Yolov5: Multi-GPU Training

[NanoCode012]

This guide explains how to properly use multiple GPUs to train a dataset with YOLOv5 on single or multiple machine(s).

Before You Start

Clone this repo and install requirements.txt dependencies, including Python>=3.8 and PyTorch>=1.6.

git clone https://github.com/ultralytics/yolov5 # clone repo
cd yolov5
pip install -r requirements.txt

Training

Select a pretrained model to start training from. Here we select YOLOv5l, the second largest model available. See our README table for a full comparison of all models. We will train this model with Multi-GPU on the COCO dataset.

Single GPU

$ python train.py  --batch-size 64 --data coco.yaml --weights yolov5l.pt --device 0

Multiple GPUs DataParallel Mode (NOT Recommended)

You can increase the device to use Multiple GPUs in DataParallel mode.

$ python train.py  --batch-size 64 --data coco.yaml --weights yolov5l.pt --device 0,1

This method is slow and barely speeds up training compared to using just 1 GPU.

Multiple GPUs DistributedDataParallel Mode (Faster than DataParallel! Recommended!!)

You will have to pass python -m torch.distributed.launch --nproc_per_node, followed by the usual arguments.

$ python -m torch.distributed.launch --nproc_per_node 2 train.py --batch-size 64 --data coco.yaml --weights yolov5l.pt

--nproc_per_node specifies how many GPUs you would like to use. In the example above, it is 2.
--batch-size is now the Total batch-size. It will be divided evenly to each GPU. In the example above, it is 64/2=32 per GPU.

The code above will use GPUs 0... (N-1).

Use specific GPUs (click to expand)

You can do so by simply passing --device followed by your specific GPUs. For example, in the code below, we will use GPUs 2,3.

$ python -m torch.distributed.launch --nproc_per_node 2 train.py --batch-size 64 --data coco.yaml --cfg yolov5l.yaml --weights '' --device 2,3

Use SyncBatchNorm (click to expand)

SyncBatchNorm could increase accuracy for multiple gpu training, however, it will slow down training by a significant factor. It is only available for Multiple GPU DistributedDataParallel training.

It is best used when the batch-size on each GPU is small (<= 8).

To use SyncBatchNorm, simple pass --sync-bn to the command like below,

$ python -m torch.distributed.launch --nproc_per_node 2 train.py --batch-size 64 --data coco.yaml --cfg yolov5l.yaml --weights '' --sync-bn

Use Multiple machines (click to expand)

This is only available for Multiple GPU DistributedDataParallel training.

Before we continue, make sure the files on all machines are the same, dataset, codebase, etc. Afterwards, make sure the machines can communicate to each other.

You will have to choose a master machine(the machine that the others will talk to). Note down its address(master_addr) and choose a port(master_port). I will use master_addr = 192.168.1.1 and master_port = 1234 for the example below.

To use it, you can do as the following,

# On master machine 0
$ python -m torch.distributed.launch --nproc_per_node G --nnodes N --node_rank 0 --master_addr "192.168.1.1" --master_port 1234 train.py --batch-size 64 --data coco.yaml --cfg yolov5l.yaml --weights ''

```bash

On machine R

$ python -m torch.distributed.launch --nproc_per_node G --nnodes N --node_rank R --master_addr "192.168.1.1" --master_port 1234 train.py --batch-size 64 --data coco.yaml --cfg yolov5l.yaml --weights ''

where `G` is number of GPU per machine, `N` is the number of machines, and `R` is the machine number from `0...(N-1)`. 
Let's say I have two machines with two GPUs each, it would be `G = 2` , `N = 2`, and `R = 1` for the above.

Training will not start until <b>all </b> `N` machines are connected. Output will only be shown on master machine!

</details>


### Notes

- This does not work on Windows!
- `batch-size` must be a multiple of the number of GPUs!
- GPU 0 will take more memory than the other GPUs. (Edit: After 1.6 pytorch update, it may take even more memory.)
- If you get `RuntimeError: Address already in use`, it could be because you are running multiple trainings at a time. To fix this, simply use a different port number by adding `--master_port` like below,

```bash
$ python -m torch.distributed.launch --master_port 1234 --nproc_per_node 2 ...

Results

Tested on COCO2017 dataset using V100s for 3 epochs with yolov5l and averaged.
DistributedDataParallel mode.

Command

$ python train.py --batch-size 64 --data coco.yaml --cfg yolov5l.yaml --weights '' --device 0
$ python -m torch.distributed.launch --nproc_per_node 2 train.py --batch-size 64 --data coco.yaml --weights yolov5l.pt
$ python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --data coco.yaml --weights yolov5l.pt
$ python -m torch.distributed.launch --nproc_per_node 8 train.py --batch-size 64 --data coco.yaml --weights yolov5l.pt

FAQ

If an error occurs, please read the checklist below first! (It could save your time)

Checklist (click to expand)

• Have you properly read this post?
• Have you tried to reclone the codebase? The code changes daily.
• Have you tried to search for your error? Someone may have already encountered it in this repo or in another and have the solution.
• Have you installed all the requirements listed on top (including the correct Python and Pytorch versions)?
• Have you tried in other environments listed in the "Environments" section below?
• Have you tried with another dataset like coco128 or coco2017? It will make it easier to find the root cause.

If you went through all the above, feel free to raise an Issue by giving as much detail as possible following the template.

