Installing TensorFlow

Setting up a GPU-accelerated environment can be challenging due to driver dependencies, version conflicts, and other complexities. Apptainer simplifies this process by encapsulating all these details

Apptainer Using NGC Containers (Our #1 Recommendation)

There are multiple ways to install and run TensorFlow. Our recommended approach is via NGC containers. The containers are available via NGC Registry. In this example we will pull TensorFlow NGC container

1. Build the container:

apptainer build tensorflow-24.03-tf2-py3.simg docker://nvcr.io/nvidia/tensorflow:24.03-tf2-py3

This will take some time, and once it completes you should see a .simg file.

For your convenience, the pre-built container images are located in directory:

/oscar/runtime/software/external/ngc-containers/tensorflow.d/x86_64/

You can choose either to build your own or use one of the pre-downloaded images.

Working with Apptainer images requires lots of storage space. By default Apptainer will use ~/.apptainer as a cache directory which can cause you to go over your Home quota.

export APPTAINER_CACHEDIR=/tmp
export APPTAINER_TMPDIR=/tmp

1. Once the container is ready, request an interactive session with a GPU

interact -q gpu -g 1 -f ampere -m 20g -n 4

1. Run a container wih GPU support

export APPTAINER_BINDPATH="/oscar/home/$USER,/oscar/scratch/$USER,/oscar/data"
# Run a container with GPU support
apptainer run --nv tensorflow-24.03-tf2-py3.simg

the --nv flag is important. As it enables the NVIDA sub-system

1. Or, if you're executing a specific command inside the container:

# Execute a command inside the container with GPU support
$ apptainer exec --nv tensorflow-24.03-tf2-py3.simg nvidia-smi

1. Make sure your Tensorflow image is able to detect GPUs

$ python
>>> import tensorflow as tf
>>> tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)
True

1. If you need to install more custom packages, the containers itself are non-writable but we can use the --user flag to install packages inside .local Example:

Apptainer> pip install <package-name> --user

Slurm Script:

Here is how you can submit a SLURM job script by using the srun command to run your container. Here is a basic example:

#!/bin/bash
#SBATCH --nodes=1               # node count
#SBATCH -p gpu --gres=gpu:1     # number of gpus per node
#SBATCH --ntasks-per-node=1     # total number of tasks across all nodes
#SBATCH --cpus-per-task=1       # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --mem=40G               # total memory (4 GB per cpu-core is default)
#SBATCH -t 01:00:00             # total run time limit (HH:MM:SS)
#SBATCH --mail-type=begin       # send email when job begins
#SBATCH --mail-type=end         # send email when job ends
#SBATCH --mail-user=<USERID>@brown.edu

module purge
unset LD_LIBRARY_PATH
export APPTAINER_BINDPATH="/oscar/home/$USER,/oscar/scratch/$USER,/oscar/data"
srun apptainer exec --nv tensorflow-24.03-tf2-py3.simg python examples/tensorflow_examples/models/dcgan/dcgan.py