login-2 ~]$ srun hostname
atlas-0241
atlas-0241
	

Using Containers with the A100

NVIDIA provides a number of containers that are optimized for the A100 hardware. It is possible that requested software is a package that is available through the NVIDIA container repository. The following link contains a repository of available containers. Requests for containers to be made available can be submitted to the helpdesk: scinet_vrsc@usda.gov

Nvidia Container Repository

As an example, pytorch is one of the optimized software packages that NVIDIA distributes. That container was installed on the A100 application tree under /apps/containers/ and can be accessed as follows.

module load apptainer/1.0.2
apptainer exec --nv /apps/containers/pytorch/pytorch-23.04.sif python3
	

After these commands, a python prompt will be available with access to pytorch:

>>>import torch
>>>torch.cuda.is_available()
True
>>>
	

It is also possible to run these in a script or batch method:

$cat myscript.py
import torch
print(torch.cuda.is_available())

$module load apptainer/1.0.2
$apptainer exec --nv /apps/containers/pytorch/pytorch-23.04.sif python3 myscript.py
True
	

And as an extension, this could be done via slurm's batch method, using the same python script:

$cat sbatch.test
#!/bin/bash -l
#SBATCH -J Container-GPU
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -p gpu-a100
#SBATCH --gres=gpu:a100_1g.10gb:1
#SBATCH -t 4:00:00
#SBATCH -A Admin

module purge
module load apptainer/1.0.2
srun apptainer exec --nv /apps/containers/pytorch/pytorch-23.04.sif python3 myscript.py

$ cat slurm-12169583.out
13:4: not a valid test operator: (
13:4: not a valid test operator: 525.105.17
True
	

Note that utilizing containers in the MIG subsections on the A100 Nodes is largely experimental, and we are monitoring and updating with patches from the associated vendors whenever possible.

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