CloudyHamster42 what's the trains-server version ?

However, when 'extra' is a positional argument then it is transformed to 'str'

Hmm... okay let me check something

SmarmySeaurchin8 it could be a switch, the problem is that when you have automatic stopping flows, they will abort a task, which is legitimate (e.g. should not considered failed)
How come you have aborted tasks in the pipeline ? If you want to abort the pipeline, you need to first abort the pipeline Task then the tasks themselves.

Wait. are you saying it is disappearing ? meaning when you cloned the Pipeline (i.e. in draft mode) the configuration was there, then the configuration disappeared ?

Not really sure that's easily done ... I mean you could query the data, but I'm not sure how you would import it. Btw why would you move from pro to self hosted?

Can you run the entire thing on your own machine (just making sure it doesn't give this odd error) ?

JitteryCoyote63 Great to hear 🙂
BTW:

Would it be possible to extend 

Task.init

  with a 

force_reuse

 that would enforce reusing these tasks

You can pass continue_last_task=True I think it should be equivalent to what you suggest

Hi AntsySeagull45
Any chance the original code was running with python2?
Which version of trains-agent are you using?

The cloning is done in another task, which has the argv parameters I want the cloned task to inherit from

JitteryCoyote63 What do you mean by that?

Hmmm, make sure the task doing the cloning is using 0.16.1 and above , because with .16 we added sections and the compatibility is between the version. Meaning if you have tasks generated with trains .16 you need trains .16 to clone them from code (so you could properly control the arguments)

https://stackoverflow.com/questions/60860121/plotly-how-to-make-an-annotated-confusion-matrix-using-a-heatmap
MagnificentSeaurchin79 see plotly example here:
https://allegro.ai/clearml/docs/docs/examples/reporting/plotly_reporting.html

Thanks!

Thanks WittyOwl57 ! let me check

So it's seemingly not the image, but maybe something to do with how Studio runs it as a kernel.

Yeah I think that for some reason it fails detecting this is actually jupyter noteboko (not really sure why), Thank you for double checking on the container !!

If i were to push the private package to, say artifactory, is it possible to use that do the install?

Yes that's the recommended way 🙂
You add the private repo here, for the agent to use:
https://github.com/allegroai/clearml-agent/blob/e93384b99bdfd72a54cf2b68b3991b145b504b79/docs/clearml.conf#L65

Hi ShinyPuppy47

getting this error pretty sprotically

What do you mean by "sporadically" ? This should be consistent ,either there is access to the clearml.conf, file or not. no ?!
What is your setup? Is this coming from the agent or manual execution ?

@<1523701868901961728:profile|ReassuredTiger98> how did you install the nightly locally ?
Can you also provide the full log?

https://github.com/allegroai/clearml/commit/6b8f21b30e005d50da35e2e5a5ded8782bce2f53

Yes that would work 🙂
You can also put it in the docker compose see TRAINS_AGENT_DEFAULT_BASE_DOCKER

@<1542316991337992192:profile|AverageMoth57> it sounds like you should use SSH authentication for the agent, just set
force_git_ssh_protocol: true
None
And make sure you have the SSH kets on the agent's machine

I am just about to move house, which is stressful enough without a global pandemic(!), so until that's completed I won't commit to anything.

Sure man 🙂 no rush, I appreciate the gesture regardless of the outcome
Many thanks!

What do you have under the "installed packages" ?

Where is the cleamlr-server running? GCP as well?

If the load balancer it Gateway can do the computation and leverage caching,

Oh that's True. But unfortunately out of scope for the open-source (well at the end someone needs to pay our salaries 🙂 )

I’d prefer not to have our EC2 instance directly exposed to the public Internet.

Yep, I tend to agree 🙂

JitteryCoyote63 s3 should work, you can go to your profile page, see if you do not have some old credentials already there, maybe this is the issue.

i'm Jax, not Manoj! lol.

I know 😄 I just mentioned that this issue is being actively discussed

Happy to hear 🙂

@<1566959357147484160:profile|LazyCat94>
I found the issue, the import of clearml should be before anything else, this way it patch the Argparser before using it

from clearml import Task

Move it to the first line, everything should work 🙂

How is this different from argparser btw?

Not different, just a dedicated section 🙂 Maybe we should do that automatically, the only "downside" is you will have to name the Dataset when getting it (so it will have an entry name in the Dataset section), wdyt ?

This is very odd ... let me check something

BTW trains agent will not delete the venv until the next run, so you can check exactly what's missing there