Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Training a model with a custom FairseqDataset implementation #5492

Closed
2 tasks done
sidharthrajaram opened this issue Apr 24, 2024 · 1 comment
Closed
2 tasks done

Comments

@sidharthrajaram
Copy link

❓ Questions and Help

Before asking:

  • search the issues.
  • search the docs.

What is your question?

I have extended FairseqDataset and created a custom dataset implementation. How do I use this custom implementation to train against some particular model architecture using fairseq-train?

What have you tried?

Read the docs regarding Tasks and have a feeling that's the direction to go. Still curious about the extent to which the CLI tool fairseq-train can be used.

What's your environment?

  • fairseq Version (e.g., 1.0 or main): main
  • PyTorch Version (e.g., 1.0): 2.2.2
  • OS (e.g., Linux): Ubuntu 20.04
  • How you installed fairseq (pip, source): source
  • Build command you used (if compiling from source): pip installed from source as described in README.
  • Python version: 3.10
  • CUDA/cuDNN version: CUDA 11.8
@sidharthrajaram
Copy link
Author

For anyone encountering the same issue, it probably varies quite a bit depending on your dataset, but I had to implement custom Task and FairseqDataset classes and then override setup_task() and load_dataset() behavior in my custom Task class.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

1 participant