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab Notebook with free GPU:
• Kaggle Notebook with free GPU: https://www.kaggle.com/ultralytics/yolov5
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide

Credits

I would like to thank @MagicFrogSJTU , who did all the heavy lifting, and @glenn-jocher for guiding us along the way.

[MagicFrogSJTU]

There will be multiple/redundant outputs. It does not affect training. This is a WIP.

I suggest we use will be fixed in the future instead of WIP. Many probably don't know what is WIP.
By the way, explain all the abbreviations. We must assume Users know nothing!

Multiple GPUs DistributedDataParallel Mode (Recommended!!)

I suggest we should explictly make it clear that DDP is faster than DP. Use this title

Multiple GPUs DistributedDataParallel Mode (Faster than DP, Recommended!!)

The tutorial is excellent! Good job!

[feizhouxiaozhu]

Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
Traceback (most recent call last):
File "train.py", line 482, in
train(hyp, tb_writer, opt, device)
File "train.py", line 130, in train
with torch_distributed_zero_first(local_rank):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/amax/objectdetection/yolov5/utils/utils.py", line 41, in torch_distributed_zero_first
torch.distributed.barrier()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 1485, in barrier
work = _default_pg.barrier()
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:514, unhandled system error, NCCL version 2.4.8
[email protected]
Password for 'https://[email protected]@gitee.com':
Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device1 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device2 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device3 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device4 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device5 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device6 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
device7 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)

Traceback (most recent call last):
File "train.py", line 468, in
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 393, in init_process_group
store, rank, world_size = next(rendezvous_iterator)
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/rendezvous.py", line 172, in _env_rendezvous_handler
store = TCPStore(master_addr, master_port, world_size, start_daemon, timeout)
RuntimeError: Address already in use
Traceback (most recent call last):
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/launch.py", line 263, in
main()
File "/home/amax/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/distributed/launch.py", line 259, in main
cmd=cmd)
subprocess.CalledProcessError: Command '['/home/amax/anaconda3/envs/pytorch/bin/python', '-u', 'train.py', '--local_rank=7', '--batch-size', '64', '--data', 'data/7classes.yaml', '--cfg', 'models/yolov5s.yaml', '--weights', '']' returned non-zero exit status 1.

[NanoCode012]

store = TCPStore(master_addr, master_port, world_size, start_daemon, timeout)
RuntimeError: Address already in use

Hello @feizhouxiaozhu , I think this may be because you are running multiple trainings at a time, and they are communicating to the same port. To fix this, you can run in a different port.
Using the example from above, add --master_port ####, where #### is a random port number.

$ python -m torch.distributed.launch --master_port 42342 --nproc_per_node 2 ... 

Please tell me if this fixed the problem. If it doesn't, can you tell us how to replicate this problem?

[NanoCode012]

Hmm, I'm not sure why that is. @feizhouxiaozhu , could you try to re-clone the repo then try again?

If error still occurs, could you try to run on coco128? Run the code below in terminal.

cd yolov5
python3 -c "from utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')" && mv -n ./coco128 ../
export PYTHONPATH="$PWD"
python -m torch.distributed.launch --master_port 9990 --nproc_per_node 2 train.py --weights yolov5s.pt --cfg yolov5s.yaml --epochs 1 --img 320

I'm currently running 8 GPU DDP custom data training, and there is no issue.

Edit: Reply was removed. @feizhouxiaozhu , is the problem solved?

[cesarandreslopez]

Excellent guide guys, thank you so much! I was training on a DGX1 and was wondering why there wasn't much of a speed difference.

[glenn-jocher]

@cesarandreslopez oh wow, lucky you. Are you seeing faster speeds now with the updated multi gpu training?

[cesarandreslopez]

@glenn-jocher in DataParallel model, every Epoch, with about 51000 images in yolov5l.yaml was taking on the DGX1 about 6 and a half minutes.

on DistributedDataParallel Mode with SyncBatchNorm I am seeing about 3 minutes and 10 seconds, so quite an improvement.

I've seen no improvement in Testing speed.

On @NanoCode012's guide there is this note:

--batch-size is now the Total batch-size. It will be divided evenly to each GPU. In the example above, it is 64/2=32 per GPU.

Based on that I assumed that batch size could be something like --batch 1024, (128 per GPU), but I kept getting Cuda out of memory after an epoch was completed and it started to test, so I eventually just went with --batch 128.

Apparent GPU use during training and testing.

During training, GPU 0 seems to have a considerably higher RAM use than other GPUS (which limits the batch size to be around the same that one GPU could handle). The processing itself seems distributed on all GPUs

GPU consumption during testing looks like this, where GPU 0 has very high memory use but it doesn't seem to process while the other 7 GPUS seem busy with the amount of memory expected for a batch of that size:

Our training size for this example is about 51000 images and our testing sample is about 5100. Testing takes about 4 minutes and a half, an epoch on training takes about 3 minutes and 10 seconds

Given the amount of time this spends on testing I am wondering if it is possible or even useful to set testing every n epochs. We are currently studying up on this repository and will understand it enough soon to be able to offer PRs.

@glenn-jocher Happy to provide you remote access to the machine for your tests and so on. It's the least we can do! Just PM me.

[NanoCode012]

Hi @cesarandreslopez , nice numbers!

The reason GPU 0 has higher memory is because it has to communicate with other GPUs to coordinate. In my test however, I don’t see that vast of a difference in GPU memory like you do. The latest one is 31GB (GPU 0) and 20 GB (others). Maybe SynBN is increasing GPU load or dataloaders for testing(?).

