any suggestion?

@<1523701435869433856:profile|SmugDolphin23> Sorry for my late response.

I've tried OutputModel in local and SageMaker like:

task = Task(...)
output_model = OutputModel(task=task, framework="PyTorch")
output_model.set_upload_destination(uri="my file server URI")

...

for path in Path(cfg.work_dir).glob("**/*.pth"):
    output_model.update_weights(str(path))

and got the results in both envs.

2024-03-01 10:45:44,592 - clearml.storage - ERROR - Exception encountered while upl...

hmm, It seems that 1.10.2 also doesn’t work.
manual upload is ok, model save capture is not.

A few corrections to the original post.

When I set CLEARML_DEFAULT_OUTPUT_URI on SageMaker, the model save was not captured and nothing was showing on the artifact tab.
If CLEARML_DEFAULT_OUTPUT_URI is not set, the model save is captured, but it only records the file path and does not upload the entity.

I don't mean continuous training but I want to know about your plans for it 😋

Hi @<1523701435869433856:profile|SmugDolphin23> 👋
Yes, I can upload a Python file by the following line.

task.upload_artifact(name="config", artifact_object="config.py")

The artifact was uploaded to the file server with or without output_uri specification.

SDK: 1.14.1
WebApp: 1.14.0-431
Server: 1.14.0-431
API: 2.28

sure

In my case, I write codes and run single batch train-val, which contains model saving, in developing phase. I want TRAINS to overwrite the dev runs for keeping dashboard clean.

I’ve just try hard-coding but the result doesn’t change.

It doesn’t work…

task = Task.init(
    project_name="my_project",
    task_name="my_task",
    output_uri=os.getenv("CLEARML_DEFAULT_OUTPUT_URI", None),
)

I would like to confirm just in case.
In the desired behavior, reuse_last_task_id=True forces it for any intervals?

if you have any idea to reuse id even if models are outputted, please tell me thx

oh I got it. my codes output models and the task catch it automatically.

maybe the arguments is simply passed to Task.init()
self._trains = Task.init( project_name=project_name, task_name=task_name, task_type=task_type, reuse_last_task_id=reuse_last_task_id, output_uri=output_uri, auto_connect_arg_parser=auto_connect_arg_parser, auto_connect_frameworks=auto_connect_frameworks, auto_resource_monitoring=auto_resource_monitoring )