How to use multiple GPUs using DataParallel?

[SM1991CODES]

Read before creating a new issue

• Users who want to use SpikingJelly should first be familiar with the usage of PyTorch.
• If you do not know much about PyTorch, we recommend that the user can learn the basic tutorials of PyTorch.
• Do not ask for help with the basic conception of PyTorch/Machine Learning but not related to SpikingJelly. For these questions, please refer to Google or PyTorch Forums.

For faster response

You can @ the corresponding developers for your issue. Here is the division:

Features	Developers
Neurons and Surrogate Functions	fangwei123456 Yanqi-Chen
CUDA Acceleration	fangwei123456 Yanqi-Chen
Reinforcement Learning	lucifer2859
ANN to SNN Conversion	DingJianhao Lyu6PosHao
Biological Learning (e.g., STDP)	AllenYolk
Others	Grasshlw lucifer2859 AllenYolk Lyu6PosHao DingJianhao Yanqi-Chen fangwei123456

We are glad to add new developers who are volunteering to help solve issues to the above table.

Issue type

• Bug Report
• Feature Request
• [x ] Help wanted
• Other

SpikingJelly version

0.0.0.0.2

@Yanqi-Chen @fangwei123456 I plan to use 3 gpus and Dataparalle is much easier than DDP. Could you please show an example of how to do this?
I tried but loss computation returns error since dataparalle seems to concat outputs along time rather than batch

Description

...

Minimal code to reproduce the error/bug

import spikingjelly
# ...