Batch size is indeed divided evenly. Is it possible to run 128 batch size on your single GPU because that is quite large for yolov5l.

Testing is done on only 1 GPU(GPU0 tests , other gpu continue train) , so that may be why you experience slow testing times. It’s is currently being worked on to use multiple GPUs there.

It is an interesting concept to test every n epochs and can certainly be done. However, maybe randomness will cause you to miss the “best” epoch, so I’m not sure if it’s good.

Edit: If you would like to do so, it’s on line 339 in Train.py, add a (epoch%interval==0) condition

Edit2: How is speed without SynBN? Since the individual batch size is around 128/8 > 8, I’m not sure if accuracy would be affected.

Edit3: If you have multiple machines you want to run this training on, there is an experimental PR you could try.

[glenn-jocher]

@cesarandreslopez ok got it, thanks for the feedback. I think I know why your testing is CUDA OOM. Before the DDP updates train and test.py shared the same batch-size (default 32), it seems likely this is still the case, except that test.py is inheriting global batch size instead of local batch size. So I suspect you should be able to train with much larger batch sizes once this bug is fixed. @NanoCode012 does that make sense about the global vs local batch sizes being passed to test.py?

Testing every n epochs is a good idea. You can currently use python test.py --notest to train without testing until the very final epoch, but we don't have a middle ground. Testing may not benefit as much from multi-gpu compared to training, because NMS ops run sequentially rather than in parallel, and tend to dominate testing time. An alternative to testing every n epochs is simply to supply a higher --conf-thres to test at. Default is 0.001, perhaps setting to 0.01 will halve your testing time.

That's a very generous offer! I'm pretty busy these days so I can't take you up on it immediately, but I'll keep that in mind in the future, thank you! It would definitely be nice to have access to something like that.

[NanoCode012]

@glenn-jocher , I just noticed that! That may be why the memory is so different. But now it’s up to optimizations. For small “total batch size”, it makes sense to pass in the entire thing. For large “total”, it doesn’t make sense.

I think one easy solution is to let user pass in one argument “—test-total”, to test their total batch size vs their divided batchsize. But it can get confusing for newcomers.

Edit: What do you think?

[glenn-jocher]

@NanoCode012 if we replace total_batch_size with batch_size on L194:
https://github.com/ultralytics/yolov5/blob/fd532d9ce3b025c1040ed9c7c3cf80fd7b0c39ae/train.py#L191-L196

And L341 would that solve @cesarandreslopez issue about testing OOM?
https://github.com/ultralytics/yolov5/blob/fd532d9ce3b025c1040ed9c7c3cf80fd7b0c39ae/train.py#L339-L348

[NanoCode012]

If we do so, datasets for testing could take num_gpu times longer. (I remember training/testing with total batchsize 16 for coco taking 1h) .

I think giving user an option is good, but we should set test to use totalbatchsize to be on by default.. Only when user has OOM, should they configure it. “—notest—total” sounds good?

[glenn-jocher]

@NanoCode012 ok got it. I think the most common use case is for users to maximize training cuda mem, so since test.py is currently restricted to single-gpu it would make sense to default it to batch_size rather than total_batch_size. But I suppose we should wait for @MagicFrogSJTU work on test.py before really modifying, since it will get a makeover shortly here. I think it's best to try and simplify the options when possible so it 'just works' as steve jobs would say, so let's avoid adding extra arguments if possible.

@cesarandreslopez I think for the time being you could apply the L194 and L341 fix described above, we have a few more significant PRs due in the coming week, so a more permanent fix for this should be included in those.

[MagicFrogSJTU]

@NanoCode012 does that make sense about the global vs local batch sizes being passed to test.py?

@glenn-jocher
After my fix, the training.py would run parallel test and global_batch_size would be split into small local_batch_size in the test time just like the training time. Problem solved.

[cesarandreslopez]

@glenn-jocher please note that when --notest is used on the current master branch it will crash after completing the first epoch.

Traceback (most recent call last):
  File "train.py", line 469, in <module>
    train(hyp, tb_writer, opt, device)
  File "train.py", line 371, in train
    with open(results_file, 'r') as f:  # create checkpoint
FileNotFoundError: [Errno 2] No such file or directory: 'runs/exp0/results.txt'

I tried doing a touch results.txt under the /runs/expoN/ folder that will avoid the error above, but then a new one will appear:

Traceback (most recent call last):
  File "train.py", line 469, in <module>
    train(hyp, tb_writer, opt, device)
  File "train.py", line 380, in train
    if (best_fitness == fi) and not final_epoch:
UnboundLocalError: local variable 'fi' referenced before assignment

so adding --notest to the command above, in yolov5 will not work right now. (this does work on yolov3 on previous tests).

Edit 1: @NanoCode012 if I follow your suggestion:

Edit: If you would like to do so, it’s on line 339 in Train.py, add a (epoch%interval==0) condition

The same error describe here as --notest will appear.

[glenn-jocher]

@cesarandreslopez should be fixed following PR https://github.com/ultralytics/yolov5/pull/518. Tested on single-GPU and CPU.

[twangnh]

hi! @glenn-jocher for multi-gpu training, if using smaller batch size than 64, could you suggest the hyperparameter to adjust like the learning rate?

