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tensorflow-2_distribution-strategies_playground's Introduction

How to run training using SLURM

Before running training please adjust checkpoint_dir variable.

  • SA-MIRI's network:
sbatch job_cte_nostrategy.sh --architecture sa_miri --epochs 20 --batch_size 128
sbatch job_cte_mirroredstrategy.sh --architecture sa_miri --epochs 20 --batch_size 128
sbatch job_cte_multiworkermirroredstrategy.sh --architecture sa_miri --epochs 20 --batch_size 128
  • MobileNet:
sbatch job_cte_nostrategy.sh --architecture mobilenet --epochs 5 --batch_size 128
sbatch job_cte_mirroredstrategy.sh --architecture mobilenet --epochs 5 --batch_size 128
sbatch job_cte_multiworkermirroredstrategy.sh --architecture mobilenet --epochs 5 --batch_size 128
  • Resnet101:
sbatch job_cte_nostrategy.sh --architecture resnet101 --epochs 5 --batch_size 64
sbatch job_cte_mirroredstrategy.sh --architecture resnet101 --epochs 5 --batch_size 64
sbatch job_cte_multiworkermirroredstrategy.sh --architecture resnet101 --epochs 5 --batch_size 64

Results

Evaluations made intra-node (using 2-4 GPUs) were using NCCLAllReduce, evaluations made inter-node (using 8+ GPUs) were using RING collective communication.

  • SA-MIRI's network: SA-MIRI's network training speedup
  • MobileNet: MobileNet training speedup
  • Resnet101: Resnet101 training speedup

Future work

It would be interesting to measure speedup for MultiWorkerMirroredStrategy with NCCL set as collective communication mechanism.

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