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Migrating From SLURM

SLURM-optimized parallel jobs will not under PBS out of the box. Conversion to PBS standards is necessary. Here we provide hints on how to proceed.

It is important to notice that mpirun is used here as an alternative to the srun in SLURM. The -n flag is used to regulate the number of tasks spawned by the MPI. The path to the script being run by MPI must be absolute. The script rights should be set to allow execution and reading.

The PBS provides some useful variables that may be used in the jobscripts PBS_O_WORKDIR and PBS_JOBID. For example:

The PBS_O_WORKDIR returns the directory, where the qsub command was submitted. The PBS_JOBID returns the numerical identifier of the job. The qsub always starts execution in the $HOME directory.

Migrating PyTorch From SLURM

The Intel MPI provides some useful variables that may be used in the scripts executed via the MPI. these include PMI_RANK,PMI_SIZE and MPI_LOCALRANKID.

  • The PMI_RANK and MPI_LOCALRANKID returns the process rank within the MPI_COMM_WORLD communicator - the process number
  • The PMI_SIZE returns the process rank within the MPI_COMM_WORLD communicator - the number of processes

For example:

$ mpirun -n 4 /bin/bash -c 'echo $PMI_SIZE'
4
4
4
4

In typical multi-gpu multi-node setting using PyTorch one needs to know:

  • World-size - i.e. the total number of GPUs in the system
  • Rank of a job in the world - i.e. the number of the current GPU in the system
  • Local GPU ID for assignment purposes inside PyTorch/TF

The following example assumes that you use PyTorch and your DistributedDataParallel process is being initialized via init_method pointing to some file.

The required changes are:

  • To get the world size, access the PMI_SIZE variable of the MPI
  • To get the process rank in the world, access PMI_RANK variable of the MPI
  • To get local GPU ID on the node (can be used to manually set CUDA_VISIBLE_DEVICES), access the MPI_LOCALRANKID variable.

Hint

Some jobs may greatly benefit from mixed precision training since the new NVIDIA A100 GPUs have excellent support for it.

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