[MagicFrogSJTU]

hi! @glenn-jocher for multi-gpu training, if using smaller batch size than 64, could you suggest the hyperparameter to adjust like the learning rate?

Internally, batch size is kept at least 64. Gradient accumulation will be used if a batch size smaller than 64 is given. Therefore, no adjust is needed if you use a smaller batch size.

[liumingjune]

Hello, I have the following problem when using multi-GPU training, which is done according to your command line.

not working on multi-GPU training.

[NanoCode012]

Hello @liumingjune, could you provide us the exact line you used?

EDIT: Also, did you use the latest repo? I think this can be the reason.

[liumingjune]

Hello @liumingjune, could you provide us the exact line you used? Looking at the screenshot, did you pass in --local_rank argument?

Thank you for your reply. My command line is

python -m torch.distributed.launch --nproc_per_node 4 train. py --device 0,1,2,3
I have 4 GPUs totally.

[NanoCode012]

Hi @liumingjune , could you try to pull or clone the repo again? I saw that your hyp values are old, and train function is missing some arguments.

I ran

git clone https://github.com/ultralytics/yolov5.git && cd yolov5
python -m torch.distributed.launch --nproc_per_node 4 train.py --device 0,1,2,3

and there were no problems.

[liumingjune]

Hi @liumingjune , could you try to pull or clone the repo again? I saw that your hyp values are old, and train function is missing some arguments.

I ran

git clone https://github.com/ultralytics/yolov5.git && cd yolov5
python -m torch.distributed.launch --nproc_per_node 4 train.py --device 0,1,2,3

and there were no problems.

OK. I will try. Maybe that's the reason. I will try. My version is a clone of Yolov5 when it first appeared.Thanks a lot!

[liumingjune]

Hello, I want to know the difference between the current version and the version just released before, because I find that the form of data set preparation is different. The previous one is to prepare the data set path and the training file, verify the file. I need to manually separate out the training data and the validation data. This is not friendly to large data volumes.

[glenn-jocher]

@liumingjune I don't know exactly what you're referring to, but the full change history is available here https://github.com/ultralytics/yolov5/commits/master

[Frank1126lin]

well, i got the same problem with @feizhouxiaozhu if I set
`--nproc_per_node 6 or 8 ',
2 or 4 is OK.

python3 -m torch.distributed.launch --master_port 9999 --nproc_per_node 8 train.py --batch-size 256 --data data/shape.yaml --cfg models/yolov5x.yaml --weights ' '

subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', 'train.py', '--local_rank=7', '--batch-size', '256', '--data', 'data/shape.yaml', '--cfg', 'models/yolov5x.yaml', '--weights', '']' returned non-zero exit status 1.

[NanoCode012]

well, i got the same problem with @feizhouxiaozhu if I set
`--nproc_per_node 6 or 8 ',
2 or 4 is OK.

python3 -m torch.distributed.launch --master_port 9999 --nproc_per_node 8 train.py --batch-size 256 --data data/shape.yaml --cfg models/yolov5x.yaml --weights ' '

subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', 'train.py', '--local_rank=7', '--batch-size', '256', '--data', 'data/shape.yaml', '--cfg', 'models/yolov5x.yaml', '--weights', '']' returned non-zero exit status 1.

Hi @Frank1126lin , could you tell me where/when that error occured?

I ran the below(your code but on coco2017) on a new clone, and there were no issues till training epoch 1. Did you try to reclone?

python3 -m torch.distributed.launch --master_port 9999 --nproc_per_node 8 train.py --batch-size 256 --data data/coco.yaml --cfg models/yolov5x.yaml --weights ' '

@feizhouxiaozhu 's error is most likely due to an old clone as stated. Proper DDP training was added not too long ago.

OK. I will try. Maybe that's the reason. I will try. My version is a clone of Yolov5 when it first appeared.Thanks a lot!

[Frank1126lin]

well, i got the same problem with @feizhouxiaozhu if I set
--nproc_per_node 6 or 8 ', 2 or 4 is OK. python3 -m torch.distributed.launch --master_port 9999 --nproc_per_node 8 train.py --batch-size 256 --data data/shape.yaml --cfg models/yolov5x.yaml --weights ' ' subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', 'train.py', '--local_rank=7', '--batch-size', '256', '--data', 'data/shape.yaml', '--cfg', 'models/yolov5x.yaml', '--weights', '']' returned non-zero exit status 1.`

Hi @Frank1126lin , could you tell me where/when that error occured?

I ran the below(your code but on coco2017) on a new clone, and there were no issues till training epoch 1. Did you try to reclone?

python3 -m torch.distributed.launch --master_port 9999 --nproc_per_node 8 train.py --batch-size 256 --data data/coco.yaml --cfg models/yolov5x.yaml --weights ' '

@feizhouxiaozhu 's error is most likely due to an old clone as stated. Proper DDP training was added not too long ago.

OK. I will try. Maybe that's the reason. I will try. My version is a clone of Yolov5 when it first appeared.Thanks a lot!

OK,thank you so much for your ans. I will try to reclone this repo and try it again.
and annother question, when I use --nproc_per_node 4, it seems takes almost same time compare with single GPU training. just like code below.
python3 -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 256 --data data/shape.yaml --cfg yolov5x.yaml --weights '' --epochs 2400
Optimizer stripped from runs/exp2/weights/last.pt, 177.4MB Optimizer stripped from runs/exp2/weights/best.pt, 177.4MB 2400 epochs completed in 2.726 hours.

