Hi @<1547028031053238272:profile|MassiveGoldfish6>

Is there a way for ClearML to simply save the model once training is done and to ignore the model checkpoints?

Yes, you can simple disable the auto logging of the model and manually save the checkpoint:

task = Task.init(..., auto_connect_frameworks={'pytorch': False}
...
task.update_output_model("/my/model.pt", ...)

Or for example, just "white-label" the final model

task = Task.init(..., auto_connect_frameworks={'pytorch': "final*.pt"}
...
torch.save("/tmp/final-01.pt")

wdyt?

Hi @<1547028031053238272:profile|MassiveGoldfish6> , you should set output_uri in Task.init to point towards your S3 bucket 🙂

I think this is great 😄 I do have another question.
I am using S3 as my remote. When creating datasets and uploading, everything is great, it is pushed S3. How do I push a model to S3 server? As is, after training, my models are save locally. How do I push a trained model to the S3 server specified in my clearml.conf file?