(RC will be out in a few days)

HugePelican43 sure you can, usually the limiting factor is memory, as it cannot be shared among processes, so if one allocated all memory the second process will crash with out of memory error

MysteriousBee56 not a different port, just not with "localhost" but with your machine's IP

Sorry my bad:
config_obj['sdk']['stuff']['here'] = value

I have no idea what string reference could be used when steps come from Task?

Oh I see, you are correct, when it comes to Tasks the assumption is your are passing strings (with selectors on the strings, i.e. the curly brackets) but there is no fancy serialization/deserialization as you have with pipelines from decorators / functions. The reason for that is that the Task itslef is a standalone, there is no way for the pipeline logic to actually "pull data" from it and "pass" it to the o...

You mean like for your internal support channel inside your company ?

Are these fields of ClearML Task?

correct

can I mix steps with Task and Function?

Hmm interesting question, I think that in theory you should be able to, I have to admit that I have not tried yet, but it should work

BTW: you will be loosing the comments 😞

how can you be snyk and lower than 0.96

Yep Snyk auto "patching" is great 🙂
as I mentioned wait for the GH sync tomorrow, a few more things are missing there
In the meantime you can just do ">= 0.109.1"

Hi DrabCockroach54
Notice the free GPU memory is global hence (low), but the memory (at least with new nvidia drivers) is per process. I'm assuming that the processes using the memory is not a sub process? could that be ? whats the OS you are running on?

I'm just trying to see what is the default server that is set, and is it responsive

I'm assuming you mean your own server, not the demo server, is that correct ?

and then second part is to check if it is up and alive

Yes, you can curl to the ping endpoint :
https://clear.ml/docs/latest/docs/references/api/debug#post-debugping

DepressedChimpanzee34 what would be easier curl or python ?

CostlyOstrich36 did you manage to reproduce it?
I tried conda w/ python3.9 on a clean Windows VM , and it worked as expected ....

I think CostlyOstrich36 managed to reproduce?!

Hi GreasyPenguin14

However the cleanup service is also running in a docker container. How is it possible that the cleanup service has access and can remove these model checkpoints?

The easiest solution is to launch the cleanup script with a mount point from the storage directory, to inside the container ( -v <host_folder>:<container_folder> )
The other option, which clearml version 1.0 and above supports, is using the Task.delete, that now supports deleting the artifacts and mod...

Hi ResponsiveCamel97
Let me explain how it works, essentially it creates a new venv inside the docker, inheriting all the packages form the main system packages.
This allows it to use the installed packages if the version match, and upgrade/change if you need, all without the need to rebuild a new container. Make sense ?

How so? they are in one place? the creation of the venv is transparent, and the packages that are there are everything you have in the docker, plus the ability to override them from the UI.
What am I missing here ?

So I have a task that just loads a model, but I don't see it as an artifact in the UI

You should see it under Artifacts, Input model if you are calling Keras load function (or similar)

You can install it, and after the wizard is done uninstall it, if you want to keep using the trains from the git clone.

how did you install trains?
pip install git+

or
pip install -U trains

but somewhere along the way, the request actually remove the header

Where are you seeing the returned value?

Wow, those guys....

try these values:

os.environ.update({
    'CLEARML_VCS_COMMIT_ID': '<commit_id>',
    'CLEARML_VCS_BRANCH': 'origin/master',
    'CLEARML_VCS_DIFF': '',
    'CLEARML_VCS_STATUS': '',
    'CLEARML_VCS_ROOT': '.',
    'CLEARML_VCS_REPO_URL': '

',
})

task = Task.init(...)

Hi AdventurousRabbit79
Try:
"extra_clearml_conf" : "aws { s3 {key: A, secret : B, region: C, }} ",Generally speaking no need for the quotes on the secret/key
You also need the comma to separate between keys.
You can test if it is working by adding the same string to your local clearml.conf and importing the cleaml package

It might do the same ?!

as a backup plan: is there a way to have an API key set up prior to running docker compose up?

Not sure I follow, the clearml API pair is persistent across upgrades, and the storage access token are unrelated (i.e. also persistent), what am I missing?

😊

SteadyFox10 With pleasure 🙂
BTW: you can retrieve the Task id from its name with
Task.get_tasks(project_name='my project', task_name='my task name')See https://allegro.ai/docs/task.html?highlight=get_tasks#trains.task.Task.get_tasks