[Frank1126lin]

I got the same problem as below:

root@:~/ai/yolov5-0818# python3 -m torch.distributed.launch --master_port 9999  --nproc_per_node 8 train.py  --batch-size 128 --data shape.yaml --cfg yolov5l.yaml --weights '' --device 0,1,2,3,4,5,6,7
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device1 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device2 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device3 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device4 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device5 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device6 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device7 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)

Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5l.yaml', data='./data/shape.yaml', device='0,1,2,3,4,5,6,7', epochs=300, evolve=False, global_rank=0, hyp='data/hyp.scratch.yaml', img_size=[640, 640], local_rank=0, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=128, weights='', workers=8, world_size=8)
Start Tensorboard with "tensorboard --logdir runs/", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mixup': 0.0}

                 from  n    params  module                                  arguments                     
  0                -1  1      7040  models.common.Focus                     [3, 64, 3]                    
  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  2                -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 
  3                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  4                -1  1   1627904  models.common.BottleneckCSP             [256, 256, 9]                 
  5                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  6                -1  1   6499840  models.common.BottleneckCSP             [512, 512, 9]                 
  7                -1  1   4720640  models.common.Conv                      [512, 1024, 3, 2]             
  8                -1  1   2624512  models.common.SPP                       [1024, 1024, [5, 9, 13]]      
  9                -1  1  10234880  models.common.BottleneckCSP             [1024, 1024, 3, False]        
 10                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1   2823680  models.common.BottleneckCSP             [1024, 512, 3, False]         
 14                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1    707328  models.common.BottleneckCSP             [512, 256, 3, False]          
 18                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1   2561536  models.common.BottleneckCSP             [512, 512, 3, False]          
 21                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1  10234880  models.common.BottleneckCSP             [1024, 1024, 3, False]        
 24      [17, 20, 23]  1     37695  models.yolo.Detect                      [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]
Model Summary: 335 layers, 4.73987e+07 parameters, 4.73987e+07 gradients
Optimizer groups: 110 .bias, 118 conv.weight, 107 other
Scanning labels ../shape/labels/train.cache (3 found, 0 missing, 0 empty, 0 duplicate, for 3 images): 3it [00:00, 5409.68it/s]
Scanning labels ../shape/labels/train.cache (3 found, 0 missing, 0 empty, 0 duplicate, for 3 images): 3it [00:00, 7362.73it/s]
Traceback (most recent call last):
  File "train.py", line 458, in <module>
Traceback (most recent call last):
  File "train.py", line 458, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 167, in train
Traceback (most recent call last):
  File "train.py", line 458, in <module>
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
AttributeError: 'NoneType' object has no attribute 'updates'
Traceback (most recent call last):
Traceback (most recent call last):
  File "train.py", line 458, in <module>
  File "train.py", line 458, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 167, in train
    train(hyp, opt, device, tb_writer)
  File "train.py", line 167, in train
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
AttributeError: 'NoneType' object has no attribute 'updates'
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
AttributeError: 'NoneType' object has no attribute 'updates'
    train(hyp, opt, device, tb_writer)
    train(hyp, opt, device, tb_writer)
  File "train.py", line 167, in train
  File "train.py", line 167, in train
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
AttributeError: 'NoneType' object has no attribute 'updates'
AttributeError: 'NoneType' object has no attribute 'updates'
Traceback (most recent call last):
  File "train.py", line 458, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 167, in train
Traceback (most recent call last):
  File "train.py", line 458, in <module>
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
AttributeError: 'NoneType' object has no attribute 'updates'
    train(hyp, opt, device, tb_writer)
  File "train.py", line 167, in train
    ema.updates = start_epoch * nb // accumulate  # set EMA updates
AttributeError: 'NoneType' object has no attribute 'updates'

Analyzing anchors... anchors/target = 6.32, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 3 dataloader workers
Starting training for 300 epochs...
Traceback (most recent call last):
  File "train.py", line 458, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 238, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 261, in <module>
    main()
  File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 257, in main
    cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', 'train.py', '--local_rank=7', '--batch-size', '128', '--data', 'shape.yaml', '--cfg', 'yolov5l.yaml', '--weights', '', '--device', '0,1,2,3,4,5,6,7']' returned non-zero exit status 1.

[NanoCode012]

Hi @Frank1126lin , regarding the ema error, I just created a fix for this at #775 and waiting for review. I am not as sure about the second error. Can you replicate this on coco dataset?

Edit: Also are there any errors in Single GPU mode?

For training time, I haven't done any test in a while, so I cannot say.

[Frank1126lin]

Hi, @NanoCode012 , follow your #775 instruction , the ema.update issue has gone. Thanks a lot.
And on sigle GPU training , no issue.

another warning comes out with cmd below: (with training in process)

python3 -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --data shape.yaml --cfg yolov5l.yaml --weights '' --device 0,1,2,3

Warning as below:

/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before 
`optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before 
`lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more 
details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)

[Frank1126lin]

with coco128 datasets , it can training in process with the warning as below:

/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)

for my own datasets, error occuring as below:

root:~/ai/yolov5-0818# python3 -m torch.distributed.launch --nproc_per_node 8 train.py --batch-size 512 --data shape.yaml  --cfg shape5l.yaml --weights '' --epochs 1200 --adam
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device1 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device2 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device3 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device4 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device5 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device6 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)
           device7 _CudaDeviceProperties(name='Tesla V100-PCIE-32GB', total_memory=32510MB)

