SmarmySeaurchin8
updated_tags = task.tags
updated_tags.remove(tag)
task.tags = updated_tags

GrievingTurkey78

maybe since the package is not directly imported in my code it is possible to get a different version to what I have locally (?).

If these are derivative packages (i.e. imported by other packages) they are not automatically logged when executing the Task manually (in order to keep the "installed packages as lean as possible on the one hand but specify also specify the important packages for you)
That said, when the "trains-agent" executed the task it will store nack...

SmarmySeaurchin8 checks the logs, maybe you can find something there

Hi @<1523711619815706624:profile|StrangePelican34>
Hmm, I think this is missing from the docs, let me ping the guys about that 🙏

This will allow them to experiment outside of clearml and only switch to it when they are in an OK state. This will also helpnot to pollute clearml spaces with half backed ideas

What's the value of runnign outside of an experiment management context ? don't you want to log it?
There is no real penalty here, no?!

Is it still running ?

I think the limit is a few GB, I'm not sure, I'll have to check
And yes the oldest experiments will be deleted first (with the exception of published experiments, they will be deleted last)

I think it would be nicer if the CLI had a subcommand to show the content of 

~/.clearml_data.json

 .

Actually, it only stores the last dataset id at the moment, no not much 🙂
But maybe we should have a cmd line that just outputs the current datasetid, this means it will be easier to grab and pipe
WDYT?

SoggyFrog26 there is a full pythonic interface, why don't you use this one instead, much cleaner 🙂

Hey WickedGoat98
I found the bug, it is due to the fact the numpy (passed to plotly) contains both datetime and nan, and plotly.js does not like it. I'll make sure this is fixed, in the meantime you can just remove the first row (it contains the nan):
df = pd.concat([tickerDf.Close, tickerDf_Change.Close_pcent], axis=1) df = df[1:]

WickedGoat98 until the next RC release (should not take long) this will solve it:
df = pd.concat([tickerDf.Close, tickerDf_Change.Close_pcent], axis=1) df = df[1:] df.index = df.index.astype(str) setattr(df, 'ticker', args.symbol)Basically removing the nan and converting the datetime to string representation (so plotly.js likes it)

WickedGoat98 give me a minute, I'm not sure it is not ClearML related

Hi HappyLion37
It seems that you are "reusing" the Tasks. Which means the second time you open them you are essentially resetting the old run and starting all over.
Try to do:
task1 = Task.init('examples', 'step one', reuse_last_task_id=False) print('do stuff') task1.close() task2 = Task.init('examples', 'step two', reuse_last_task_id=False) print('do some more stuff') task2.close()

Hmm let me check first when it is going to upgraded and if there is a workaround

Let me chech

and if you add --skip-task-init ?
I think what happens is that the clearml-Task, adds a Task.init call without the output_uri that is called before "your" Task.init, and this is what causes it to be ignored. Could that be the case?

this issue on when trying to set up on our remote machines

You mean setting up the trains-server on remote machine?

@<1523701868901961728:profile|ReassuredTiger98> thank you so much for testing it!

Hi WittyOwl57 , that is awesome fix! what does "dynamic_ncols" change?
it seems like a tqdm parameter, not sure what it does ...

Just so I understand,
scheduler executes main every 60sec
main spins X sub-processes
Each subprocess needs to report scalars ?

1. look at immediate parents for identically-named files
   ....

UnevenDolphin73 are you saying this will be your way to log the diff between two versions (for increased visibility) ?
If so, how would you visualize it ?
(I really like this idea of visualizing the changeset, trying to think if there is "smart" way to create a callback to make the approach kind of best-practice) wdyt?

Are they expanded in the "api_server" ? (I verified on a linux machine, same error, the env in the api_server is not being resolved)

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

Hi @<1561885921379356672:profile|GorgeousPuppy74>
Please use threads to ask questions, so we keep everything tidy
(and if you can please remove your first message, and merge it with the above one, this one and edit this one, for better readability)

regrading the issue, you need to either have clearm.conf in your Home folder, I'm assuming thisis /root/
not /home/ubuntu/.
Also not sure why you need to expose ports...

Hi @<1627478122452488192:profile|AdorableDeer85>
Are you referring to running the pipeline on a remote machine ? could you provide the full Task/Pipeline log ?

can someone show me an example of how 

PipelineController.create_draft

I think the idea is to store a draft versio of the pipeline (not the decorator type, I think, but the one launching pre-executed Tasks).
GiganticTurtle0 I'm not sure I fully understand how / why you are using it, can you expand?

EDIT:

However, my intention is ONLY to create it to be executed later on.

Hmm so may like enqueue it?

ClearML best practice to create a draft pipeline to have the task on the server so that it can be cloned, modified and executed at any time?

Well it is, we just assume that you executed the pipeline somewhere (i.e. made sure it works) 🙂

Correction:
What you actually are looking for (and I will make sure we have it in the doc) is :
pipeline.start(queue=None)It will just leave it as is, so you can manually enqueue / clone it 🙂

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

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

Totally !

Sure, thing, I'll fix the "create_draft" docstring to suggest it