nccl_test.yaml

# This measures NCCL all reduce performance with Torch.

# Usage:
#   $ sky launch -c nccl --use-spot nccl_test.yaml 

# Example output
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:1
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:2
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:3
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:4
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:5
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:The average bandwidth of all_reduce with a 4.0GB payload (5 trials, 16 ranks):
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]: algbw: 2.053 GBps (16.4 Gbps)
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]: busbw: 3.850 GBps (30.8 Gbps)
# (head, rank=0, pid=17654) [nccl-ebd1-head-8x3wqw6d-compute:0]:

name: torch-nccl-allreduce

num_nodes: 2

resources:
  accelerators: A100:8
  use_spot: True

setup: |
  pip install torch
  git clone https://github.com/stas00/ml-engineering.git

run: |
  cd ml-engineering/network/benchmarks
  NNODES=`echo "$SKYPILOT_NODE_IPS" | wc -l`
  MASTER_ADDR=`echo "$SKYPILOT_NODE_IPS" | head -n1`
  python -u -m torch.distributed.run \
    --nproc_per_node $SKYPILOT_NUM_GPUS_PER_NODE \
    --nnodes $NNODES \
    --rdzv_endpoint $MASTER_ADDR:8888 \
    --rdzv_backend c10d \
    --max_restarts 0 \
    --role `hostname -s`: \
    --tee 3 \
    all_reduce_bench.py