Which version? is this reproducible in this example?
None
(can you try with the latest clearml version 1.13.2?)

Oh, I misunderstood then docs/examples, sorry. I'm using pytorch-ignite.

Thanks for the tip!

Hi @<1631102016807768064:profile|ZanySealion18>

ClearML doesn't pick up model checkpoints automatically.

What's the framework you are using?
BTW:

Task.add_requirements("requirements.txt")

if you want to specify Just your requirements.txt, do not use add_requirements use:

Task.force_requirements_env_freeze(requirements_file="requirements.txt")

(add requirements with a filename does the same thing, but this is more readable)

@<1523701087100473344:profile|SuccessfulKoala55> Kind reminder again, thanks and sorry!

@<1523701087100473344:profile|SuccessfulKoala55> kind reminder not to miss this when you catch time, thanks!

No worries, sorry for pinging, was just making sure you (or anyone else who might help) doesn't miss it 🙂
I use Task.add_requirements("requirements.txt") right before the Task.init.
In main, I parse arguments command-line, add_requirements, initialize Task and call execute_remotely. After that it's all pretty much the usual workflow. Initialize the model, setup dataloaders, optimizer and run the training. I'm using pytorch-ignite and have model checkpoint made on validation evaluator COMPLETED event.

Sorry for the delay 🙏 - how do you import your packages and where do you initialize ClearML relative to the rest of the code?

Kind ping on this thread, thanks! 🙂

clearml-1.13.1

Task.add_requirements("requirements.txt")
task = Task.init(project_name="My project", task_name="My task")
task.execute_remotely(queue_name="default")
...

Hi @<1631102016807768064:profile|ZanySealion18> , can you provide more info on what framework you're using, which ClearML SDK version and how you're initializing the ClearML task?

    model_checkpoint = ModelCheckpoint(
        "checkpoint",
        n_saved=2,
        filename_prefix="best",
        score_function=score_function,
        score_name="accuracy",
        global_step_transform=global_step_from_engine(trainer),
    )

    # Save the model after every epoch of val_evaluator is completed
    val_evaluator.add_event_handler(
        Events.COMPLETED, model_checkpoint, {"model": model}
    )