NVIDIA
[ResNet-50/pytorch] FP32 and AMP Mode taking same time to complete 90 Epochs
I'm trying to benchmark the performance of V100 and A100 GPU's on our cluster. im trying to run 90 epoch and capture time for convergence.
The throughput for FP32 is giving 1400 Images/sec (90 Epochs time: 2776m17.375s) and AMP is 3950 images/sec (90 Epochs time: 2767m31.195s).
The code is pulled from repo: PyTorch/Classification/ConvNets/resnet50v1.5
To Reproduce I have run this code using slurm and py-axis. This script is used to run for FP32 precision.
For running on AMP, i have changed the precision to AMP. although time for convegence for 90 Epochs is not as per submitted?
Further there is no implementation to stop training after reaching MLcomms standard convegence accuracy: 76%.
#!/bin/bash #SBATCH --job-name=pytorch_4GPU #SBATCH --nodes=1 #SBATCH --gres=gpu:4 #SBATCH --partition=gpu #SBATCH --reservation=testing #SBATCH --time=3-00:00:00 #SBATCH --nodelist=gpu035 #SBATCH --exclusive #SBATCH --output=pytorch_gpu_4_%j.out #SBATCH --error=pytorch_gpu_4_%j.err
time srun --container-image=/scratch/cdacapp/enroot/nvidia+pytorch+21.03-py3.sqsh
--container-name=pytorch
--container-mounts=/var/share/slurm/slurm.taskprolog:/var/share/slurm/slurm.taskprolog,/scratch/cdacapp:/scratch/cdacapp
sh -c 'cd /scratch/cdacapp/pytorch/DeepLearningExamples/PyTorch/Classification/ConvNets && python ./multiproc.py
--nproc_per_node 4 --nnodes 1 ./launch.py --model resnet50
--precision FP32 --mode convergence --platform DGX1V /scratch/cdacapp/pytorch/image2012
--raport-file benchmark_4GPU.json --epochs 90 --no-checkpoints
--optimizer-batch-size 1024 --batch-size 256 --workers 4 --prefetch 4 --seed 100'
Environment
- Container version : pytorch:21.03-py3
- GPUs in the system : 4x Tesla V100-SXM2-16GB
- CUDA driver version : 470.57.02
`import torch import numpy as np
... your model and data loading code ...
Define early stopping parameters patience = 5 best_accuracy = 0 counter = 0
Training loop for epoch in range(num_epochs): ... training code ...
Validation step
model.eval()
with torch.no_grad():
val_accuracy = validate(model, val_loader) # Implement your validation function
model.train()
Check for improvement
if val_accuracy > best_accuracy:
best_accuracy = val_accuracy
counter = 0
else:
counter += 1
Early stopping condition
if counter >= patience:
print(f'Early stopping at epoch {epoch}')
break
`
Hi @kishoryd, I have one question how much training time does it need to train for 90 epochs?
