id stringlengths 3 8 | text stringlengths 1 115k |
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st179968 | In theory, yes. As long as you get cards with a higher bandwidth than your Ethernet setup it should result in an improvement. But since NCCL is built for using GPUDirect, I’m not sure if it will work with NCCL out of the box. If it doesn’t, you could try and experiment with IPoIB and fall back to using NCCL’s TCP trans... |
st179969 | Hello.
I’m trying to use “all_reduce_multigpu” function with shared memory (list created by multiprocess module). However, it does not work with the shared list object (only working with a normal list).
I want to use the shared list to get return values from the child processes. Is there any alternative way?
Thank you ... |
st179970 | My Pytorch distributed code (similar to ImageNet example, but without multiprocessing) was working on distributed nodes, using the GLOO backend (Python 3.7, Pytorch 1.0.1). There was some system updates, and now all of a sudden I have intermittent success with the code, with the init_process_group throwing various erro... |
st179971 | Hi!
Can you share how you’re calling init_process_group, which initialization method you’re using, etc? If you get the first error on one machine and the second error on another, it is possible that the second error is caused by the first process crashing.
I’m asking because the read: Bad address error makes me think t... |
st179972 | I’m using the file method, on a parallel file system:
jobid = os.environ['SLURM_JOB_ID']
world_size = int(os.environ['SLURM_NTASKS'])
rank = int(os.environ['SLURM_PROCID'])
dist.init_process_group(backend='gloo', init_method='file:///scratch/gpfs/me/main_'+jobid+'.txt',
worl... |
st179973 | When you run this multiple times, do those runs use the same SLURM_JOB_ID? PyTorch makes an attempt to remove the file that is used for initialization at exit, but if any of the processes crashes it may stick around, and cause problems. You can “fix” this by force removing the file before starting a new run. |
st179974 | No, the SLURM system gives you a unique SLURM_JOB_ID for each run that you do (which is why I’m using it, to ensure the file is unique for each run). |
st179975 | I noticed the note on fcntl, is there some test I should run on the parallel file system to ensure there are no issues with correct locking? I think GPFS should be fine, but perhaps theres some edge case, and a newer driver or something caused things to mess up.
I was able to try out NCCL, and this appears to be workin... |
st179976 | churchillmic:
No, the SLURM system gives you a unique SLURM_JOB_ID for each run that you do (which is why I’m using it, to ensure the file is unique for each run).
Thanks, that rules out clobbering the same file from multiple runs.
churchillmic:
I noticed the note on fcntl, is there some test I should run on th... |
st179977 | Hi there,
I’m using GPFS filesystem for file init, and I also got the read(): bad address problem. When I change the file location to local /tmp, it’s fine.
FYI: the mandatory file lock of Gluster and some known issues https://docs.gluster.org/en/v3/Administrator%20Guide/Mandatory%20Locks/ 15 |
st179978 | I try to make data parallelism compatible with model parallelism, but I encounter RuntimeError: all tensors must be on devices[0] during this process. Below is a simplified example of my code (my torch version is 1.0.1.post2):
import torch
import torch.nn as nn
import torch.nn.functional as F
class MyModel(nn.Module)... |
st179979 | There are assumptions in torch.nn.parallel.DistributedDataParallel today that prevent you from doing this unfortunately. We’re working on some changes to DDP to make this possible. Stay tuned.
cc @mrshenli |
st179980 | This should be possible after #19271 17. Here is a tutorial 33 @lyy1994 could you please help to verify if that works for you? |
st179981 | Hello, is there any table where can I check which GPUs are compatible with pyTorch?
