Can you add a full log of an experiment?
Hi @<1552101447716311040:profile|SteadySeahorse58> , if the experiment is still in pending mode it means that it wasn't picked up by any worker. Please note that in a pipeline you have the controller that usually runs on the services queue and then you have the steps where they all can run on different queues - depending on what you set
Hi! Good to see another ClearML user that carries it with them between companies ^^
Also, did you make sure to give the required permissions to the clearml folders in /opt/clearml/ ?
Oh LOL 😛
BoredPigeon26 , it's a feature in our next release 🙂
Hi @<1545216070686609408:profile|EnthusiasticCow4> , generally speaking, pipelines are a special type of task. When you write steps using decorators you don't have to add the task init. However you can also build pipelines using existing tasks in the system, where those were created with task.init
Hi @<1702492411105644544:profile|YummyGrasshopper29> , console logs are saved in Elastic. I would check on the status of your container
Hi @<1566596960691949568:profile|UpsetWalrus59> , it seems you are correct. Can you please open a github issue to follow up on this? I'm sure it should be fixed fairly quickly 🙂
And you made sure to run clearml-agent init on the machine or to implement the clearml.conf manually?
Hi @<1523702496097210368:profile|ScantChimpanzee51> , your steps look ok but the error pretty much indicates that there is a folder permissions issue. Please navigate manually to /opt/clearml/data folder and check "ls -al" command what are the user and permissions for the "elastic_7" folder and then enter the elastic_7 folder and check the same for its "nodes" subfolder. If the permissions are correct try restarting the docker and checking if it helps.
How do you run docker compose? If you run it with the -d in the end it should stay and be persistent even after restart, if I'm not mistaken
You can also just delete the installed packages section from the webUI and it will force it to use the requirements.txt
SubstantialElk6 , I think this is what you're looking for:
https://clear.ml/docs/latest/docs/references/sdk/dataset#get_local_copyDataset.get_local_copy(..., part=X)
Can you add a code snippet that reproduces this for you please?
Hi @<1659005876989595648:profile|ExcitedMouse44> , you can simply configure the agent not to install anything and just use the existing environment 🙂
The relevant env variables for this are: CLEARML_AGENT_SKIP_PIP_VENV_INSTALL
CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL
None
Maybe SmugDolphin23 or AppetizingMouse58 might have some insight into this since behavior of Datasets changed in the last year I think
Hi @<1631102016807768064:profile|ZanySealion18> , I would suggest using the web UI as a reference. Open developer tools and check what is being sent/received when looking at the workers/queues pages
Hi @<1569496075083976704:profile|SweetShells3> , and how do you expect to control the contents of the file? Via the UI or to upload it and then run the pipeline?
Logs shows me that key is mounted to the docker container
How are you mounting the credentials?
What version of ClearML-Agent are you using?
JitteryCoyote63 , let me take a look if it happens to me as well 🙂
Hi @<1523701842515595264:profile|PleasantOwl46> , the version is released, thus public. Not sure what you mean, can you please elaborate?
Hi @<1673501397007470592:profile|RelievedDuck3> , should be possible. What errors are u getting?
Hi TartBear70 ,
You can use the following method:
https://clear.ml/docs/latest/docs/references/sdk/task/#taskset_random_seed
Please note you need to set it before running Task.init()
If you set it to None this will cancel any random seed override performed by ClearML.
Tell me if this helps 🙂
Also, what GPUs are you running on that machine?
VividDucks43 , I think I might have misunderstood you a bit - For a single pipeline you would have the same requirements.txt, why would you need many?
In that case you are correct. If you want to have a 'central' source of data then Datasets would be the suggested approach. Regarding your question on adding data, you would always have to create a new child version and append new data to the child.
Also maybe squashing the dataset might be relevant to you - None
Having the latest versions is always a good practice