Alternatively I understand I can also run the agent using...

No you should not if you are running the agent inside a container it cannot work in docker mode and spin its own containers
Bottom line use clearml-agent daemon

I am actually saving a dictionary that contains the model as a value (+ training datasets)

How are you specifically doing that? pickle?

This is odd it says 1.0.0 but then, it was updated t weeks ago ...

Ohh yes, if the execution script is not on git and git exists, it will not add it (it will add it if it is in a tracked file via the uncommitted changes section)
ZanyPig66 in order to expand the support to your case. Can you explain exactly which files are on git and which are not?

Thanks!
In the conf file, I guess this will be where ppl will look for it.

This is the thread checking the state of the running pods (and updating the Task status, so you have visibility into the state of the pod inside the cluster before it starts running)

And command is a list instead of a single str

"command list", you mean the command argument ?

Hi SteadyFox10 the way it works is that Trains limits the debug image history by reusing the same files names, so the UI will only present the iterations where the debug images are relevant for. With your sample code it looks like it exposes a bug , the generated link should contain iteration number, it does not and so it overwrites the debug images every iteration. Here is the image link: https://demofiles.trains.allegro.ai/Test/test_images.6ed32a2b5a094f2da47e6967bba1ebd0/metrics/Test/te...

It's more or less here:
https://github.com/allegroai/clearml-session/blob/0dc094c03dabc64b28dcc672b24644ec4151b64b/clearml_session/interactive_session_task.py#L431
I think that just replacing the package would be enough (I mean you could choose hub/lab, which makes sense to me)

can we also put the path to the CA?

Yes :)

Hi MuddySquid7
You can only add reports (scalars plots etc.) , though not to a published Task.
If you want to add an artifact, this should work.
prev_task = Task.get_task(task_id='112233') prev_task.mark_started(force=True) prev_task.reload() prev_task.upload_artifact(..., wait_for_upload=True) prev_task.mark_stopped(force=True)

Hi SteadyFox10
Short answer no 😞
Long answer, full permissions are available in the paid tier, along side a few more advanced features.
Fortunately in this specific use case, the community service allows you to share a single (or multiple) experiments with a read-only link. Would that work ?

DeliciousSeal67
are we talking about the agent failing to install the package ?

Hi UnsightlyShark53 , just a quick FYI, you can also log the entire config file config.json this will be stored as model configuration, and you can see it in the input/output models under the artifacts tab.
See example here you can path either the path to the configuration file, or the dictionary itself after you loaded the json, whatever is more convenient :)

Are you saying you have a single line in the console output of the component Task?

If i were to push the private package to, say artifactory, is it possible to use that do the install?

Yes that's the recommended way 🙂
You add the private repo here, for the agent to use:
https://github.com/allegroai/clearml-agent/blob/e93384b99bdfd72a54cf2b68b3991b145b504b79/docs/clearml.conf#L65

Sigint (ctrl c) only
Because flushing state (i.e. sending request) might take time so only when users interactively hit ctrl c we do that. Make sense?

If i have an alternative location for the vscode, where should i indicate in the configuration?

We might need to add support for that, but it should not be a problem to override (e.g. downloadable link like http/s3/ etc.)
Is this something that is doable ?

p.s. you should remove this line 🙂
extra_index_url: ["git@github.com:salimmj/xxxx"]

(Venv mode makes sense if running inside a container, if you need docker support you will need to mount the docker socket inside)
What is exactly the error you re getting from clearml? And what do you have in the configuration file?

basically use the template 🙂 we will deprecate the override option soon

Hmm are you running the clearml-agent on this machine? (This is the orchestration module, it will spin the Tasks and the dockers on the gpus)

it overwrites the previous run?

It will overwrite the previous if
Under 72h from last execution no artifact/model was createdYou can control it with "reuse_last_task_id=False" passed to Task.init
Task name itself is Not unique in the system, think of it as short description
Make sense ?

I think you are correct and the first time you spin the server it is not possible (I mean you need it up to get the access/secerey and only then you can insert them into the helm values) ... 😞

well done man!

In the "installed packages" section you should have "nvidia-dali-cuda110" In the agent's clearml.conf you should add:extra_index_url: [" ", ]https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf#L78
Should solve the issue

Hmm do you host it somewhere? Is it pre-installed on the container?

more like testing especially before a pipeline

Hmm yes, that makes sense.
Any chance you can open a github issue on it?
Let me see if I understand, basically, do not limit the clone on execute_remotely, right ?

When did this PipelineDecorator come. Looks interesting 

A few days ago (I think)
It is very cool! checkout the full object proxy interaction on the actual pipeline logic This might be better for your workflow, https://github.com/allegroai/clearml/blob/c85c05ef6aaca4e...

Hmmm is this s3 compatible?

Hi @<1523702969063706624:profile|PoisedShark13>

However, INSTALLED PACKAGES of my task is misses many of installed packages (any idea why?)

It automatically detects the directly imported packages, literally analyzing your code base and looking for imports
The derivative packages (i.e. the one that any of the "main" packages need, will be listed after the first time the agent installs everything)
If something specific is missing, you can manually add it with:

Task.add_requiremen...