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

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)

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")
...

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

Thanks for the tip!

Kind ping on this thread, thanks! 🙂

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

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.

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

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

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}
    )