ohh AbruptHedgehog21 if this is the case, why don't you store the model with torch.jit.save and use Triton to run the model ?
See example:
https://github.com/allegroai/clearml-serving/tree/main/examples/pytorch
(BTW: if you want a full custom model serve, in this case you would need to add torch to the list of python packages)

we will try to use Triton, but it’s a bit hard with transformer model.

Yes ...

All extra packages we add in serving)

So it should work, you can also run your preprocess class manually from your own machine (for debugging), if you pass to it a local file (basically the downloaded model file from the UI, it should work

it. But it’s maybe not the best solution

Yes... it is not, separating the pre/post to CPU instance and letting triton do the GPU serving is a lot more effici...

WackyRabbit7 I guess we are discussing this one on a diff thread 🙂 but yes, should totally work, that's the idea

(without having to execute it first on Machine C)

Someone some where has to create the definition of the environment...
The easiest to go about it is to execute it one.
You can add to your code the following line
task.execute_remotely(queue_name='default')This will cause you code to stop running and enqueue itself on a specific queue.
Quite useful if you want to make sure everything works, (like run a single step) then continue on another machine.
Notice that switching between cpu...

How can i get loaded model in Preporcess class in ClearML Serving?

ComfortableShark77
You mean your preprocess class needs a python package or is it your own module ?

So currently there is a limit (from the elasticsearch) of about 10k (anything above the is subsampled)
In the new version we are adding a "maximize" button, then in the full screen you will have the raw data including all ???k samples. sounds good?

https://stackoverflow.com/questions/5419/python-unicode-and-the-windows-console
Hmm try to set this one before spinning the agent
Windows
set PYTHONIOENCODING=:replaceInside Colab
os.environ["PYTHONIOENCODING"] = ":replace"

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" `

HurtWoodpecker30

The agent uses the

requirements.txt

)

what do you mean by that? aren't the package listed in the "Installed packages" section of the Task?
(or is it empty when starting, i.e. it uses the requirements.txt from the github, and then the agent lists them back into the Task)

I wonder if this hack would work
Assume you upload an artifact/model to ' s3://storage.yandexcloud.net:443/clearml-models ' notice the port is added. Would that trigger a popup in the UI?
Also what happens if you add tge credential manually in the profile page?

LOL I see a meme waiting for GrumpyPenguin23 😉

A quick fix will be:
` import dotenv
dotenv.load_dotenv('~/.env')
from clearml import Task # Now we can load it.
import argparse

if name == "main":
# do stuff `wdyt?

Hi LudicrousDeer3
I have to admit I cannot remember one in the wild (I might be wrong though).
What's the specific use case you had in mind ?

in the docker-compose file. Still strange...

hmm yes it is... If you have an idea on what went wrong let me know, we would love to fix it

That is correct. Unfortunately though this is not part of the open source, this means that for the open source it might be a bit more hands-on to deploy an llm model

Basically I think I'm asking, is your code multi-node enabled to begin with ?

A few epochs is just fine

If this doesn't help.
Go to your ~/clearml.conf file, at the bottom of the file you can add agent.python_binary and change it to to the location of python3.6 (you can run which python3.6 to get the full path):
agent.python_binary: /full/path/to/python3.6

Is there an easy way to add a docker argument in the python script?

On the task it self in the UI you can edit the docker arguments and add any missing flags
(task.set_base_docker will do the same from code)
You can also edit the configuration and always add this flag:
None

SubstantialElk6 try to add -e CLEARML_AGENT_EXTRA_PYTHON_PATH=/code/app/flair
It should add it to the runtime pythonpath
(to the BASE DOCKER IMAGE on the Task itself)

Hi StrangePelican34 , you mean poetry as package manager of the agent? The venvs cache will only work for pip and conda, poetry handles everything internally:(

DilapidatedDucks58 use a full link , without the package name
git+

LovelyHamster1 NICE! 👍

My main issue with this approach is that it breaks the workflow into “a-sync” set of tasks:

This is kind of the way you depicted it, meaning, there is an an initial dataset, "offline process" (i.e. external labeling) then, ingest process.

I was wondering if the “waiting” operator can actually be a part of the pipeline.
This way it will look more clear what is the workflow we are executing.

Hmm, so pipeline is "aborted", then the trigger relaunches the pipeline, and the pipeli...

Hi ElegantCoyote26 , in theory no limit, but that depends on how you spined the services queue agent:
https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_daemon
See services mode :

To limit the number of simultaneous tasks run in services mode, pass the maximum number immediately after the

--services-mode

option (e.g.

--services-mode 5

)

Hi @<1697056701116583936:profile|JealousArcticwolf24>
Can you run your pipeline on an agent (i.e. remotely) but launching it from the UI (not the taskscheduler)?

Hi ElegantCoyote26
Try:
task = Task.create(....) task.output_uri = " ..."

Hi HappyLion37
It seems that you are "reusing" the Tasks. Which means the second time you open them you are essentially resetting the old run and starting all over.
Try to do:
task1 = Task.init('examples', 'step one', reuse_last_task_id=False) print('do stuff') task1.close() task2 = Task.init('examples', 'step two', reuse_last_task_id=False) print('do some more stuff') task2.close()

ExcitedSeaurchin87 I took a quick look, dude this is awesome!!! Thank you 🤩

Could it be you have some custom SSL certificate installed, or policy ?
can you get other https sites? (for example your clearml-server)