I think it's inside the container since it's after the worker pulls the image

Oh that makes more sense, I mean it should not build the from source, but make sense
To solve for build for source:
Add to the "Additional ClearML Configuration" section the following line:
agent.package_manager.pip_version: "<21"
You can also turn on venv caching
Add to the "Additional ClearML Configuration" section the following line:
agent.venvs_cache.path: ~/.clearml/venvs-cache
I will make sure w...

now realise that the ignite events callbacks seem to not be fired

So this is an ignite issue ?

BTW: I think an easy fix could be:
if running_remotely(): pipeline.start() else: pipeline.create_draft()

When I give my Minio to output_uri argument, it uploads 500 KB /sec as before.

But it worked well when using StorageManager and uploading to the minio directly, is that correct?

.. I give my Minio to output_uri argument

How long did it take to run the demo code I posted?
(The one you mentioned took 0.16s to run locally)

BoredHedgehog47 you need to make sure "<path here>/train.py" also calls Task.init (again no need to worry about calling it twice with different project/name)
The Task.init call will make sure the auto-connect works.
BTW: if you do os.fork , then there is no need for the Task.init, the main difference is that POpen starts a whole new process, and we need to make sure the newly created process is auto-connected as well (i.e. calling Task.init)

So I think it makes more sense in this case to work with the former.

Totally !

If you set the package_manager to peotry then it will only use the lock files
https://github.com/allegroai/clearml-agent/blob/21c4857795e6392a848b296ceb5480aca5f98e4b/docs/clearml.conf#L53
If you clear the "Installed Packages" section, it will just use the "requirements.txt" in the repository itself.
What's the specific use case, and the problem we are trying to solve?

The other order (with custom decorator above pipeline fails - just for you info

)

This is on "purpose" the pipeline decorator has to be the top decorator.
Glad it works!

Would it also be possible to query based on

multiple

user properties

multiple key/value I think are currently not that easy to query,
but multiple tags are quite easy to do

tags=["__$all", "tag1", "tag2],

Notice that in your example you have

plt.figure()

This actually clears the matplotlib figure, this is why we are getting a first white image then the actual plot,
once I removed it I got a single plot (no need for the manual reporting)

X, y = make_regression(
    n_samples=100,     # Number of samples
    n_features=10,     # Number of features
    noise=0.1,   # Number of informative features
    random_state=42    # For reproducibility
)

# Convert to DataFrame for better f...

Hi @<1668065560107159552:profile|VivaciousPenguin20>
I think you are looking at the wrong experiment, this is a 3 year old experiment ? this does not seem to be your currently executed experiment, right?

Hmm DepressedChimpanzee34 my bad it seems the loading is done via YAML loader, but the dumping is straight forward str casting...
https://github.com/allegroai/clearml/blob/6e6271fb91f2aeb2aa7a13c6d07d4e635baaa670/clearml/backend_interface/task/task.py#L934
What would you expect to get (BTW "value\blah" is Not a valid string assignment in python as there is no \b escape character, it should be "value\blah" which translates into the text "value\blah")

TenseOstrich47 this looks like elasticserach is out of space...

'config.pbtxt' could not be inferred. please provide specific config.pbtxt definition.

This basically means there is no configuration on how to serve the mode, i.e. size/type of lower (input) layer and output layer.
You can wither store the configuration on the creating Task, like is done here:
https://github.com/allegroai/clearml-serving/blob/b5f5d72046f878bd09505606ca1147d93a5df069/examples/keras/keras_mnist.py#L51
Or you can provide it as standalone file when registering the mo...

Hi LazyTurkey38
What do you mean the git repo is not recognized? When execute_remotely leaves you should see on the task a ref to the git repo with the exact commit ID you have locally pulled, do you see it under the Execution tab?

restart the notebook kernel ?

JitteryCoyote63

somehow the previous iterations, not sure yet if it’s coming from my code, ignite or clearml

ClearML will automatically continue reporting from the previous iteration (i.e. if before continuing the Task the last iteration was 100, then the next report with iteration =0 will actually be 101)

task.set_initial_iteration(engine.state.iteration)

Basically it is called automatically by ClearML (obviously only when you continue an aborted Task)

Hi VexedCat68

The scheduler is set to run once per hour but even now I've got around 40+ anonymous running tasks.

Based on the screenshots these are the Datasets (which are also a Task with specific type etc).
I would actually name the Datasets you are creating You need to specify the parent version (i.e. how would it know it is a child dataset changeset) I'm assuming they are all uploading everything, hence still running?BTW: you can use the argument single_instance=True maki...

from the notebook run !ls ~/clearml.conf

This means that in your "Installed packages" you should see the line:
Notice that this is not a pypi artifactory (i.e. a server to add to the extra index url for pip), this is a direct pip install from a git repository, hence it should be listed in the "installed packages".
If this is the way the package was installed locally, you should have had this line in the installed packages.
The clearml agent should take care of the authentication for you (specifically here, it should do nothing).
If ...

Sure

Yes , both work :(

DilapidatedDucks58 if you have so many parameters, why don't you use the
task.connect_configuration(dict)
It will put it in the artifacts, as an editable json alike string.

Hmm that is odd. Let me take a look and ask the guys. Thank you for quickly testing the RC! I'm hoping a new RC with a fix will be there tomorrow, if we can quickly replicate

It runs directly but leads to the above error with clearml

Both manually (i.e. calling Task.init and running it without agent, and with agent ? same exact behavior ?

Hi CheekyFox58
If you are running the HPO+training on your own machine, it should work just fine in the Free tier

The HPO with the UI and everything, is designed to run the actual training on remote machines, and I think this makes it a Pro feature.

Thanks JitteryCoyote63 , once we have a reproducible example the fix should be very quick to push (with these things reproducing it is the challenge)

@<1724960464275771392:profile|DepravedBee82> I just realized, the agent is Not running in docker mode, correct? (i.e. venv mode)
If this is the case how come it is running as root? (could it be is is running inside a container? how was that container spinned?)

GentleSwallow91 notice that on the Task you have "Installed Packages" this is the equivalent of requirments.txt , you can edit it and add a missing package, or programatically add it in code (though usually directly imported packages are automatically registered, how come this one is missing?)

to add a package in code:
Task.add_requirements(package_name="my_package", package_version=">=1") task = Task.init(...)

base docker image but clearML has not determined it during the script ru...

The agent is using Bash (but when you add command line to the docker run, .bashrc is not executed, hence no conda in PATH)
Maybe add the full path to the conda executable:
ocker_setup_bash_script= [ "export PATH=""/workspace/miniconda/bin:$PATH", "export LOCAL_PYTHON=/workspace/miniconda/bin/python3","/workspace/miniconda/bin/conda activate /PATH_GOES_HERE"])