How to modify the identifier of GPU and the number of GPU to train the model?

[megvii-model]

How to modify the identifier of GPU and the number of GPU to train the model?

[chensnathan]

You can specify GPU ids with CUDA_VISIBLE_DEVICES. For example CUDA_VISIBLE_DEVICES=4,5,6,7 pods_train --num-gpus 4, it will use the last 4 GPUs for training. You may need to adjust the warmup iterations and warmup factor when you use fewer GPUs for training.

[Huzhen757]

I added statements：os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1' in the train_net script。When performing train_net script training, Report an error：
Default process group is not initialized
How to solve it?
And the default batch_ size is 4, I use two 3090 and the memory is 24G to train, how to modify the size of the batch size?

[Huzhen757]

oh，I know that I need to modify the IMS_PER_BATCH and IMS_PER_DEVICE parameter in the config script to change its batch_size.
But, for the training of two 3090 graphics cards, I will change WARMUP_FACTOR and WARMUP_ITERS parameters should be ？

[chensnathan]

When you use two GPUs, the error Default process group is not initialized should not show up.

For changing the WARMUP_FACTOR and WARMUP_ITERS:
WARMUP_ITERS = 1500 * 8 / NUM_GPUS
WARMUP_FACTOR = 1. / WARMUP_ITERS

[Huzhen757]

I have now modified the corresponding parameters in the config script, but run train_ net script still reports an error:
Default process group is not initialized

[Huzhen757]

Traceback (most recent call last):
File "train_net.py", line 106, in
launch(
File "/media/data/huzhen/YOLOF-torch/cvpods/engine/launch.py", line 56, in launch
main_func(*args)
File "train_net.py", line 96, in main
runner.train()
File "/media/data/huzhen/YOLOF-torch/cvpods/engine/runner.py", line 270, in train
super().train(self.start_iter, self.start_epoch, self.max_iter)
File "/media/data/huzhen/YOLOF-torch/cvpods/engine/base_runner.py", line 84, in train
self.run_step()
File "/media/data/huzhen/YOLOF-torch/cvpods/engine/base_runner.py", line 185, in run_step
loss_dict = self.model(data)
File "/home/hz/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/data/huzhen/YOLOF-torch/playground/detection/coco/yolof/yolof_base/yolof.py", line 133, in forward
losses = self.losses(
File "/media/data/huzhen/YOLOF-torch/playground/detection/coco/yolof/yolof_base/yolof.py", line 211, in losses
dist.all_reduce(num_foreground)
File "/home/hz/anaconda3/envs/torch/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 953, in all_reduce
_check_default_pg()
File "/home/hz/anaconda3/envs/torch/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 210, in _check_default_pg
assert _default_pg is not None,
AssertionError: Default process group is not initialized

[chensnathan]

Could you provide more details about your command for training?

[Huzhen757]

I am using the train_net script under tools folder for training, Some parameters in the config script are adjusted, including IMS_PER_BATCH， IMS_PER_DEVICE, WARMUP_FACTOR and WARMUP_ITERS parameters。And add extra statement in the train_net script ： os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'.
And update the path of Dataset in the base_dataset script.
Other default parameters and hyper-paramters dont change.

[chensnathan]

You need to add --num-gpus to your command when you train with yolof.
BTW, we recommend using pods_train as given in README.

[Huzhen757]

Now there is a new error in the 'dist URL' parameter:
cvpods.engine.launch ERROR: Process group URL: tcp://127.0.0.1:50147
RuntimeError: Address already in use

ai...Your code actually is too hard to run。。。。

[chensnathan]

Why not just follow the steps in README. It should work well.

[Huzhen757]

Using the method in REDEME to train, it can only modify the number of GPUs, but it definitely can't update the identifier of GPU to train at all.

[chensnathan]

It can.... I give an exmaple above.

You can specify GPU ids with CUDA_VISIBLE_DEVICES. For example CUDA_VISIBLE_DEVICES=4,5,6,7 pods_train --num-gpus 4, it will use the last 4 GPUs for training. You may need to adjust the warmup iterations and warmup factor when you use fewer GPUs for training.

[Huzhen757]

Ok，I konw. Take 2 GPUs for training , it still report error :
assert base_world_size == 8, "IMS_PER_BATCH/DEVICE in config file is used for 8 GPUs"
AssertionError: IMS_PER_BATCH/DEVICE in config file is used for 8 GPUs

The number of GPUs required by your code is too large. My team only has 4 GPUs per machine，I don't think I can train.....ai....

[Huzhen757]

I useing 4 GPUs for training with the way you provided， like this：
CUDA_VISIBLE_DEVICES=0，1，2，3 pods_train --num-gpus 4

But it still report a error ：
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1607370156314/work/torch/lib/c10d/ProcessGroupNCCL.cpp:784, invalid usage, NCCL version 2.7.8

How could I solve it ？ Thanks ！

[chensnathan]

Many reasons can produce this error. You can refer to this solution and have a try.

[Huzhen757]

OK，I trying to see if I could work it out. Thanks ！

[xuyuyan123]

这个代码太难跑了

[Huzhen757]

这个代码太难跑了

是的，很难跑，他是与基于cvpods库实现的， 需要安装这个库然后编译这个库，然后在源码中还要编译。而且最少要四张卡才能跑，非常吃显卡。。。之前我试了4张2080ti跑，结果还是报错，也就是上面个的error。难定，不想train这个代码了，其实这篇论文的encoder部分倒是可以学习的，其他的地方我懒得花时间了。。还得跑自己的实验，唉。。。