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$ salloc -p gpu --nodes=1 --gres=gpu:1 -A yourProjectName-gpu --time=00:20:00
salloc: Granted job allocation 4360927
salloc: Waiting for resource configuration
salloc: Nodes nid002828 are ready for job

$ module load tensorflow/rocm5.6-tf2.12

$ module list
Currently Loaded Modules:
1) craype-x86-milan 7) pawsey 13) cray-libsci/23.09.1.1
2) libfabric/1.15.2.0 8) pawseytools 14) PrgEnv-gnu/8.4.0
3) craype-network-ofi 9) gcc/12.2.0 15) singularity/4.1.0-mpi
4) perftools-base/23.09.0 10) craype/2.7.23 16) tensorflow/rocm5.6-tf2.12
5) xpmem/2.8.4-1.0_7.3__ga37cbd9.shasta 11) cray-dsmml/0.2.2
6) pawseyenv/2024.05 12) cray-mpich/8.1.27

$ python3 01_horovod_mnist.py 
2023-09-07 14:32:18.907641: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
INFO:root:This is process with rank 0 and local rank 0
INFO:root:This is process with rank 0 and local rank 0: gpus available are: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
2023-09-07 14:32:23.886297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 134200961 MB memory:  -> device: 0, name: AMD Instinct MI250X, pci bus id: 0000:d1:00.0.0
[...]Epoch 1/401875/1875 [==============================] - 7s 1ms/step - loss: 0.3005 - accuracy: 0.9133
Epoch 2/40
1875/1875 [==============================] - 2s 1ms/step - loss: 0.1417 - accuracy: 0.9575
Epoch 3/40
1875/1875 [==============================] - 2s 1ms/step - loss: 0.1066 - accuracy: 0.9681
[...]
Epoch 39/40
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0191 - accuracy: 0.9935
Epoch 40/40
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0186 - accuracy: 0.9938
[...]
INFO:root:This is process with rank 0 and local rank 0: my prediction is [[ -3.5764134  -6.1231604  -1.5476028   2.1744065 -14.56255    -5.4938045
  -20.374353   12.388017   -3.1701622  -1.0773858]]

#!/bin/bash --login
#SBATCH --job-name=distributed_tf
#SBATCH --partition=gpu
#SBATCH --nodes=2              #2 nodes in this example 
#SBATCH --exclusive            #All resources of the node are exclusive to this job
#                              #8 GPUs per node (16 "allocation packs" in total for the job)
#SBATCH --time=00:05:00
#SBATCH --account=pawsey12345-gpu #IMPORTANT: use your own project and the -gpu suffix

#----
#Loading needed modules:
export ROCM_PATH=/opt/rocm #Workaround for path errors with new CPE. Will be removed after container fix.
module load tensorflow/<version>
echo -e "\n\n#------------------------#"
module list

#----
#Printing the status of the given allocation
echo -e "\n\n#------------------------#"
echo "Printing from scontrol:"
scontrol show job ${SLURM_JOBID}

#----
#If additional python packages have been installed in user's own virtual environment
VENV_PATH=$MYSOFTWARE/manual/software/pythonEnvironments/tensorflowContainer-environments/myenv

#----
#Clear definition of the python script containing the tensorflow training case
PYTHON_SCRIPT=$MYSRATCH/matilda-machinelearning/models/01_horovod_mnist.py

#----
#TensorFlow settings if needed:
#  The following two variables control the real number of threads in Tensorflow code:
export TF_NUM_INTEROP_THREADS=1    #Number of threads for independent operations
export TF_NUM_INTRAOP_THREADS=1    #Number of threads within individual operations 

#----
#Execution
#Note: srun needs the explicit indication full parameters for use of resources in the job step.
#      These are independent from the allocation parameters (which are not inherited by srun)
#      Each task needs access to all the 8 available GPUs in the node where it's running.
#      So, no optimal binding can be provided by the scheduler.
#      Therefore, "--gpus-per-task" and "--gpu-bind" are not used.
#      Optimal use of resources is now responsability of the code.
#      "-c 8" is used to force allocation of 1 task per CPU chiplet. Then, the REAL number of threads
#         for the code SHOULD be defined by the environment variables above.
echo -e "\n\n#------------------------#"
echo "Code execution:"
#When using a virtual environment:
srun -N 2 -n 16 -c 8 --gres=gpu:8 bash -c "source $VENV_PATH/bin/activate &&  python3 $PYTHON_SCRIPT"
#When no virtual environement is needed:
#srun -N 2 -n 16 -c 8 --gres=gpu:8 python3 $PYTHON_SCRIPT

#----
#Printing information of finished job steps:
echo -e "\n\n#------------------------#"
echo "Printing information of finished jobs steps using sacct:"
sacct -j ${SLURM_JOBID} -o jobid%20,Start%20,elapsed%20

#----
#Done
echo -e "\n\n#------------------------#"
echo "Done"