Or can I just set it to Task.current_task() ?

This would be my only improvement, otherwise awesome!!!
output_model.update_weights(weights_filename=os.path.join(training_data_path, 'runs', 'train', 'yolov5s6_results', 'weights', 'best.onnx'))

FierceHamster54 are you sure you have write permissions ?

AgitatedDove14 Got that invalid region error on the set_upload_destination() while the region ( aws-global ) I specified in my agent config worked fine to retrieve a dataset from the same bucket
2022-11-04 15:05:40,784 - clearml.storage - ERROR - Failed testing access to bucket XXXXX: incorrect region specified for bucket XXXX (detected region eu-central-1) Traceback (most recent call last): File "/root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages/clearml/model.py", line 1396, in set_upload_destination uri = storage.verify_upload(folder_uri=uri) File "/root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages/clearml/storage/helper.py", line 640, in verify_upload _Boto3Driver._test_bucket_config( File "/root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages/clearml/storage/helper.py", line 1698, in _test_bucket_config raise StorageError(msg) clearml.storage.helper.StorageError: Failed testing access to bucket ml-clearml.xhr.fr: incorrect region specified for bucket ml-clearml.xhr.fr (detected region eu-central-1) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/.clearml/venvs-builds/3.8/task_repository/yolov5.git/train_model.py", line 64, in <module> results = train_model(**kwargs) File "/root/.clearml/venvs-builds/3.8/task_repository/yolov5.git/train_model.py", line 36, in train_model output_model.set_upload_destination(' ') File "/root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages/clearml/model.py", line 1398, in set_upload_destination raise ValueError("Could not set destination uri to: %s [Check write permissions]" % uri) ValueError: Could not set destination uri to: [Check write permissions]

Oh okay, my initial implementation was not far off:
` task = Task.init(project_name='VINZ', task_name=f'VINZ Retraining {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')
task.set_progress(0)

print("Training model...")
os.system(train_cmd)
print("✔️ Model trained!")

task.set_progress(75)

print("Converting model to ONNX...")
os.system(f"python export.py --weights {os.path.join(training_data_path, 'runs', 'train', 'yolov5s6_results', 'weights', 'best.pt')} --img 1280 --include onnx")
print("✔️ Model converted to ONNX!")
task.set_progress(100)

print("Exporting model to ClearML...")
output_model = OutputModel(
    task=task,
    framework='yolov5',
    name='VINZ Model'
)

output_model.set_upload_destination(' ` ` ')
output_model.update_weights(os.path.join(training_data_path, 'runs', 'train', 'yolov5s6_results', 'weights', 'best.onnx'))
output_model.tags = [f'train-dataset-{continuous_learning_dataset_id[:8]}'] `

Well the credentials are scoped to the entire bucket, but do I have to specify the full uri path ? the model files management is not fully managed like for the datasets ?

Oh that's great, thanks 😍 🎉

Ah no i cant since the pipeline is in its own dummy model and you cannot reattach pipelines to real projects so I must instanciate a dummy task just to attach the output model to the correct project

Hi FierceHamster54

Do I need to instantiate a task inside my component ? Seems a bit redundant....

Yes, so the idea is that the Task (along the code) will be automatically linked with the output model, for better traceability.
That said you can "import" a model into the system (i.e. it was created somewhere else and you want to register it with InputModel.import_model
https://clear.ml/docs/latest/docs/clearml_sdk/model_sdk#importing-models
I guess "Input" from that perspective is the code is Using it so it is considered the code's input model rather then the code output, aka it created it

Oh, that's nice, if I import a model using InputModel do I still need to specify a OutputModel ?

BTW: what happens if you pass the same s3://bucket to Task.init output_uri ? I assume you are getting the same access issue ?

Okay! Tho I only see a param to specify a weights url while I'm looking to upload local weights

👍

while I want to upload a converted

.onnx

weights with custom tags to my custom project

Oh I see, sure, see this one?
https://github.com/allegroai/clearml/blob/master/examples/reporting/model_reporting.py

Or:
output_model.update_weights(weights_filename="/path/to/file.onnx")

Well I uploaded datasets in the previous steps with the same credentials

My bad, the specified file did not exists since I forgot to raise an exception if the export command failed >< Well I guess this is the reason, will test that on monday

Do I need to instantiate a task inside my component ? Seems a bit redundant....

while I'm looking to upload local weights

Oh, so this is not "importing uploaded (exiting) model" but manually creating a Model.
The easiest way to do that is actually to create a Task for Model uploading, because the model itself will be uploaded to unique destination path, and this is built on top of the Task.
Does that make sense ?

Hmmm, let me check

do I still need to specify a OutputModel

No need, only if you want to upload a local model file (but I assume in this case, no new model is created)

Is there an example of this somewhere ? Cause I'm training a YOLOv5 model which already has ClearML intergration built-in but it seems to be hardcoded to attach its task to a Yolov5 project and upload .pt file as artifact while I want to upload a converted .onnx weights with custom tags to my custom project

FierceHamster54 I saw you saying the YOLOv5 project and name are hardcoded in there. Fixed that for ya 😉 https://github.com/ultralytics/yolov5/pull/10100

Could it be it checks the root target folder and you do not have permissions there only on subfolders?

I have to specify the full uri path ?

No it should be something like " s3://bucket "

the model files management is not fully managed like for the datasets ?

They are 🙂

Trying it now