For example, geforce gtx/rtx models? |
st179982 | I think pytorch can use on every GPU which can work with CUDA, you may check the website for reference 2 |
st179983 | Yes, but im talking about a Distributed Neural Network. I tried to execute it on Tesla series with < 3.x compute capabilite and it doesnt work. So, I want to make sure if any GPU with > 3.x will work. |
st179984 | PyTorch works with compute cability 3.5 and higher. This is the Tesla Kepler series (K20, K40, K80). |
st179985 | Yes they do. You can check on this website 2 for the compute capability of your GPU and it works, if your GPU has a capability of 3.5 or above. |
st179986 | I’d like to report a weird behaviour in 1.0 that I could resolve by only going back to 0.4. I am training a network on quite a big data set (35 GB) and use 4 gpus by applying the command
torch.nn.DataParallel(model).cuda()
Further I am having a big batch size (>1000) which makes the command
torch.multiprocessing.set_sh... |
st179987 | Looking at the available sharing strategies 38, the file_system one is clearly prone to leaks. If the data loader ends up allocating new shared tensors for every epoch, this would explain your leak. Did you try using the file_descriptor sharing strategy? |
st179988 | file_descriptor is the default setting. I tried it of course but could not use for other reasons. |
st179989 | If you can (have sudo privilege), increase the file descriptor limit of your system and use the file_descriptor sharing strategy. |
st179990 | Hi Masters,
I am trying the following code on 2 nodes with diff num of CPU/GPU devices, running one parameter server (ps) process and diff num of worker process on each node.(e.g. global_ranks:[[0(ps),2(worker),3(worker)],[1(ps),4(worker)]])
For CUDA init reasons, I turned mp.set_start_method('spawn', force=True) on sl... |
st179991 | Hi! Hard to tell where this is going wrong. The warning from multiprocessing happens in the parent process (I think) and doesn’t pinpoint where the crash is happening. It looks like neither process gets to log the time up message, so do they even break out of their loops? |
st179992 | Hi Pieter,
You are right, they do break out of their loops or may be only warning messages after crashes.
Any way to debug? Thank you in advance.
I add logger = multiprocessing.log_to_stderr(), logger.setLevel(multiprocessing.SUBDEBUG) to the demo, getting the following info:
[INFO/Process-2] process shutting down
[DEB... |
st179993 | Hard to say. I did a quick search and came up with https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming 79, which might be useful here. It’s a warning that comes from deep in the guts of multiprocessing, so I’d start there. |
st179994 | When using torch.nn.parallel.DistributedDataParallel to parallelize the network on multiple GPUs, do nn.BatchNorm become synchronized among GPUs ?
I suppose it is, because there is a broadcast_buffers flag in DistributedDataParallel defaulted to True.
Do anyone has any thoughts or confirmation on this ? |
st179995 | The buffers in batch norm are synchronized between processes if broadcast_buffers=True, yes. This means that all processes get a copy of the buffers on process with rank 0. If you want to use a synchronized batch norm, check out nn.SyncBatchNorm 301. |
st179996 | Hi,
I am testing p2p communication of torch.distributed.
I have 2 nodes, with gloo backend.
When I isend / irecv multiple tensors with diff ‘tag’, it doesn’t show the expected result.
Could somebody help me of the async p2p?
Node0:
import torch
import torch.distributed as dist
if __name__ == "__main__":
rank = 0
... |
st179997 | You’re kicking off the send/recv operations, but don’t synchronize on completion. You’ll have to add calls to req.wait() to ensure the send/recv operations have actually completed. If you’re looking for synchronous send/recv instead, replace calls to isend/irecv by calls to send/recv (without the i). |
st179998 | I train my model across several machines. I have two machines which have gpus and infiniband cards. The networks is 1Gbit, Infiniband is 2x40Gbit. When I remove cards, and start training everything works, though slower than on one machine. When I run with infiniband setup, the system just hangs. There’s 100% GPU utilis... |
st179999 | It might be a bios issue. I have a similar issue with 4Xv100 GPU that runs too slow. It turns out to be a bios setup problem. There is a bios adjustments which needs to be arranged as performance mode.
[V100 is too slow for training 39](http://v100 18 topic) |
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