MagicFrogSJTU
commented
4 years ago 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
NanoCode012
cesarandreslopez
glenn-jocher
twangnh
liumingjune
Frank1126lin
bsugerman
wudashuo
machine 1 didn't show anything, no training process:
training on one machine, 1min faster per epoch:
📚 This guide explains how to properly use multiple GPUs to train a dataset with YOLOv5 🚀 on single or multiple machine(s). UPDATED 25 December 2022.
Before You Start
Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7. Models and datasets download automatically from the latest YOLOv5 release.
💡 ProTip! Docker Image is recommended for all Multi-GPU trainings. See Docker Quickstart Guide
💡 ProTip!
torch.distributed.runreplacestorch.distributed.launchin PyTorch>=1.9. See docs for details.Training
Select a pretrained model to start training from. Here we select YOLOv5s, the smallest and fastest 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
Multi-GPU DataParallel Mode (⚠️ not recommended)
You can increase the
deviceto use Multiple GPUs in DataParallel mode.This method is slow and barely speeds up training compared to using just 1 GPU.
Multi-GPU DistributedDataParallel Mode (✅ recommended)
You will have to pass
python -m torch.distributed.run --nproc_per_node, followed by the usual arguments.--nproc_per_nodespecifies how many GPUs you would like to use. In the example above, it is 2.--batchis 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`. ```bash $ python -m torch.distributed.run --nproc_per_node 2 train.py --batch 64 --data coco.yaml --cfg yolov5s.yaml --weights '' --device 2,3 ```
Use SyncBatchNorm (click to expand)
[SyncBatchNorm](https://pytorch.org/docs/master/generated/torch.nn.SyncBatchNorm.html) 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, ```bash $ python -m torch.distributed.run --nproc_per_node 2 train.py --batch 64 --data coco.yaml --cfg yolov5s.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, ```bash # On master machine 0 $ python -m torch.distributed.run --nproc_per_node G --nnodes N --node_rank 0 --master_addr "192.168.1.1" --master_port 1234 train.py --batch 64 --data coco.yaml --cfg yolov5s.yaml --weights '' ``` ```bash # On machine R $ python -m torch.distributed.run --nproc_per_node G --nnodes N --node_rank R --master_addr "192.168.1.1" --master_port 1234 train.py --batch 64 --data coco.yaml --cfg yolov5s.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 all `N` machines are connected. Output will only be shown on master machine!
Notes
--batchmust be a multiple of the number of GPUs.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_portlike below,Results
DDP profiling results on an AWS EC2 P4d instance with 8x A100 SXM4-40GB for YOLOv5l for 1 COCO epoch.
Profiling code
```bash # prepare t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html cd .. && rm -rf app && git clone https://github.com/ultralytics/yolov5 -b master app && cd app cp data/coco.yaml data/coco_profile.yaml # profile python train.py --batch-size 16 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0 python -m torch.distributed.run --nproc_per_node 2 train.py --batch-size 32 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1 python -m torch.distributed.run --nproc_per_node 4 train.py --batch-size 64 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1,2,3 python -m torch.distributed.run --nproc_per_node 8 train.py --batch-size 128 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1,2,3,4,5,6,7 ```A100
device0 (G)
train
val
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):
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
Credits
I would like to thank @MagicFrogSJTU, who did all the heavy lifting, and @glenn-jocher for guiding us along the way.