Cannot generate translation using m2m_100

Greetings,

I was trying to deploy and use the m2m_100 model. I tried to do so on a p3.8xlarge instance since the model loading will eat all the memory on the p3.2xlarge.

With CUDA version 11.0 using deep learning ubuntu AMI Deep Learning Base AMI (Ubuntu 18.04) Version 30.0 , and using the latest pip version of torch, and installed fairseq and fairscale using git. When trying to reproduce the generation results, I keep getting CUDA Out-of-Memory errors. Even when I tried adding --cpu it kept trying to use the GPUs and kept exiting because of CUDA memory issues.

Please advise, Thanks,

[shruti-bh]

@dk-et Can you check if you get the same error with commit 6debe291 on the master branch?

[shruti-bh]

@AbdallahNasir Please refer to the latest version of the README for checkpoints and pipeline arguments to use with different hardware configurations.

About fine-tuning, that’s a great point. We don’t have support yet for loading a pretrained model and fine-tuning it, but we will try to add this soon.