Okey thanks!

Ah! That's it, thank you very much ! I did not know this was an issue. I though the dataset was only linked to the fileserver and not to the specific url used to upload it.

I was looking at the code of the Dataset class, but I could not find where the files_server is retrieved.

here is the command I am using :
sudo docker run -it -v /home/ubuntu/app/:/app/ -v /home/ubuntu/folder/clearml.conf:/root/clearml.conf --network "clearml_backend" my_image bash

I noticed logs start as follows :
/usr/bin/python3.9 /usr/bin/python3.9: No module named pip /usr/local/bin/python3.8

there is nothing in the env

Okey thank you!
If I plan using S3 for external file storage, do I still need Elasticsearch and Mongo ?

I did, I copy pasted the config from within the docker

I can provide a screenshot, but I'd need to hide the urls 😅 and if do so it would look just like Malcolm's screenshot.

The logs continue like this :
` Summary - installed python packages:
pip:

• attrs==20.3.0
• backports.entry-points-selectable==1.1.1
• certifi==2021.10.8
• chardet==4.0.0
• clearml==1.1.4
• Cython==0.29.26
• distlib==0.3.4
• filelock==3.4.0
• furl==2.1.3
• future==0.18.2
• idna==2.10
• jsonschema==3.2.0
• numpy==1.21.5
• orderedmultidict==1.0.1
• pathlib2==2.3.6
• Pillow==8.4.0
• platformdirs==2.4.0
• psutil==5.8.0
• pyhocon==0.3.59
• PyJWT==2.0.1
• pyparsing==2.4.7
• pyrsistent==0.18.0
• pyt...

If it helps, I tried changing the python version to 3.9 (which is also installed in my image). The change is reflected in the agent's config (the lines that appear when starting the worker) but it's still using 3.8 when executing the script.

Sorry for the late reply. It is indeed a venv, I though it would not be an issue since the PYTHONPATH and the PATH are both set to prioritize the venv. I'll try to create a more classic image.

Python 3.8.12

of course, I am checking using the env command

Maybe it is some sort of misunderstanding from my side ? I thought :
Task.enqueue(task, queue_name="training_queue")is what starts the execution of the task. Do I need another function ?

It seems the agent does not like working with scripts located inside a git repository, I moved the requirements and the script in a folder without a .git and it works now, thank you!

Thank you! Is there a way to test the agent on a machine without GPU ?
When running this little script, I can see my agent installing the requirements, but it does not seem to ever start running the task.
task = Task.create( project_name="train", task_name="train", requirements_file="./requirements.txt", repo="") task.set_script(entry_point="./test.py") Task.enqueue(task, queue_name="training_queue")The logs are as follows :
` Starting Task ...

even thought when starting the worker I see this :
agent.python_binary = /opt/venv/bin/python3

(in the logs)

yes, it's set to true in the logs as well

I am very confused now, I tried switch to my local machine and change the clearml.conf.
It only partly worked :
Dataset.list_datasets() returns the correct list (from the remote server).
But Dataset.get(dataset_id="ce2abe847e004ac282cc435bfa9c4bd5")
gives me :
2021-12-20 13:46:39,404 - clearml.storage - ERROR - Could not download ` , err: Failed getting object localhost:8081/annotation_dataset/annotation.ce2abe847e004ac282cc435bfa9c4bd5/artifacts/state/state.json (404): <!DO...

I tried to fix the python binary in the config as well :
agent.python_binary = /opt/venv/bin/python3where :
/opt/venv/bin/python3is the output of which python ran inside a docker container using my image.
In the clearml-agent logs I see this :
/root/.clearml/venvs-builds/3.8/bin/python -u /root/.clearml/venvs-builds/3.8/code/train.pySo I don't know if it's using the same python version or not.

and agent.python_binary is empty.

What is the proper way to change a clearml.conf ?

The URLs are correct, I can use them to download the dataset zip.

Is there a way to make it synchronous ?

The fileserver is remote, but the bandwidth is not an issue.
Is the automatic artifact storage of clearml async ? (meaning even if the task is finished it could still be uploading associated artifacts ?)

For example to create a dataset, I use this :
from clearml import Dataset ds = Dataset.create(dataset_project='XX', dataset_name='XX') ds.add_files( path='/tmp/tmpbk2g6c3h' ) ds.upload() ds.finalize()

I don't really know. I just detected it automatically from the start, so I haven't looked into it yet.