Namespace(adam=True, batch_size=64, bucket='', cache_images=False, cfg='./models/shape5l.yaml', data='./data/shape.yaml', device='', epochs=1200, evolve=False, global_rank=0, hyp='data/hyp.scratch.yaml', img_size=[640, 640], local_rank=0, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=512, weights='', workers=8, world_size=8)
Start Tensorboard with "tensorboard --logdir runs/", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mixup': 0.0}

                 from  n    params  module                                  arguments                     
  0                -1  1      7040  models.common.Focus                     [3, 64, 3]                    
  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  2                -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 
  3                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  4                -1  1   1627904  models.common.BottleneckCSP             [256, 256, 9]                 
  5                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  6                -1  1   6499840  models.common.BottleneckCSP             [512, 512, 9]                 
  7                -1  1   4720640  models.common.Conv                      [512, 1024, 3, 2]             
  8                -1  1   2624512  models.common.SPP                       [1024, 1024, [5, 9, 13]]      
  9                -1  1  10234880  models.common.BottleneckCSP             [1024, 1024, 3, False]        
 10                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1   2823680  models.common.BottleneckCSP             [1024, 512, 3, False]         
 14                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1    707328  models.common.BottleneckCSP             [512, 256, 3, False]          
 18                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1   2561536  models.common.BottleneckCSP             [512, 512, 3, False]          
 21                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1  10234880  models.common.BottleneckCSP             [1024, 1024, 3, False]        
 24      [17, 20, 23]  1     37695  models.yolo.Detect                      [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]





Model Summary: 335 layers, 4.73987e+07 parameters, 4.73987e+07 gradients



Optimizer groups: 110 .bias, 118 conv.weight, 107 other
Scanning labels ../shape/labels/train.cache (3 found, 0 missing, 0 empty, 0 duplicate, for 3 images): 3it [00:00, 5444.79it/s]
Scanning labels ../shape/labels/train.cache (3 found, 0 missing, 0 empty, 0 duplicate, for 3 images): 3it [00:00, 7186.13it/s]
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    train(hyp, opt, device, tb_writer)
    self._try_put_index()
  File "train.py", line 239, in train
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError

Analyzing anchors... anchors/target = 6.40, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 3 dataloader workers
Starting training for 1200 epochs...
Traceback (most recent call last):
  File "train.py", line 459, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 239, in train
    pbar = enumerate(dataloader)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 291, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 764, in __init__
    self._try_put_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
    index = self._next_index()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 208, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/distributed.py", line 80, in __iter__
    assert len(indices) == self.total_size
AssertionError
Traceback (most recent call last):
  File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 261, in <module>
    main()
  File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 257, in main
    cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', 'train.py', '--local_rank=7', '--batch-size', '512', '--data', 'shape.yaml', '--cfg', 'shape5l.yaml', '--weights', '', '--epochs', '1200', '--adam']' returned non-zero exit status 1.

[NanoCode012]

Hi @Frank1126lin , I have never gotten that error before. If coco128 works, but your custom dataset doesn't. Then we clearly see the problem.

A quick search got me here https://github.com/open-mmlab/mmdetection/issues/1223 .

I then noticed you used only 3 pictures? but 8 GPUS? How is it logical 😕 ? Although this might be a good idea to add a check if the number of images is divisble by the number of GPUs..

[glenn-jocher]

@NanoCode012 there's code in the create_dataloader() function in datasets.py to reduce batch-size to the length of the dataset when people try to use stupidly small datasets. In this case you probably want an assert to produce a hard error on someone trying to use more GPUs or nodes than images in their dataset.
https://github.com/ultralytics/yolov5/blob/fb4fc8cd0215443696a75dffbf56351266ccdfb0/utils/datasets.py#L63

[Frank1126lin]

Hi @Frank1126lin , I have never gotten that error before. If coco128 works, but your custom dataset doesn't. Then we clearly see the problem.

A quick search got me here open-mmlab/mmdetection#1223 .

I then noticed you used only 3 pictures? but 8 GPUS? How is it logical ? Although this might be a good idea to add a check if the number of images is divisble by the number of GPUs..

OK， I got that.Thanks a lot. I was just testing , so didn't notice that difference. I will change the datasets and try again.
sorry about that again, and as for me ,I am just a beginner at DL, and I like yolov5 so much.
so , shall we add some notice to begginners like me ? just like @glenn-jocher said.

[glenn-jocher]

@Frank1126lin is right, we want to add some more error checking for this particular eventuality.

If there's one thing I've learned about making open source code it's that people will find ways to break what you wrote that you never considered, so abundant error checking seems to be a must. Even if you thought people would use your code for a, b and c, they will also use it for d, e, and f that you never considered, so best to have all the bases covered with good explanation asserts to help guide usage in the right direction, i.e. "ERRO: your dataset size is 3, but you are using 8 GPUs. Reduce GPUs to 3 or increase dataset size >= 8."

[bsugerman]

Do you have a branch/fork of the main repo that is collecting all these changes?

If I try to run with --device=0,1 without using the torch.distributed.launch, I consistently receive an error from a convolution:

RuntimeError: Input type (torch.cuda.HalfTensor) and weight type (torch.cuda.FloatTensor) should be the same

even though I never encounter this when running on a single gpu. I thought maybe this thread would fix that but I get all sorts of errors when trying it. Seems there are a lot of suggested edits in the previous posts so it seems like a curated branch would be best for developing this. I didn't see one.

