The Python process exited with exit code 137 (SIGKILL: Killed). This may have been caused by an OOM error. Check your command's memory usage.

[Koliya]

I am running a hugging face model on a GPU cluster (g4dn.xlarge, 16GB Memory, 4 cores). I run the same model in four different notebooks with different data sources. I created a workflow to run one model after the other. These notebooks run fine individually, but in the workflow setup, it gives me a Fatal error: The Python kernel is unresponsive (The Python process exited with exit code 137 (SIGKILL: Killed). This may have been caused by an OOM error. Check your command's memory usage.).

[daniel_sahal]

It could be due to the caching that may use some amount of memory when you're reusing cluster.

Simply try increasing your memory and/or optimize your code a little bit.

[Koliya]

I am not using a big batch of data during the process. It's just five text documents with less than 1000 characters each approximately. I am utilising the GPU to run the transformer model. So the model itself is not really running on CPU. That's why it is weird to get an OOM error with a significantly less amount of data that's been processed from the CPU.

[Kaniz]

Hi @Koliya Wedanage​, We haven’t heard from you since the last response from @Daniel Sahal​, and I was checking back to see if their suggestions helped you.

Or else, If you have any solution, please share it with the community, as it can be helpful to others.

Also, Please don't forget to click on the "Select As Best" button whenever the information provided helps resolve your question.

[jose_gonzalez]

You can check the executor's logs to narrow down the error if you would like, but technically, this is a OOM and increasing your cluster's resource will mitigate this issue

[fkemeth]

You might accumulate gradients when running your Huggingface model, which typically leads to out-of-memory errors after some iterations. If you use it for inference only, do

with torch.no_grad():
    # The code where you apply the model