UnevenDolphin73 if you have the time to help fix / make it work it will be greatly appreciated 🙂

Hi SmallDeer34
ClearML automagical logging will work on the current python process. But in your example yyour Bash is running another python script (that has nothing to do with the original notebook), hence clearml automagic is not aware of it (i.e. it cannot "patch" the tensorboard calls).
In order to make it work.
you should do something like:
from joeynmt import train train.main(...)Or something similar 🙂
Make sense ?

or by trains

We just upload the image as is ... I think this is SummaryWriter issue

sdk.conf will add it to the default loaded values (as I think you deduced).
can copy paste the sdk.conf here? (maybe something is missing there?)

could you remove it and test ?

Hmm, it is not returned, it is inside the function....

but I cannot compare between them

I think we noticed it, and this will be fixed in the next server update (again, some plotly.js issue there)

And voila full trace including Git and uncommitted changes, python packages, and the ability to change arguments from the UI 🙂

UPD: works on 1.7.0 as well, the bug is introduced in 1.8.0

Thanks JitteryCoyote63 , just to be clear, is this only in comparison or also on the individual Tasks ?

Ohh, like a query based only of the stored url ?
Do you also has the creating Task ?

This port is odd, it should be 8008
8015

https://techbeacon.com/enterprise-it/5-ways-lose-data-kubernetes-how-avoid-them
see (1) and (4)

Yes, the agent's mode is global, i.e. all tasks are either inside docker or in venv. In theory you can have two agents on the same machine one venv one docker listening to two diff queues

somehow set docker_args and docker_bash_setup_script equivalent??task.set_base_docker(...)
# somehow setup repo and branch to download to remote instance before runningThis is automatically detected based on your local commit/branch as well ass uncommitted changes

seems like I'm passing in my own docker image which is then used at run time?

You are passing the Default docker image, if the Task does not list a specific docker image it will use the one you passed.
Yes this is "docker mode" (in venv mode no dockers are used, it just creates a new venv per experiment and installs everything inside the venv)

If you wan to change the Args, go to the Args section in the Configuration tab, when the Task is in draft mode you can edit them there

the latter is an ec2 instance

and the agent fails to install on the ec2 machine ?

can I add user properties to a scheduler configuration?

please expand, what do you mean by user property and how one would use it?

and pip install clearml-agent fails?

well cudnn is actually missing from the base image...

We use an empty queue to enqueue our tasks in, just to trigger the scheduler

it's only importance is that the experiment is not enqueued anywhere else, but the trigger then enqueues it

👍

It's just that the trigger is never triggered
(Except when a new task is created - this was not the case)

Is the trigger controller running on the services queue ?

Awesome, PRs are always welcome, and we try to help with any request and feature coming for users. We just added audio support (RC releasing in a few days) based only on users request.
https://github.com/allegroai/trains/issues/120

Hi WickedStarfish97

As a result, I don’t want the Agent to parse what imports are being used / install dependencies whatsoever

Nothing to worry about here, even if the agent detects the python packages, they are installed on top of the preexisting packages inside the docker. That said if you want to over ride it, you can also pass packages=[]

That being said it returns none for me when I reload a task but it's probably something on my side.

MistakenDragonfly51 just making sure, you did call Task.init, correct ?
What dues
from clearml import Task task = Task.current_task()returns ?

Notice that you need to create the Task before actually calling Logger.current_logger() or Task.current_task()

Merged, is it working for you now?

It was installed by 'pip install kwcoco' while my conda env was active.

Well I guess my question is, how does conda know ehere to install it form, if this is not on the public channels ? is there a specific conda channel you added (or preconfigured) ?

Hi VexedCat68
Check this example:
https://github.com/allegroai/clearml/blob/4f9aaa69ed2d5b8ea68ebee5508610d0b1935d5f/examples/scheduler/trigger_example.py#L44

👍

The difference is that I want a single persistent machine, with a single persistent python script that can pull execute and report multiple tasks

So basically instead of using the agent, so simply spin a sub process ?

By default the pl Trainer will output everything to TB, which we automatically store. But verify that TB is installed