Also if anyone knows a simple fix for my particular problem, love to hear it!!

[Frank1126lin]

I got the yolov5l model on my own datasets with about 1200 epochs. It takes me 11.4hours in my machine with TESLA V100 * 8. I also used the python -m torch.distributed.launch --nproc_per_node 8 ...,Is there any other ways to accelerating this process?

[Frank1126lin]

and I got this warning at the begining in my training:

Using 5 dataloader workers
Starting training for 3800 epochs...
Epoch gpu_mem GIoU obj cls total targets img_size
0/3799 7.06G 0.02126 0.006547 0.0009961 0.0288 46 640: 0%| | 0/1 [00:23lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)

It seems about something with pytorch's warning.
And for the weight that trained complete, it works well.

[glenn-jocher]

@Frank1126lin make sure you are using the latest version of pytorch and this repo. In your screenshot you are not using all available CUDA memory, which would accelerate training.

[wudashuo]

I got the yolov5l model on my own datasets with about 1200 epochs. It takes me 11.4hours in my machine with TESLA V100 * 8. I also used the python -m torch.distributed.launch --nproc_per_node 8 ...,Is there any other ways to accelerating this process?

I know you are new in DL, but 11 hours is very fast, for my 10000+ images dataset, it takes over 100 hours with 1080Ti*4.
raise your batch size until your GPU mem is fully used, you only used 7.84G of your GPU MEM.
btw: are you using python3.6? python3.8 is recommended

[glenn-jocher]

@wudashuo right. YOLOv5x COCO trainings take about 10-12 days on a single V100. Another trick is that single GPU training is always more efficient, i.e. you can train 8 models with 1 GPU each in parallel faster than 8x GPU trainings in series. This allows for the most efficient parallelized hyperparameter searches etc.

[wudashuo]

@glenn-jocher hi glenn, thanks for your amazing work! I have a question about DDP.
When I used
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --data mydata.yaml --cfg yolov5l.yaml --weights yolov5l.pt
on one machine(1080Ti * 4), it took about 4m10s per epoch.
Yesterday I was trying to use two machines(1080Ti * 4 per machine), so I doubled the batch size, all 8 GPUs are fully used, but it took longer than training on one machine(5m36s per epoch).
on machine 0:
python -m torch.distributed.launch --nproc_per_node 4 --nnodes 2 --node_rank 0 --master_addr "192.168.1.18" --master_port 1234 train.py --batch-size 128 --data mydata.yaml --cfg yolov5l.yaml --weights yolov5l.pt --epochs 2000 --notest
on machine 1:
python -m torch.distributed.launch --nproc_per_node 4 --nnodes 2 --node_rank 1 --master_addr "192.168.1.18" --master_port 1234 train.py --batch-size 128 --data mydata.yaml --cfg yolov5l.yaml --weights yolov5l.pt --epochs 2000 --notest
this is machine 0 screenshot:

machine 1 didn't show anything, no training process:

training on one machine, 1min faster per epoch:

I am wondering what went wrong? 8 GPUs can't be slower than 4 GPUs.

[NanoCode012]

UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)

Hi @Frank1126lin , I usually see this when using a small dataset that has a short epoch.

Hi @wudashuo ,

machine 1 didn't show anything, no training process:

Regarding output for multi-machine, it will only output on the master machine because it will be redundant to output on other machines. I will add this info to the guide.

Yesterday I was trying to use two machines(1080Ti * 4 per machine), so I doubled the batch size, all 8 GPUs are fully used, but it took longer than training on one machine(5m36s per epoch).

There will be overhead using multiple machine compared to single machine, however it is weird that you don't get any speed improvement at all. I don't have two machines to test this, but from single machine test, 8 GPU is faster than 4 GPU. It would be great if you can find the underlying reason.

@glenn-jocher On a probably related note, I ran a few test of yolov5s (5 epochs) at varying batch sizes on 2 GPU DDP but found that there to be varying time taken. An increase of batch size does not always decrease time taken. Maybe there is some bottleneck ?

Env: My jupyterlab docker at commit 5f07782f569324bf32dbc63441128d4935c9254a (For future ref)

| Batch-size | Epoch 0 | Epoch 1 | Epoch 2 |
| --- | --- | --- | --- |
| 64 | 9:45 | 6:43 | 6:54 |
| 128 | 9:54 | 5:54 | 5:47 |
| 256 | 9:01 | 6:18 | 6:23 |

I ran a full COCO run on yolov5s at varying batch size as well. (2GPU DDP) The graph is quite interesting. There is a ripple effect at larger batch sizes.

Result from test.py
| Batch-size | mAP 0.5 | mAP 0.5..0.95 |
| --- | --- | --- |
| 64 | 56.2 | 36.9 |
| 128 | 56.18 | 36.99 |
| 256 | 56.58 | 37.0 |

Hypothesis: could a bigger batch-size get better results for a model?

Edit: See below for updated table!

[glenn-jocher]

@NanoCode012 that's really interesting! This has been something I've been worried about, as I'm forced to use small batch sizes for the larger models when training. Yes, larger batches provide smoother batchnorm statistics and optimizer gradients, so it makes sense that they should result in better final models. The response vs epochs is really interesting also, with the oscillations. This may be due to EMA, as at bs 256 it's updating 4X less than at bs 64, but it's hard to tell.

