Yes, it will always create a new Task.

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

I see.
You can get the offline folder programmatically then copy the folder content (it's the same as the zip, and you can also pass a folder instead of zip to the import function)
task.get_offline_mode_folder()You can also have a soft link of the offline folder (if you are working on a linux machine:
ln -s myoffline_folder ~/.trains/cache/offline

But this will require some code changes...

I think this is the main issue, is this reproducible ? How can we test that?

This code will give you one graph titled "loss" with two series: (1) trains (2) loss

Hi TrickyRaccoon92 , yes the examples folder is a special case, I'm not sure you can directly delete it.
Can you archive individual experiments in it ?

Not sure why, but for some reason it seems it is failing to analyze the code, hence the warning and no packages...
Any other hints on your setup that might help to better understand the root cause ? maybe home folder with unicode characters ? python installed in a specific way?

but it still not is able to run any task after I abort and rerun another task

When you "run" a task you are pushing it to a queue, so how come a queue is empty? what happens after you push your newly cloned task to the queue ?

Hi SmugDog62

My guess is that there's an issue with the git repo detector.

Seems like you are correct
Can are you getting on the execution tab?
Is the repo correct?
Do you see the notebook in the uncommited changes ?

and the step is "queued" or is it "queued" in the pipeline state (i.e. the visualization did not update) ?

TartSeal39 please let me know if it works, conda is a strange beast and we do our best to tame it.
Specifically when you execute manually on a conda env we collect (separately) the conda packages & the python packages (so later we can replicate on both conda & pip, or at least do our best)
Are you running both development env and agent with conda ?

Hmm that is a good idea, and I think you are correct, it cannot support it. But it will be easy to do, maybe adding an argument trigger_on_archive ? wdyt?

Thanks WittyOwl57 ! let me check

VirtuousFish83
Hmm that is odd, could you send the full log?

Thanks!!

So are you saying the large file size download is the issue ? (i.e. network issues)

Hi MagnificentSeaurchin79
Unfortunately there is currently no way to reorder the plots, but you have a valid point. I suggest a GitHub UX issue ?
Regrading the debug samples, the difference is that the confutation matrix report is actually metadata, you can get these numbers by the API or the download, but the debug samples are static images ...

BTW: you can try to produce an interactive side by side confusion matrix with plotly, and use report_plotly_figure

I reached over 1M API calls in about one week using clearml-serving

Oh that makes sense now 🙂
If I remember correctly, adding an additional model to a signal clearml-serving instance should not actually change the number of API calls, they are mostly affected by the number of clearml-serving / containers and not in the number of models.

In the main pipeline I want to work with the secondary pipeline and other functions decorated with 

PipelineDecorator

. Does ClearMl allow this? I have not been able to get it to work.

Usually when we think about pipelines or pipelines, the nested pipeline is just another Task you are running in the DAG (where the target queue is the services queue).
When you say nested pipelines with decorators, what exactly do you have in mind ?

Many thanks 🙂

Hi FrothyShark37

is the task scheduler only acessible through the SDK?

yes, in the open source version this is strictly code based. I know the enterprise tier has a UI for it, but in terms of features I believe this is equivalent

Let me check that for a sec

Sure thing, anyhow we will fix this bug so next version there is no need for a workaround (but the workaround will still hold so you won't need to change anything)

Hi PompousBeetle71 , what exactly is the scenario / problem we are trying to solve ?

Hi @<1631826770770530304:profile|GracefulHamster67>
if you want your current task:

task = Task.current_task()

if you need the pipeline Task from the pipeline component

pipeline = Task.get_task(Task.current_task().parent)

where are you trying to get the pipelines from? I'm not sure I understand the use case?

One thought is to initialise a new clearML task in each fold to capture the iteration-level metrics, and then create another task/experiment at the end to capture the aggregated metrics across folds.

That is probably the easiest, and the most scalable.
BTW: with the mew reporting feature, you can integrate the comparison of the CV directly into your final report 🙂

These instructions should create the exact chart:
None
What am I missing ?

Hi @<1523701523954012160:profile|ShallowCormorant89>
This is generally based on number of agents, or am I missing something ? Also is it based on Task or decorated functions ?

I see, by default it will look for requirements.txt in the root of the repo (the actual repo).
That said in code you can specify the requirements .txt:
Task.force_requirements_env_freeze(requirements_file='repo/project-a/requirements.txt') task = Task.init(...)Notice, you need to call it prior to the Task.init call