It works. However, still, it sometimes takes a strangely long time for the agent to pick up the next task (or process it), even if it is only "Hello World".

The agent check every 2/5 seconds if there is a new Task to be launched, could that be it?

Hi @<1627478122452488192:profile|AdorableDeer85>
I'm sorry I'm a bit confused here, any chance you can share the entire notebook ?
Also any reason why this is pointing to "localhost" and not IP/host of the clearml-server ? is the agent running on the same machine ?

You can put a breakpoint here, and see what you are sending:
https://github.com/allegroai/trains/blob/17f7d51a93deb52a0e7d6cdd59da7038b0e2dd0a/trains/backend_api/session/session.py#L220

LethalCentipede31 sure:
task.upload_artifact(object_or_file, name)https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py

Sounds good?

Hi NutritiousBear41 , asking questions here is exactly the reason we open the Slack channel :)
Regrading the error, it might be that you are stubbled on a bug , do you get the git repo on the UI?

Fix in the next version 🙂

SmallDeer34
I think this is somehow related to the JIT compiler torch is using.
My suspicion is that JIT cannot be initialized after something happened (like a subprocess, or a thread).
I think we managed to get around it with 1.0.3rc1.
Can you verify ?

Hmm, so currently you can provide help, so users know what they can choose from, but there is no way to limit it.
I know the Enterprise version has something similar that allows users to create a custom "application" from a Task, there you can define a drop and as such, but that might be an overkill here, wdyt?

Hi @<1545216070686609408:profile|EnthusiasticCow4>
The auto detection of clearml is based on the actual imported packages, not the requirements.txt of your entire python environment. This is why some of them are missing.
That said you can always manually add them

Task.add_requirements("hydra-colorlog") # optional add version="1.2.0"
task = Task.init(...)

(notice to call before Task.init)

` from clearml.automation.parameters import LogUniformParameterRange
sampler = LogUniformParameterRange(name='test', min_value=-3.0, max_value=1.0, step_size=0.5)
sampler.to_list()

Out[2]:
[{'test': 1.0},
{'test': 3.1622776601683795},
{'test': 10.0},
{'test': 31.622776601683793},
{'test': 100.0},
{'test': 316.22776601683796},
{'test': 1000.0},
{'test': 3162.2776601683795}] `

Hi JitteryCoyote63

cleanup_service task in the DevOps project: Does it assume that the agent in services mode is in the trains-server machine?

It assumes you have an agent connected to the "services" queue 🙂
That said, it also tries to delete the tasks artifacts/models etc, you can see it here:
https://github.com/allegroai/trains/blob/c234837ce2f0f815d3251cde7917ab733b79d223/examples/services/cleanup/cleanup_service.py#L89
The default configuration will assume you are running i...

Hi JoyousElephant80

Another possibility would be to run a process somewhere that periodically polls ClearML Server for tasks that have recently finished

this is the easiest way to implement what you are after, and have full control over the logic itself.
Basically you inherit from the Monitor class
And implement the callback function:
https://github.com/allegroa...

feature value distribution over time

You mean how to create this chart? None

ohh that is odd. Let me check a minute

JitteryCoyote63 I think this one:
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py

Hi ClumsyElephant70

s there a way to run all pipeline steps, not in isolation but consecutive in the same environment?

You mean as part of a real-time inference process ?

Hurray ?

Hi WickedGoat98

but is there also a way to delete them, or wipe complete projects?

https://github.com/allegroai/trains/issues/16

Auto cleanup service here:
https://github.com/allegroai/trains/blob/master/examples/services/cleanup/cleanup_service.py

I have to leave i'll be back online in a couple of hours.
Meanwhile see if the ports are correct (just curl to all ports see if you get an answer) if everything is okay, try again to run the text example

Hi FancyChicken53
This is a noble cause you are after 😉
Could you be more specific on what you had in mind, I'll try to find the best example once I have more understanding ...

copy paste the trains.conf from any machine, it just need the definition of the trains-server address.
Specifically if you run in offline mode, there is no need for the trains.conf and you can just copy the one on GitHub

Can you print the actual values you are passing? (i.e. local_file remote_url )

Then we can figure out what can be changed so CML correctly registers process failures with Hydra

JumpyPig73 quick question, the state of the Task changes immediately when it crashes ? are you running it with an agent (that hydra triggers) ?

If this is vanilla clearml with Hydra runners, what I suspect happens is Hydra is overriding the signal callback hydra adds (like hydra clearml needs to figure out of the process crashed), then what happens is that clearml's callback is never cal...

Just to make sure, the first two steps are working ?
Maybe it has to do with the fact the "training" step specifies a docker image, could you try to remove it and check?
BTW: A few pointers
The return_values is used to specify multiple returned objects stored individually, not the type of the object. If there is a single object, no need to specify
The parents argument is optional, the pipeline components optimizes execution based on inputs, for example in your code, all pipeline comp...

Ohh, clearml is designed so that you should not worry about that, download_dataset = StorageManger.get_local_copy() this is cashed, meaning the machine that runs that like the second time will not re download the path.
This means step 1 is redundant, no?
Usually when data is passed between components it is automatically uploaded as artifact to the Task (stored on the files server or object storage etc.) then downloaded and passed to the next steps.
How large is the data that you are wo...

Okay, I was able to reproduce it (this is odd) let me check ...

Correct, and that also means the code the runs is not auto-magically logged.

is it normal that it's slower than my device even though the agent is much more powerful than my device? or because it is just a simple code

Could be the agent is not using the GPU for some reason?

Or am I forced to do a get, check if the latest version is fainallyzed,

Dataset Must be finalized before using it. The only situation where it is not is because you are still in the "upload" state.

, then increment de version of that version and create my new version ?

I'm assuming there is a data processing pipeline pushing new data?! How do you know you have new data to push?