[HANDS-ON BUG]

[MojtabaAbdi]

### Bonus Unit 1 Notebook Error
Hello. I have a problem with executing my code in Bonus Unit 1 and it arises from this line, where, honestly talking, I have not manipulated anything:

!mlagents-learn ./config/ppo/Huggy.yaml --env=./trained-envs-executables/linux/Huggy/Huggy --run-id="Huggy2" --no-graphics

Below is a screetshot of an execution of the cell:

Actually I have copied the Bonus Unit 1 notebook to my google drive and ran there.

[RubSevian]

I have same problem

[RubSevian]

I fixed this problem with a quick fix of 56 lines on torch.float32 in the file /content/ml-agents/ml-agents/mlagents/torch_utils/torch.py .
P.S this line has already been fixed in the screenshot

[simoninithomas]

Hi, I think the solution for now provided by @RubSevian is the best (thanks 🤗 ) I'm going to check with MLAgents team to see where this error comes from.

[MojtabaAbdi]

@RubSevian @simoninithomas Thank you a lot. It worked for me too.

[iyaijuil]

Hi, I think the solution for now provided by @RubSevian is the best (thanks 🤗 ) I'm going to check with MLAgents team to see where this error comes from.

Hi, I also meet the same problem in unit5 SnowballTarget, I tried the same solution by @RubSevian but still can't fix it (it worked when I tried to fix Unit1 problem)

Here is the screenshot of an execution of the cell after I applied @RubSevian solution:

"RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat1 in method wrapper_CUDA_addmm)"

[RubSevian]

@iyaijuil Based on your mistake, I can make an assumption that the problem is in choosing a device, perhaps you need to specify what specifically to use the cpu or video card (CUDA)

[MrPark97]

Hi, I think the solution for now provided by @RubSevian is the best (thanks 🤗 ) I'm going to check with MLAgents team to see where this error comes from.

Hi, I also meet the same problem in unit5 SnowballTarget, I tried the same solution by @RubSevian but still can't fix it (it worked when I tried to fix Unit1 problem)

Here is the screenshot of an execution of the cell after I applied @RubSevian solution:

"RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat1 in method wrapper_CUDA_addmm)"

I've encountered same problem with 5th unit

[iyaijuil]

@iyaijuil Based on your mistake, I can make an assumption that the problem is in choosing a device, perhaps you need to specify what specifically to use the cpu or video card (CUDA)

Thanks for your reply. I used google colab to train the model. I followed the tutorial to use T4 GPU as my runtime type, and I used Macbook pro M3. Is it because there is any conflict within this set up?

[maartenx01]

I'm encountering the same issue on Unit 5 of Deep RL Course of RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat1 in method wrapper_CUDA_addmm) No issues with Units 1-4.

[Andimeo]

Same for me. Don't know how to explicitly set device.

I've even tried to add a .to(device) for each forward function in. mlagents/trainers/torch_entities/networks.py. But another error (about ambiguous bool or something) shows.

[MojtabaAbdi]

Actually, you don't need to train using a GPU. It took me 12 minutes to train the model with a cpu on colab. Thereby, you won't encounter errors.

[maartenx01]

Actually, you don't need to train using a GPU. It took me 12 minutes to train the model with a cpu on colab. Thereby, you won't encounter errors.

Thank you so much! This worked!

[grib0ed0v]

Looks like the proposed fix (changing torch.cuda.FloatTensor to torch.float32) was merged in upstream of ml-agents .

But to me, it also doesn't work. I experienced the same as @iyaijuil described.

I finally just run experiment on cpu by adding env variable.

!CUDA_VISIBLE_DEVICES='' mlagents-learn ./config/ppo/SnowballTarget.yaml --env=./training-envs-executables/linux/SnowballTarget/SnowballTarget --run-id="SnowballTarget1" --no-graphics

To me, it took around 8 min training for 200k on Colab CPU, so I agree with @MojtabaAbdi - just run on CPU and that's it.

[INFO] SnowballTarget. Step: 200000. Time Elapsed: 443.264 s. Mean Reward: 25.114. Std of Reward: 2.328. Training.