Hi VivaciousWalrus99
Could you attach the log of the run ?
By default it will use the python it is running with.
Any chance the original experiment was executed with python2 ?

It seems the code is trying to access an s3 bucket, could that be the case? PanickyMoth78 any chance you can post the full execution log? (Feel free to DM so it won't end up being public)

Did you meant 

--detached

 ?

Oops yes sorry you are correct should be --detached 🙂

Hi @<1547028116780617728:profile|TimelyRabbit96>
Start with the simple scikit learn example
https://github.com/allegroai/clearml-serving/tree/main/examples/sklearn
The pipeline example is more complicated, it needs the base endpoints, start simple 😃

I'm not sure about the intended use of

connect_configuration

now.

Basically here is the rationale behind it:
I have a config file that I want to log on the Task, and I Also want to be able to change this configuration file externally when launching using an agent (i.e. edit the content) I have a nested dictionary that I do not want to flatten and push as hyper-parameters because it is not very readble, so I want to store it in a more human readable form and edit it a...

I am asking this because my NGINX server is giving Gateway Timeouts for delete calls sometimes.

Sync ... it might make sense if you have a lot of load. it might also be that the server is preoccupied with other requests

do you have a video showing the use case for clearml-session

I totally think we should, I'll pass it along 🙂

what is the difference between vscode via clearml-session and vscode via remote ssh extension ?

Nice! remote vscode is usually thought of as SSH, basically you have your vscode running on your machine, and using SSH vscode automatically connects to the remote machine.

Clearml-Session also ads a new capability VSCode inside your browser, where the VSCode itself as well...

but it is not possible to write to a private channel in which the bot is added.

Is this a Slack limitation ?

Also, don't be shy, we love questions 🙂

Should be fixed soon (1.10 is supposed to be released next week)

Hi SteadySeagull18

However, it seems to be entirely hanging here in the "Running" state.

Did you set a an agent to listen to the "services" queue ?
Someone needs to run the pipeline logic itself, it is sometimes part of the clearml-server deployment but not a mist

-e

:user/private_package.git@57f382f51d124299788544b3e7afa11c4cba2d1f#egg=private_package

Is this the correct link to the repo and a valid commit id ?

Can you post a few more lines from the agent's log ?
Something is failing to install I'm just not sure what

(also im a bit newer to this world, whats wrong with openshift?)

It's the most difficulty Kubernetes flavor to work with 🙂

weve already tried that but it didnt really change ...

Can you provide full log? as well as how you created the pods ?

ExcitedFish86 verified, fix will be available on GitHub soon :)

BTW: UnevenDolphin73 you should never actually do "task = clearml.Task.get_task(clearml.config.get_remote_task_id())"
You should just do " Task.init() " it will automatically take the "get_remote_task_id" and do all sorts of internal setups, you will end up with the same object but in an ordered fashion

Yes even without any arguments give to Task.init() , it has everything from the server

Set it on the PID of the agent process itself (i.e. the clearml-agent python process)

BTW: for future reference, if you set the ulimit in the bash, all processes created after that should have the new ulimit

Oh that is odd. Is this reproducible? @<1533620191232004096:profile|NuttyLobster9> what was the flow that required another task.init?

JitteryCoyote63 The release was delayed due a last minute issue, should be released later today. Anyhow the code is updated on GitHub, so you can start implementing :) let me know if I can be of help :)

Is it possible in Clearml to somehow allocate resources so that maybe after running a number of Alice's tasks, Bob's task get processed (Like maybe Round robin fashion)

Hi DeliciousBluewhale87
A few options here:
set the agent with high / low priority queues. Make sure Alice pushes into low priority (aka HPO) then Bob can push into high priority when he needs. This makes a lot of sense when you have automation processes spinning many experiments. expanding (1) you could set differe...

I remember there were some issues with it ...

I hope not 😞 Anyhow the only thing that does matter is the auto_connect arguments (meaning if you want to disable some, you should pass them when calling Task.init)

JitteryCoyote63 fix should be pushed later today 🙂
Meanwhile you can manually add the Task.init() call to the original script at the top, it is basically the same 🙂

It is currently only enabled when using ports mode, it should be enabled by default , i.e a new feature :)

and sometimes there are hanging containers or containers that consume too much RAM.

Hmmm yes, but can't you see it in CLearML dashboard ?

unless I explicitly add container name in container arguments, it will have a random name,
it would be great if we could set default container name for each experiment (e.g., experiment id)

Sounds like a great feature! with little implementation work 🙂 Can you add a GitHub issue on clearml-agent ?

pip install git+ https://github.com/allegroai/clearml.git
out 🙂

Thanks!

nice 🙂

It all depends how we store the meta-data on the performance. You could actually retrieve it from the say val metric and deduce the epoch based on that

Also, can the image not be pulled from dockerhub but used from the local build instead?

If you have your docker configured to pull from local artifactory, then the agent will do the same 🙂 (it is calling the docker command just like you do)

agent.default_docker.arguments: "--mount type=bind,source=$DATA_DIR,target=/data"

Notice that you are use default docker arguments in the example
If you want the mount to always be there use extra_docker_arguments :
https://github.com/...

By default the agent will add the root of the git repository into the pythonpath , so that you can import...