Palmetto NVIDIA DGX A100 User Guide

This document provides a quick user guide on using the NVIDIA DGX A100 nodes on the Palmetto cluster.


NVIDIA DGX A100 is a computer system built on NVIDIA A100 GPUs for AI workload. For more details, please check the NVIDIA DGX A100 web Site.

Request a DGX A100 Node

On Palmetto, the DGX A100 nodes are integrated into the PBS resource and workload manager. An authorized user should request a DGX A100 Node using a qsub command in one the following ways:

  • Request a DGX A100 node in the interactive mode

qsub -I -l select=1:ncpus=64:mem=500gb:ngpus=4:gpu_model=a100,walltime=1:00:00 -q dgx_mri

  • Request a DGX A100 node in the batch mode

qsub dgx_test.pbs

The job script dgx_test.pbs is shown as follows:


#PBS -N dgx_t01
#PBS -q dgx_mri
#PBS -l select=1:ncpus=64:mem=900gb:ngpus=8:gpu_model=a100,walltime=1:00:00

# List available GPUs
nvidia-smi -L

# Run a GPU program

# other command lines 

Use Containers on DGX A100 nodes

Containers simplifies the process of building and running HPC and AI software. With a container, the entire software environment is packaged into a single bundle, i.e., an image file.

Among multiple container platforms, singularity has become a preferred choice in the HPC community. Singularity is designed to allow containers to be executed as if they were native programs or scripts on a host system: no daemon is required to build or run containers, and the security model is compatible with shared systems. To learn how to use Singularity, you can consult

NGC (NVIDIA GPU-Accelerated Containers)

When you work on DGX A100, the NVIDIA GPU-Accelerated Containers, or NGC, is an essential resource. NGC offers a comprehensive catalog of GPU-accelerated software for deep learning, machine learning, and HPC. For details, check the NGC website at

NGC provides a private Docker image registry which you can pull container images using docker or singularity. The user guide at describes how to use singularity to work with NGC images.

Common singularity commands

Below are some command commands that you should be familiar with when using singularity.

  • Pull a NGC container image into a singularity image
singularity pull docker://
  • Build a singularity image from a NGC container image
singularity build tensorflow.sif tensorflow_20.08-tf1-py3.sif
  • Interact with a singularity container
# first, pull or build an image
singularity build lolcow.sif docker://godlovedc/lolcow
# start a container with the image in the interactive mode
singularity shell lolcow.sif
~> whoami
~> id
  • Run a command within a singularity container
# run command within a container
# here cowsay is the program and moo is an argument
singularity exec lolcow.sif cowsay moo
  • Run a container that comes with a runscript

A container can have a runscript that is executed when the container is run. See singularity definition file at for how the runscript is defined.

singularity run lolcow.sif

Access host files

When enabled by the system administrator, Singularity allows you to map directories on your host system to directories within your container using bind mounts. This allows you to read and write data on the host system with ease.

A singularity container has its own operating system. When you run a singularity container, the singularity program swaps the host operating system into the one inside the container. As a result, the host file system becomes inaccessible. For you to read and write files on the host system within the container, you need to tell the singularity program to bind directories on the host system into the container. There are two primary methods: system-defined bind paths and user-defined bind paths.

  • System-defined bind paths: The system administrator has the ability to define what bind paths will be included automatically inside each container. In the default configuration, the system default bind points are `$HOME``` ,/sys:/sys,/proc:/proc,/tmp:/tmp,/var/tmp:/var/tmp,/etc/resolv.conf:/etc/resolv.conf,/etc/passwd:/etc/passwd, and$PWD. Here the first path before:` is the path from the host and the second path is the path in the container.

  • User-defined bind paths: The Singularity action commands (run, exec, shell, and instance start) will accept the --bind/-B command-line option to specify bind paths, and will also honor the $SINGULARITY_BIND (or $SINGULARITY_BINDPATH) environment variable.

For example, the following command binds the /scratch2 directory on the host system into the /mnt directory in the container:

singularity exec --bind /scratch2:/mnt my_container.sif ls /mnt

Use NVIDIA GPU inside a singularity container

Singularity commands like run, shell, and exec can take an --nv option, which will setup the container’s environment to use an NVIDIA GPU and the basic CUDA libraries to run a CUDA enabled application.