The repo defaults to scaling the loss by batch size, so bs 256 has 2x the loss per optimizer update, but half as many optimizer updates.

I don't know why speed doesn't improve at bs 256 vs 128. Perhaps the dataloader is a bottleneck at the larger batch sizes, as 256x4 images need to be loaded from the hard drive then at the same time. Maybe larger batch sizes would benefit from more dataloader workers, or caching the dataset with --cache. I can't say, as I don't have much multi-gpu experience unfortunately.

[glenn-jocher]

@NanoCode012 another point is that bs increases may provide much more speed improvements up to bs64, and less afterwards, as for example at bs16, there are 4 forward passes before an optimizer update, but at bs64 and up there is always a single forward pass. So moving from bs16 to 32 to 64 one should see significant speed gains, past that the benefit may plateau.

[Frank1126lin]

@glenn-jocher @wudashuo @NanoCode012 Thank you all guys. Yeah , I am using a small datasets(99 images) and with small batchsize 512, I will try agian later with all these advice.
What amazing job you guys have done. Amazing magic...

[glenn-jocher]

@Frank1126lin a 99 image dataset (too small to produce useful results), logically can only be paired with a batch-size of up to 99.

[Frank1126lin]

@Frank1126lin a 99 image dataset (too small to produce useful results), logically can only be paired with a batch-size of up to 99.

I trained 1200 epochs with 99 images (2 classes), and the result seems powerfull(mAP:0,5 is 0.923). I used batch-size 64 to train again yestoday with 3800 epochs, still in process now.
As you mentioned above, seems I used too many GPUs. ╮（╯＿╰）╭

[NanoCode012]

@Frank1126lin , fyi, if your individual batchsize (totalbatchsize/num_gpu) is small (generally <16), maybe using syncbn could give you some performance boost.

Edit: I’m not sure about its effect on small datasets though..

[Frank1126lin]

@Frank1126lin , fyi, if your individual batchsize (totalbatchsize/num_gpu) is small (generally <16), maybe using syncbn could give you some performance boost.

@NanoCode012 , OK, got you, thanks a lot. （゜▽＾*））

[NanoCode012]

Hi! I finished another test with varying batch sizes on yolov5m. I'll combine the earlier table of 5s for comparison.
I also attempted to finetune the 5s model on the new hyp for 20&100 epochs.

Result

Command: python test.py --data coco.yaml --img 640 --conf 0.001

Env: Docker py36
5s trained and fine-tuned on commit: 5f07782f569324bf32dbc63441128d4935c9254a
5m trained on commit: 0c01afc390da079047633e142b6718629e0f7ea4
| Model | Batch-size | mAP 0.5 | mAP 0.5..0.95 |
| --- | --- | --- | --- |
|5s | 64 | 56.2 | 36.9 |
| | 128 | 56.18 | 36.99 |
| | 256 | 56.58 | 37.0 |
| | | | |
| 5m | 32 | 63.11 | 44.1 |
| | 64 | 62.77 | 43.92 |
| | 128 | 63.09 | 44.0 |
| | | | |
| 5s fine_20 | 256 | 55.21 | 36.02 |
| 5s fine_100 | 256 | 56.13 | 37.03 |

Conclusion

Since I trained 5s and 5m on two different commits(I'm questioning why I did so..), we cannot properly compare them against each other. However, we see that batch size does not mean a higher accuracy. It could just be a fluke that the earlier 5s 256bs had a higher value than single. Is it weird that the 5m_ddp could not match your single training by 1 whole percentage?

Fine tuning the 5s does not seem to make it go higher than the normal training. Maybe the new hyp aren't as good for COCO or this idea isn't a good one?

[HardLaugh]

larger Batch training, may be we can try LARS(Layer-wise Adaptive Rate Scaling)

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[cswwp]

@glenn-jocher in DataParallel model, every Epoch, with about 51000 images in yolov5l.yaml was taking on the DGX1 about 6 and a half minutes.

on DistributedDataParallel Mode with SyncBatchNorm I am seeing about 3 minutes and 10 seconds, so quite an improvement.

I've seen no improvement in Testing speed.

On @NanoCode012's guide there is this note:

--batch-size is now the Total batch-size. It will be divided evenly to each GPU. In the example above, it is 64/2=32 per GPU.

Based on that I assumed that batch size could be something like --batch 1024, (128 per GPU), but I kept getting Cuda out of memory after an epoch was completed and it started to test, so I eventually just went with --batch 128.

Apparent GPU use during training and testing.

During training, GPU 0 seems to have a considerably higher RAM use than other GPUS (which limits the batch size to be around the same that one GPU could handle). The processing itself seems distributed on all GPUs

GPU consumption during testing looks like this, where GPU 0 has very high memory use but it doesn't seem to process while the other 7 GPUS seem busy with the amount of memory expected for a batch of that size:

Our training size for this example is about 51000 images and our testing sample is about 5100. Testing takes about 4 minutes and a half, an epoch on training takes about 3 minutes and 10 seconds

Given the amount of time this spends on testing I am wondering if it is possible or even useful to set testing every n epochs. We are currently studying up on this repository and will understand it enough soon to be able to offer PRs.

@glenn-jocher Happy to provide you remote access to the machine for your tests and so on. It's the least we can do! Just PM me.

Cool, which command you used to show this image ?

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