. I am not sure this is related to the fact the model is not correctly converted to TorchScript

Because Triton Only supports TorchScript (Not torch models) 🙂

I think the limit is a few GB, I'm not sure, I'll have to check
And yes the oldest experiments will be deleted first (with the exception of published experiments, they will be deleted last)

This is why we recommend using pip and not conda ...
PunySquid88 after removing the "//gihub" package is it working ?

I can then programmatically choose which file to import with importlib. Is there a way to tell clearml programmatically to analyze the files, so it can built up the requirements correctly?

Sadly no 😞
It analyzes the running code, then if it decides it is not a self contained script it will analyze the entire repo ...

I just saw that 

Task.create

 takes

Task.create is Not Task.init. It is meant to allow you to create new Tasks (think Jobs) from ...

Are you doing from keras import ... or from tensorflow.keras import ?

Can you send the full log? This is odd, it will by default use the python executable it (the agent) is running with.
Regardless you can specify the python executable to be used here:
https://github.com/allegroai/clearml-agent/blob/bd411a19843fbb1e063b131e830a4515233bdf04/docs/clearml.conf#L44

Are the key/secret the one you see in the UI?

GrumpyPenguin23 could you help and point us to an overview/getting-started video?

Exactly, that’s my problem: I want to remove it to make sure it is reinstalled (because the version can change)

JitteryCoyote63 yes, this is definitely a pip bug... can you test with the latest pip version, maybe it was fixed? (i.e. git+https:// link)

PompousBeetle71 so basically exclude parameters that are considered "local" only, so that other people will not accidentally use them?

If we have the time maybe we could PR a fix?!

Could you post what you see under "installed packages" in the UI ?

PompousBeetle71 , what you are saying that for some reason the --gpus all will not configure the Nvidia drivers to use all the gpus, when running bare metal (i.e no docker). Did I understand you correctly ?

Hi VexedCat68
Yes the serving is a bit complicated. Let me try to explain the underlying setup, before going into more details.

clearml-serving CLI is a tool to launch / setup. (it does the configuration and enqueuing not the actual serving) control plan Task -> Storing the state of the serving (i.e. which end points needs to be served, what models are used, collects stats). This Task has no actual communication with the serving requests/replies (Running on the services queue) Serving Task...

I think the real issue is that I am not able to specify a platform for the model,

None
there is no need to specify it, remove it from the config.pbtxt - the clearml-serving will automatically add the background

I think so (you can also comment out the Task.init() just to verify this is not a clearml issue)

Hi SarcasticSparrow10

Is it better to post such questions on Stackoverflow so they benefit everybody?

Yes, I think you are correct it would please do 🙂

Try to do " reuse_last_task_id='task_id_here'" ,t o specify the exact Task to continue )click on the ID button next to the task name in the UI)
If this value is true it will try to continue the last task on the current machine (based on project/name, combination) if the task was executed on another machine, it will just start a ...

Hi SpotlessLeopard9

I got many tasks that were just hang at the end of the script without ...

I remember this exact issue was fixed with 1.1.5rc0, see here:
https://clearml.slack.com/archives/CTK20V944/p1634910855059900

Can you verify with the latest RC?
pip install clearml==1.1.5rc3

Glad to hear 🙂

error in my-package setup command:

Okay this seems like an error in the setup.py you have in the "mypackage" folder

Try:
task.update_requirements('\n'.join([".", ])) 

JitteryCoyote63 see here https://stackoverflow.com/questions/55385900/pip3-setup-py-install-requires-pep-508-git-url-for-private-repo bottom line, you have to add package@ before the link, but if you do that and the package is already installed it will not install using the git repo, this is an issue with pip. I think that since the agent installs everything from scratch it should work for you. Wdyt?

Let me check something

Hi GrittyKangaroo27
Maybe check the TriggerScheduler , and have a function trigger something on k8s every time you "publish" a model?
https://github.com/allegroai/clearml/blob/master/examples/scheduler/trigger_example.py

IrritableGiraffe81 could it be the pipeline component is not importing pandas inside the function? Notice that a function decorated with pipeline component become a stand-alone, this means that if you need pandas you need to import inside the function. The same goes for all the rest of the packages used.
When you are running with run_loclly or debug_pipeline you are using your local env , as opposed to the actual pipeline where a new env is created inside the repo.
Can you send the Entire p...

Hi JuicyDog96
The easiest way at the moment (apologies for still lack of RestAPI documentation, it is coming:)
Is actually the code (full docstring doc)
https://github.com/allegroai/trains/tree/master/trains/backend_api/services/v2_8
You can access it all with an easy Pythonic interface, for example:
from trains.backend_api.session.client import APIClient client = APIClient() tasks = client.tasks.get_all()

how come the previous gitdiff passed ?

Wait @<1715900788393381888:profile|BitingSpider17> are you passing it on a single Task? these values are read by the daemon (i.e. running on the host) which means it is not getting them from the Task context (which leads to zero effect on the mount points)
Notice that in new versions of the clearml-agent the SDK mount point was changed to: sdk_cache: "/clearml_agent_cache" exactly to solve for the non-root containers:
[None](https://github.com/allegroai/clearml-agent/blob/6b31883e4579...

SoggyBeetle95 the question is, where does clearml stores these arguments, and the answer is on the Task object (from there the agent will take them and apply to the docker execution). Now since all users see all the tasks, they also see these arguments. Wdyt?

Honestly, this is all related to issue #340.

makes total sense.
But actually this id different from #340. The feature is to store the Data on the Task, this means each Task in your "pipeline" will be upload a new copy of the data. No?

I'd suggest some 

task.detach()

 method for remote execution maybe

That is a good idea, in theory it can also be used in local execution