The --nv flag will:

  • Ensure that the /dev/nvidiaX device entries are available inside the container, so that the GPU cards in the host are accessible.

  • Locate and bind the basic CUDA libraries from the host into the container, so that they are available to the container, and match the kernel GPU driver on the host.

  • Set the LD_LIBRARY_PATH inside the container so that the bound-in version of the CUDA libraries are used by applications run inside the container.

Example of running a tensorflow program using a singularity container

Step 1. Change into a scratch space

mkdir -p /local_scratch/$USER
cd /local_scratch/$USER

Step 2. Build or pull a tensorflow image

singularity pull docker://tensorflow/tensorflow:latest-gpu

Step 3. Run the container with GPU support

singularity run --nv tensorflow_latest-gpu.sif

Step 4. Execute a python script that verify the GPU is available

Singularity> python
>>> from tensorflow.python.client import device_lib
>>> print(device_lib.list_local_devices())
>>> exit()
Singularity> exit

Step 5. Write a python program that use the environment inside the container. Here, we use an existing one that perform image classification.

rsync -av /software/examples/dgx_mri/ ~/

Step 6. Run the program within the container

singularity exec --nv tensorflow_latest-gpu.sif python3 ~/

Example files

The example files are located in the directory /software/examples/dgx_mri on Palmetto. On a DGX A100 node, you can repeat the above procedures by execute:

bash /software/examples/dgx_mri/dgx_test.pbs


qsub /software/examples/dgx_mri/dgx_test.pbs
  • dgx_test.pbs

#PBS -N dgx_t01
#PBS -q dgx_mri
#PBS -l select=1:ncpus=256:mem=900gb:ngpus=8:gpu_model=a100,walltime=1:00:00

# 1. List available GPUs
echo "Test #1: List available GPUs"
nvidia-smi -L

# 2. Run a native CUDA program
echo "Test #2: Run a native CUDA program"

# 3. Run a grogram in a singularity container
echo "Test #3: Run a Tensorflow program within a singularity container"
mkdir -p /local_scratch/$USER/dgx_mri
cd /local_scratch/$USER/dgx_mri
# Pull a docker image to a singularity image file
if [ ! -f tensorflow_latest-gpu.sif ]; then
    singularity pull docker://tensorflow/tensorflow:latest-gpu

# Create a user program
cat <<EOF >$HOME/
from tensorflow.python.client import device_lib


# Run a program within the singularity image
singularity exec --nv tensorflow_latest-gpu.sif /usr/local/bin/python $HOME/

# 4. Use only the first GPU
echo "Test #4: Run a a Tensorflow program within a singularity container using one GPU"
singularity exec --nv tensorflow_latest-gpu.sif /usr/local/bin/python

# 5. Run an image classification example using a NGC PyTorch container
# fetch an NGC container
if [ ! -f tensorflow_20.08-tf1-py3.sif ]; then
    singularity pull docker://
# create a sample program
mkdir -p /scratch2/$USER/mnist
rsync -av /software/examples/dgx_mri/ /scratch2/$USER/mnist/
# run the sample program using the container
# here, we also show how to mount a local directory to a container directory using --bind
singularity exec --nv --bind /scratch2/$USER/mnist:/mnt tensorflow_20.08-tf1-py3.sif python3 /mnt/
import tensorflow as tf
from tensorflow import keras
import numpy as np

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

label_cloth_dict = {0:'T-shirt/top', 1:'Trouser', 2:'Pullover', 3:'Dress', 4:'Coat', 5:'Sandal', 6:'Shirt', 7:'Sneaker', 8:'Bag', 9:'Ankle boot' }
def label_name(x):
    return label_cloth_dict[x]

train_images = train_images / 255.0
test_images = test_images / 255.0

simple_model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),

              metrics=['accuracy']), train_labels, epochs=15)

test_loss, test_acc = simple_model.evaluate(test_images, test_labels)
print(f'test_loss={test_loss} test_accuracy={test_acc}')