I was to set up DDP (distributed data parallel) on a DGX A100 but it doesn't work. Whenever I try to run it simply hangs. My code is super simple just spawning 4 processes for 4 gpus (for the sake of debugging I simply destroy the group immediately but it doesn't even reach there):
def find_free_port():
""" https://stackoverflow.com/questions/1365265/on-localhost-how-do-i-pick-a-free-port-number """
import socket
from contextlib import closing
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(('', 0))
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
return str(s.getsockname()[1])
def setup_process(rank, world_size, backend='gloo'):
"""
Initialize the distributed environment (for each process).
gloo: is a collective communications library (https://github.com/facebookincubator/gloo). My understanding is that
it's a library/API for process to communicate/coordinate with each other/master. It's a backend library.
export NCCL_SOCKET_IFNAME=eth0
export NCCL_IB_DISABLE=1
https://stackoverflow.com/questions/61075390/about-pytorch-nccl-error-unhandled-system-error-nccl-version-2-4-8
https://pytorch.org/docs/stable/distributed.html#common-environment-variables
"""
if rank != -1: # -1 rank indicates serial code
print(f'setting up rank={rank} (with world_size={world_size})')
# MASTER_ADDR = 'localhost'
MASTER_ADDR = '127.0.0.1'
MASTER_PORT = find_free_port()
# set up the master's ip address so this child process can coordinate
os.environ['MASTER_ADDR'] = MASTER_ADDR
print(f"{MASTER_ADDR=}")
os.environ['MASTER_PORT'] = MASTER_PORT
print(f"{MASTER_PORT}")
# - use NCCL if you are using gpus: https://pytorch.org/tutorials/intermediate/dist_tuto.html#communication-backends
if torch.cuda.is_available():
# unsure if this is really needed
# os.environ['NCCL_SOCKET_IFNAME'] = 'eth0'
# os.environ['NCCL_IB_DISABLE'] = '1'
backend = 'nccl'
print(f'{backend=}')
# Initializes the default distributed process group, and this will also initialize the distributed package.
dist.init_process_group(backend, rank=rank, world_size=world_size)
# dist.init_process_group(backend, rank=rank, world_size=world_size)
# dist.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)
print(f'--> done setting up rank={rank}')
dist.destroy_process_group()
mp.spawn(setup_process, args=(4,), world_size=4)
why is this hanging?
nvidia-smi output:
$ nvidia-smi
Fri Mar 5 12:47:17 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.102.04 Driver Version: 450.102.04 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 A100-SXM4-40GB On | 00000000:07:00.0 Off | 0 |
| N/A 26C P0 51W / 400W | 0MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 1 A100-SXM4-40GB On | 00000000:0F:00.0 Off | 0 |
| N/A 25C P0 52W / 400W | 3MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 2 A100-SXM4-40GB On | 00000000:47:00.0 Off | 0 |
| N/A 25C P0 51W / 400W | 3MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 3 A100-SXM4-40GB On | 00000000:4E:00.0 Off | 0 |
| N/A 25C P0 51W / 400W | 3MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 4 A100-SXM4-40GB On | 00000000:87:00.0 Off | 0 |
| N/A 30C P0 52W / 400W | 3MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 5 A100-SXM4-40GB On | 00000000:90:00.0 Off | 0 |
| N/A 29C P0 53W / 400W | 0MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 6 A100-SXM4-40GB On | 00000000:B7:00.0 Off | 0 |
| N/A 29C P0 52W / 400W | 0MiB / 40537MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 7 A100-SXM4-40GB On | 00000000:BD:00.0 Off | 0 |
| N/A 48C P0 231W / 400W | 7500MiB / 40537MiB | 99% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 7 N/A N/A 147243 C python 7497MiB |
+-----------------------------------------------------------------------------+
How do I set up ddp in this new machine?
Update
btw I've successfully installed APEX because some other links say to do that but it still fails. For I did:
went to: https://github.com/NVIDIA/apex follwed their instructions
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
but before the above I had to update gcc:
conda install -c psi4 gcc-5
it did install it as I successfully imported it but it didn't help.
Now it actually prints an error msg:
Traceback (most recent call last):
File "/home/miranda9/miniconda3/envs/metalearning/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/home/miranda9/miniconda3/envs/metalearning/lib/python3.8/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/miranda9/miniconda3/envs/metalearning/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
KeyboardInterrupt
Process SpawnProcess-3:
Traceback (most recent call last):
File "/home/miranda9/miniconda3/envs/metalearning/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/home/miranda9/ML4Coq/ml4coq-proj/embeddings_zoo/tree_nns/main_brando.py", line 252, in train
setup_process(rank, world_size=opts.world_size)
File "/home/miranda9/ML4Coq/ml4coq-proj/embeddings_zoo/distributed.py", line 85, in setup_process
dist.init_process_group(backend, rank=rank, world_size=world_size)
File "/home/miranda9/miniconda3/envs/metalearning/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 436, in init_process_group
store, rank, world_size = next(rendezvous_iterator)
File "/home/miranda9/miniconda3/envs/metalearning/lib/python3.8/site-packages/torch/distributed/rendezvous.py", line 179, in _env_rendezvous_handler
store = TCPStore(master_addr, master_port, world_size, start_daemon, timeout)
RuntimeError: connect() timed out.
During handling of the above exception, another exception occurred:
related:
- https://github.com/pytorch/pytorch/issues/9696
- https://discuss.pytorch.org/t/dist-init-process-group-hangs-silently/55347/2
- https://forums.developer.nvidia.com/t/imagenet-hang-on-dgx-1-when-using-multiple-gpus/61919
- apex suggestion: https://discourse.mozilla.org/t/hangs-on-dist-init-process-group-in-distribute-py/44686
- https://github.com/pytorch/pytorch/issues/15638
- https://github.com/pytorch/pytorch/issues/53395
from How to solve dist.init_process_group from hanging (or deadlocks)?
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