UnevenDolphin73 i would use apiclient:
APIClient().projects.edit(project=project_id, system _tags=[])

*I might have a few typos above but that should be the gist

User/pass should be enough,
Could it be the specific commit ID is not pushed?

Hi @<1610083503607648256:profile|DiminutiveToad80>
do you have a full log? can you share the code you are trying to run?

Can you login to it?

Could it be that this is the callback that causes it?
None

throw an error when running without 

clearml.conf

 which tells the user to run clearml-init first?

I would like potential users to be able to just run the example code and get the experience, or even integrate with their code, without the need to run a single configuration
(Basically to alleviate as many potential hurdles from getting users on board clearml)

I'm trying to figure if this is reproducible...

But I think this error has only appeared since I upgraded to version 1.1.4rc0

Hmm let me check something

And only the main one ?

and they don't know how to write code, is this still possible?

well this means there is some standard of the data, right? what is that standard? unfortunately in our space there is no standard fort data, it's just too generic, so everyone always end with custom parsing of a sort.
Does that make sense ?

Hmm, I think the issue is here (the docker command mount)
'-v', '/tmp/.clearml_agent.de0n48pm.cfg:/root/clearml.conf'

creating a dataset with parents worked very well and produced great visuals on the UI!

woot woot!

I tried the squash solution, however this somehow caused a download of all the datasets into my

so this actually works, kind or like git squash, bottom line it will repackage the data from all the different versions into one new version. This means downloading the data from all squashed versions, then repackaging it into a single new version. Make sense ?

PompousBeetle71 , basically reset experiment will clear all the outputs, and input model model is well, input, it is not cleared. In the next execution it will be overridden. There is actually a way to change it from the UI, and override the initial model weights.

DrabCockroach54 that is quite cool!
Basically here is what I would do
Query Tasks that are both Running and Do not have system tag "development" (that means running on agents) + filter only tasks that start say 10 min ago Go over the list and see if (1) they have GPU scalar reported (meaning GPU is accessible) (2) min/max/val of GPU utilization is under 5%wdyt?

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)

Have a wrapper over Task to ensure S3 usage, tags, version number etc and project name can be skipped and it picks from the env var

Cool. Notice that when you clone the Task and the agents executes it, the project is already defined, so this env variable is meaningless, no ?

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?

Could it be it was never allocated to begin with ?

AstonishingSeaturtle47 I think there's a workaround for the GitHub multiple repo issue. See https://gist.github.com/gubatron/d96594d982c5043be6d4

Nice SourOx12 !

Seems like something is not working with the server, i.e. it cannot connect with one of the dockers.
May I suggest to carefully go through all the steps here, make sure nothing was missed
https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md

Especially number (4)

Try removing this magic environment that tells the sub-process there was already an Initialized Task.

import os env = dict(**os.environ) env.pop('TRAINS_PROC_MASTER_ID', None) 🙂

Are you getting the error from boto failing to launch additional ec2 instances ?

Nooooooooooooooooooooooo

NICE! MoodyCentipede68 this is awesome 🙂

Hi @<1631102016807768064:profile|ZanySealion18>

I'm using SSH for authentication, however, known_hosts doesn't seem to be passed to the docker so it prompts for authentification/fingerprint. Any ideas?

Hmm it is supposed to automatically mount your ~/.ssh folder into the docker to solve for that.
First try to set force_git_ssh_protocol: true
None
If that does not he...

Thanks GorgeousMole24
That is a very good point! passing to product guys

Hmm two questions: 1. How come it did not detect the packages when you were running the original task manually? 2. Could it be the poetry manager option is not working correctly?! Can you verify the venv is created with all packages? If so can you post the full log?

Any specific use case for the required "draft" mode?

But essentially Prefect also has agents to run jobs on machines where the processes run (which seems to be exactly the same model as in ClearML),

Yes ait is conceptually very similar

this data is highly regulated data, ...

The main difference that with ClearML the agents are running on Your machines (either local or on Your cloud account) the clearml-server does not actually have access to the data streaming through it.
Does that make sense ?