okay, I'll make sure we order it correctly

Not really 😞
Everyone can do everything, the idea is sharability and accessibility.
I do know that in the paid tier they have full access control roles SSO etc, but unfortunately its way too complicated for the open-source.
Basically what I'm saying is trust your fellow colleagues 🙂

Is this the same?
https://github.com/allegroai/clearml/issues/411

That seems reasonable to me:)

Hi @<1657918706052763648:profile|SillyRobin38>

I'm curious to know if it's possible to prevent uploading a duplicate endpoint.
...and we attempt to upload it again without any changes to the command content,

Basically you overwrite it, and yes, possible 🙂

any other aspect, could the system prevent the duplicate upload?

so basically check the hash and say, no need to upload?

Now I need to figure out how to export that task id

You can always look it up 🙂
How come you do not have it?

task = Task.get_task(project_name='project', task_name='best_model_ever')

Great!
BTW: you can take some inspiration from here:
https://github.com/allegroai/trains/blob/master/examples/automation/task_piping_example.py
Or from the full pipeline:
https://github.com/allegroai/trains/blob/master/examples/pipeline/pipeline_controller.py

No worries 🙂
Is this what you were looking for ?

BroadMole98 thank you for noticing !
I'll make sure it is fixed (a few other properties are also missing there, not sure why, I'll ask them to take a look)

Hi CourageousWhale20
Most documentation is here https://allegro.ai/docs

@<1523701868901961728:profile|ReassuredTiger98> in the UI can you see it in the "installed packages" section under the Execution Tab ?

The class documentation itself is also there under "References" -> "Trains Python Package"
Notice that due to a bug in the documentation (we are working on a fix) the reference part is not searchable in the main search bar

But I do not have anything linked correctly since I rely in conda installing cuda/cudnn for me

From the log it installed:
cudatoolkit==11.1.1
based on the CUDA it found on the host machine: agent.cuda_version = 110
But for some reason it installed the pytorch from the conda "pytorch" repo without the cuda support.

And the one with the CPU version? is it with "~=" or "="?

Hi Team, I'm currently trying to install ClearML-Server on a Powerpc server with RedHat7.

You are a brave man LividCrab90 !

s there dockerfiles for the ClearML-Server stack somewhere ?

The main issue is replacing the DB containers, do you have elastic/mongo/redis for powerpc ?

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

SteepDeer88
Try the following:
` Task.add_requirements("pycocotools-windows", "; platform_system == "Windows"")
Task.add_requirements("pycocotools", "; platform_system != "Windows"")

Task.init(...) You should see in your "installed packages" something like: pycocotools-windows ; platform_system == "Windows"
pycocotools ; platform_system != "Windows" `

ScantMoth28 where are you seeing this warning ?

I'm wondering what happens if i were to host the instance and one of these were to go down from time to time in production, as the deployments provided by the helm chart are not redundant.

Long story short, it will break the clearml-server, please do not take them down, if you do need to do that, also take down the clearml-server. The python clients will wait until it is up again, so no session would be destroyed

Hi JealousParrot68
This is the same as:
https://clearml.slack.com/archives/CTK20V944/p1627819701055200
and,
https://github.com/allegroai/clearml/issues/411

There is something odd happening in the files-server as it replaces the header (i.e. guessing the content o fthe stream) and this breaks the download (what happens is the clients automatically ungzip the csv).
We are working on a hit fix to he issue (BTW: if you are using object-storage / shared folders, this will not happen)

Nicely found @<1595587997728772096:profile|MuddyRobin9> !

from task pick-up to "git clone" is now ~30s, much better.

This is "spent" calling apt update && update install && pip install clearml-agent
if you have those preinstalled it should be quick

though as far as I understand, the recommendation is still to not run workers-in-docker like this:

if you do not want it to install anything and just use existing venv (leaving the venv as is) and if something is missing then so be it, then yes sure that the way to go

BTW: you can also just add -e " CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1" to the docker args (under the Execution tab) to override the setting of the docker.
you can also add " export; " to the docker startup bash script section (do not add "#/bin/bash" , just the actual script) to get a list of all the environment variables inside the docker, just in case

Hi OddShrimp85

right place to ask about clearml serving.

It is 🙂

I did not manage to get clearml serving work with my own clearml server and triton setup.

Yes it should have been updated already, apologies.
Until we manage to sync the docs, what seems to be your issue, maybe we can help here?

Any plans to add unpublished state for clearml-serving?

Hmm OddShrimp85 do you mean like flag, not being served ?
Should we use archive ?
The publish state, basically locks the Task/Model so they are not to be changed, should we enable unlocking (i.e. un-publish), wdyt?

By the way, will downloading still happen if the datasets is available in the cache folder?

If it is cached, then there is no need to re-download 🙂

Hi @<1523701304709353472:profile|OddShrimp85>
Do you mean Dataset.get_local_copy() ?

When you set the pod make sure you mount the clearml local cache folder to the PV
basically /root/.clearml/cache/

Hi @<1689446563463565312:profile|SmallTurkey79>

App Credentials now disappear on restart.

You mean in the web UI?