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scripts/distributed/torch-distributed-gpu-test.py

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+#!/usr/bin/env python
+
+#
+# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
+# many nodes) can talk to each other via nccl and allocate gpu memory.
+#
+# To run first adjust the number of processes and nodes:
+#
+# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
+#
+# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
+#
+# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
+#
+# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
+#
+# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
+#
+# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
+#
+# which should tell you what's going on behind the scenes.
+#
+#
+# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
+# runs on 2 nodes of 4 gpus per node:
+#
+# #SBATCH --job-name=test-nodes        # name
+# #SBATCH --nodes=2                    # nodes
+# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+# #SBATCH --cpus-per-task=10           # number of cores per tasks
+# #SBATCH --gres=gpu:4                 # number of gpus
+# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
+# #SBATCH --output=%x-%j.out           # output file name
+#
+# GPUS_PER_NODE=4
+# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+# MASTER_PORT=6000
+#
+# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
+# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
+# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
+# torch-distributed-gpu-test.py'
+#
+
+import fcntl
+import os
+import socket
+
+import torch
+import torch.distributed as dist
+
+
+def printflock(*msgs):
+    """solves multi-process interleaved print problem"""
+    with open(__file__, "r") as fh:
+        fcntl.flock(fh, fcntl.LOCK_EX)
+        try:
+            print(*msgs)
+        finally:
+            fcntl.flock(fh, fcntl.LOCK_UN)
+
+
+local_rank = int(os.environ["LOCAL_RANK"])
+torch.cuda.set_device(local_rank)
+device = torch.device("cuda", local_rank)
+hostname = socket.gethostname()
+
+gpu = f"[{hostname}-{local_rank}]"
+
+try:
+    # test distributed
+    dist.init_process_group("nccl")
+    dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
+    dist.barrier()
+
+    # test cuda is available and can allocate memory
+    torch.cuda.is_available()
+    torch.ones(1).cuda(local_rank)
+
+    # global rank
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+
+    printflock(f"{gpu} is OK (global rank: {rank}/{world_size})")
+
+    dist.barrier()
+    if rank == 0:
+        printflock(f"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")
+
+except Exception:
+    printflock(f"{gpu} is broken")
+    raise