terminal

So the thing is, regardless of the link you should end with:
helper <clearml.storage.helper.StorageHelper object at 0x....>But the code that failed seemed to return None, which makes me suspect the url itself is somehow broken.
Any chance you have a space before the "s3://" ?
BTW : what's the clearml version you are using ?

if project_name is None and Task.current_task() is not None: project_name = Task.current_task().get_project_name()This should have fixed it, no?

BeefyHippopotamus73 this error seems like it is coming from boto3, are you sure the credentials are properly configured and that you have read permission ?

Ohh "~/trains.conf" is root probably

the second seems like a botocore issue :
https://github.com/boto/botocore/issues/2187

Hi JitteryCoyote63
Wait a few hours, there is a new fix, I'll make sure we upload it later today (scheduled to be there anyhow, I'll push it forward)

AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_DEFAULT_REGION

My bad, I worded my question wrong I see,

LOL no worries 🙂

Any chance you have some "debug" leftover in the Pipeline code:
https://github.com/allegroai/clearml/blob/7016138c849a4f8d0b4d296b319e0b23a1b7bd9e/examples/pipeline/pipeline_from_decorator.py#L113

Maybe we should show a warning when we it is being called, or ignore it when running via an agent ...

Okay, let me see...

UpsetTurkey67 my apologies I just notices the message

UnevenDolphin73 sounds great, any chance you can open a git issue on clearml-agent repo for this feature request ?

I think poetry should somehow return error if toml is "empty" then we can detect it...

https ?

@<1533619716533260288:profile|SmallPigeon24> , failed task should not actually be reused (i.e. cached), are you saying a failed Task is being reused? or are you saying that you want to "invalidate" the cache in the execution but still leave the Task as completed ?

JitteryCoyote63 that makes total sense!!
The reporting subprocess is not being updated with the new value! Let me check how we can pass it along...

Nice!!!

${PWD} works!

This will be resolved every call to Task.init (so I would recommend against it), how about "$HOME/" ?

So I think there are two bugs here?
--args overrides="key=value" does not work request: add --hydra to override hydra arguments (and if this is added the first one is not needed)Is that correct?

If a Task is in the 'Completed' I think the only option is to 'Reset' it (see image).

In the UI yes, in code you can do task.mark_aborted(force=True)

You do clear the previous run execution but I think for a repetitive task this is fine.

I would avoid that, no?

Hi WackyRabbit7
Yes, we definitely need to work on wording there ...
"Dynamic" means you register a pandas object that you are constantly logging into while training, think for example the image files you are feeding into the network. Then Trains will make sure it is constantly updated & uploaded so you have a way to later verify/compare different runs and detect dataset contemplation etc.
"Static" is just, this is my object/file upload and store it as an artifact for me ...
Make sense ?

TrickyFox41 are you saying that if you add Task.init inthe code it works, but when you are calling "clearml-task" it does not work? (in both cases editing the Args/overrides ?

It's the same but done from outside, you want the same and "offline" as well right?

Hi UnevenDolphin73

Does ClearML somehow

remove

any loggers from

logging

module? We suddenly noticed that we have some handlers missing when running in ClearML

I believe it adds a logger, it should not remove any loggers,
What's the clearml version you are using ?

FierceRabbit20 it seems the Pipeline Task that was created is missing the "installed requirements" section. How are you creating the actual pipeline Task? is this from code?

Hmm, we could add an optional test for the python version, and the fail the Task if the python version is not found. wdyt?

Won't it be too harsh to have system wide restriction like that ?

oh dear ...
ScrawnyLion96 let me check with front-end guys 😞

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...

Hmm Could you check if it makes a difference importing ClearML before shap ?
If this changes nothing, could you put a standalone script to reproduce the issue ?