Hi AgitatedDove14 , is the Dataset.get will take all child too?

I’m running the agent in ‘pip’ mode. I need to fetch certain secret values, which would be best done using Python code. If I incorporate it into the script (repository), others could deduce the path to retrieve the environment or secret value. Storing the environment variables in the clearml.config isn’t very flexible either.

Hi @<1523701070390366208:profile|CostlyOstrich36>
i attach for complete log

here my structure:

.
├── app
│   ├── backend
│   └── frontend
├── assets
│   ├── demo-app-sample.png
│   └── workflow.png
├── config
│   ├── clearml.conf
│   ├── list_models.py
│   ├── list_optimizer.py
│   ├── __pycache__
│   └── train_config.py
├── docker
│   ├── Dockerfile
│   ├── Dockerfile.app
│   ├── requirements.prod.txt
│   ├── requirements.train.txt
│   └── requirements.txt
├── lightning_logs
├── Mak...

Hi @<1523701070390366208:profile|CostlyOstrich36> , just want to update,
this is solve by

• remove

-f 

2. change Task.force_requirements_env_freeze(False, req_path) -> Task.add_requirements(req_path)
3. change my clearml-agent settings

Hi, @<1523701070390366208:profile|CostlyOstrich36> ,yes! correct! how to achive that? it will save my storage.

you can spesificly use Task.add_requirements and pointing to path requirement.txt

None

https://github.com/mert-kurttutan/torchview
maybe can try this one, and can send to logger clearml at the end.

i see thanks for the answer, i will read that reference.

Hi SmugDolphin23 , i have try 1.8.4rc1, and yeah its working! Thanks!

it seems your clearml-agent didn't setup the right git account. are you sure setup on your agent conf?

my case more like there is a task/process that running but somehow its takes too long to completed. it can be because connection issue forgot to put connection timeout, a problem connection database, etc that makes status still running, but its traped in a situation like that.

so i want to force shutdown a task to failed if that happen

Hi @<1523701070390366208:profile|CostlyOstrich36> , i think can try this to run it as standalone:

yeah, it seems i will try option 2 that you mentioned.
i upload my full my configuration, and when done using google colab my configuration will gone too.

alright, will try thanks!

# downloading data from s3 manager = StorageManager() target_folder = manager.download_folder( local_folder='/tmp', remote_url=f' ` '
)

    # upload to clearml
    dataset = Dataset.create(
        dataset_project=metadata[2], 
        dataset_name=metadata[3],
        dataset_tags=tags,
        output_uri=" ` ` "
    )
    fp_target_folder = os.path.join(target_folder, minio_s3_url)
    print('>>...

    from src.net import Classifier
ModuleNotFoundError: No module named 'src'

hmm yeah i think, this is not possible to share a whole script here.

i attach train.py here,
and to run it i do python src/train.py

hi @<1523701070390366208:profile|CostlyOstrich36> , i mean uv this None

Hi @<1523701994743664640:profile|AppetizingMouse58> , i have update on #228. Thanks!

i see thankyou!

my config is same like issue #763

` import clearml
from clearml import StorageManager, Dataset
from rich import print

version_clearml = clearml.version

manager = StorageManager()

print(f'clearml: {version_clearml}')
try:
minio_s3_url = 'x/x/x/x/x/x/x'
print('\n-------------download folder-------------')
target_folder = manager.download_folder(
local_folder='tmp',
remote_url=f' '
)
except Exception as e:
print(e)
print('FAILED: download fold...

maybe accidently install my custom solution on this https://github.com/muhammadAgfian96/clearml/commit/01db9aa40537a6c2f83977220423556a48614c3a at that time. so i said the test is passed.

https://github.com/allegroai/clearml/issues/865#issue-1517283786

hi i have similar case, but can we scheduled new task here?

def trigger_task_func(task_id):
    print("trigger running...")
    try:
        previous_task = Task.get_task(task_id=task_id)
        print(previous_task.artifacts)
        try:
            fp = previous_task.artifacts['latest_condition'].get_local_copy()
            params = open_json(fp)
            last_index = params.get('last_index')
            day_n = params.get('iteration')
            print("Success Fetching", param...

yes, so far i know, if we want to upload dataset on clearml, we need provide local_path to data, then clearml will upload to the platform.

my data not on local, but s3 bucket.
is there a way to point s3 url ? my currently workflow is download my data from s3 bucket to local, then upload to clearml.

i see,
thanks for clarify. i just want to find other solutions to storing secret value. rightnow i just storing secret value on env in clearml.conf in my workers. but it will complicated if there is new value, i need update workers conf and redeploy workers.

yup correct. but the scheduler not created idk why. here my code and the log

from doctest import Example
from clearml.automation import TriggerScheduler, TaskScheduler
from clearml import Task
import json


def open_json(fp):
    with open(fp, 'r') as f:
        my_dictionary = json.load(f)
    return my_dictionary
    
    
def trigger_task_func(task_id):
    print("trigger running...")
    try:
        previous_task = Task.get_task(task_id=task_id)
        print(previous_task.artifact...

still i need do this?
dataset.upload() dataset.finalize()
i have another question,
if we have uploaded data clearml, how we add data?
this is my way right now.

dataset = Dataset.create( dataset_project=metadata[2], dataset_name=metadata[3], description=description, output_uri=f" ", parent_datasets=[id_dataset_latest] )

Hi @<1523701205467926528:profile|AgitatedDove14> ,
Yes i want to do that, but so far i know Task.enqueue will execute immediately, i need execute task to spesific time, and i see to do that i need scheduler and set recurring False, set time.

I tried that create scheduler, but the scheduler not created when the function executed.

{"meta":{"id":"37a8d7b26c534f9da20682801f9bd2bd","trx":"37a8d7b26c534f9da20682801f9bd2bd","endpoint":{"name":"projects.get_all_ex","requested_version":"2.20","actual_version":"1.0"},"result_code":200,"result_subcode":0,"result_msg":"OK","error_stack":"","error_data":{}},"data":{"projects":[]}}