Example for benchmarking ML worloads using Torch Profiler and NSight #322
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…s/awsome-distributed-training into llama2-fsdp-benchmark
perifaws
requested changes
May 13, 2024
| ## Plenty of EFA level variables | ||
| ## Comment out for non-efa instances (G4d, P3) | ||
| ## For G5.12x, Comment out RDMA and Fork safe | ||
| ## For G4dn and other G5, comment out all |
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You may want to have sections for clarity. Otherwise it can be confusing as there's a list of variables some of which are commented but only 1 explanation on the top
| export FI_PROVIDER=efa | ||
| export NCCL_DEBUG=INFO | ||
| export TORCH_NCCL_ASYNC_ERROR_HANDLING=1 | ||
| #export NCCL_SOCKET_IFNAME=ens |
| export FI_PROVIDER=efa | ||
| export NCCL_DEBUG=INFO | ||
| export TORCH_NCCL_ASYNC_ERROR_HANDLING=1 | ||
| #export NCCL_SOCKET_IFNAME=ens |
| export NCCL_DEBUG=INFO | ||
| export TORCH_NCCL_ASYNC_ERROR_HANDLING=1 | ||
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| #export NCCL_SOCKET_IFNAME=ens |
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split in sections or remove
| conda activate llamapretrain | ||
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| # Install pytorch and other dependencies | ||
| conda install -y pytorch==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia |
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put the versions at the top w/ the mamba version
| --output /fsx/nsys_profiles2/llama2/report_llama2_job%q{SLURM_JOB_ID}_rank%q{SLURM_PROCID}_on_%q{HOSTNAME}.nsys-rep \ | ||
| torchrun "${TORCHRUN_ARGS[@]}" $TRAIN_SCRIPT "${MODEL_ARGS[@]}" | ||
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| #srun -u -l "${ENROOT_ARGS[@]}" /usr/local/cuda/bin/nsys profile -w true -t cuda,nvtx,osrt,cudnn,cublas \ |
mhuguesaws
reviewed
May 15, 2024
| ## For G4dn and other G5, comment out all | ||
| export FI_EFA_USE_DEVICE_RDMA=1 # use for p4d | ||
| export FI_EFA_FORK_SAFE=1 | ||
| export FI_LOG_LEVEL=1 |
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Suggested change
| export FI_LOG_LEVEL=1 | |
| export FI_LOG_LEVEL=warn |
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This recipe is built on meta's llama recipe with modifications to allow for model pretraining (LLAMA2) on FSDP with an additional ability to profile the workloads using either Torch Profiler or NSight.
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