Sure thing, my pleasure 🙂

Seriously though, thank you.

If I did that, I am pretty sure that's the last thing I'd ever do...... 🤣

LOL can't wait to see that

I'll just take a screenshot from my companies daily standup of data scientists and software developers..... that'll be enough!

LOL I see a meme waiting for GrumpyPenguin23 😉

Pffff security.

Data scientist be like....... 😀

Network infrastructure person be like ...... 😱

This appears to confirm it as well.

https://github.com/pytorch/pytorch/issues/1158

Thanks AgitatedDove14 , you're very helpful.

Basically it gives it direct access to the host, this is why it is considered less safe (access on other levels as well, like network)

Does "--ipc=host" make it a dynamic allocation then?

Hmm good question, I'm actually not sure if you can pass 24GB (this is not a limit on the GPU memory, this affects the memblock size, I think)

In my case it's a Tesla P40, which has 24 GB VRAM.

Oh, so this applies to VRAM, not RAM?

Yes 🙂 https://discuss.pytorch.org/t/shm-error-in-docker/22755
add either "--ipc=host" or "--shm-size= 8g " to the docker args (on the Task or globally in the clearml.conf extra_docker_args)
notice the 8g depends on the GPU

I believe the standard shared allocation for a docker container is 64 MB, which is obviously not enough for training deep learning image classification networks, but I am unsure of the best solution to fix the problem.