FrothyShrimp23 , I think this is more of a product design - The idea of a published task is one that cannot be easily changed afterwards. What is your use case for wanting to often unpublish tasks? Why publish them to begin with? And why manually?
Hi @<1603560525352931328:profile|BeefyOwl35> , can you please elaborate on what you mean by running the build command?
Can you add a full log?
Runs perfectly with Minio too 🙂
#git_host="
http://bitbucket.org "
Can you please provide full logs of everything?
Hi @<1523701083040387072:profile|UnevenDolphin73> , not in the open source
You don't need to do any special actions. Simply run your script from within a repository and ClearML will detect the repo + commit + uncommitted changees
Which container version though?
ReassuredTiger98 , I played with it myself a little bit - It looks like this happens for me when an experiment is running and reporting images and changing metric does the trick - i.e reproduces it. Maybe open a github issue to follow this 🙂 ?
DepressedChimpanzee34 , I see. Regarding the things that are not currently implemented, please open a github issue so we can track this 🙂
Hi @<1603560525352931328:profile|BeefyOwl35> , The agent uses it's own entry point, so yes you do need to specify it even if it's in the dockerfile 🙂
Hi @<1670964701451784192:profile|SteepSquid49> , that sounds like the correct setup 🙂
What were you thinking of improving or do you have some pain points in your current setup?
How would the ec2 instance get the custom package code to it?
Hi BoredBat47 , use the --foreground tag to see the logs 🙂
From the looks of this example this should be connected automatically actually
https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py
Hi JitteryCoyote63 , I think this is what you're looking for:
https://clear.ml/docs/latest/docs/references/sdk/task#move_to_project
Can you try with Task.connect()
?
https://clear.ml/docs/latest/docs/references/sdk/task#connect
I don't think you need to mix. For example if you have a pre-prepared environment then it should something like export
CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=<PATH_TO_ENV_BINARY>
You'll need to assign an agent to run on the queue, something like this: 'clearml-agent daemon -- foreground --queue services'
Then add a screenshot of the info section
You ran the same exact command for agent one with --docker
and one without --docker
and the one without managed to reach the files?
Can you try running it via agent without the docker?
Before injecting anything into the instances you need to spin them up somehow. This is achieved by the application that is running and the credentials provided. So the credentials need to be provided to the AWS application somehow.
Hi @<1750327622178443264:profile|CleanOwl48> , you need to set the output_uri
in Task.init()
for example to True
to upload to the files server or to a string if you want to use s3 for example
Hi @<1747428509627715584:profile|CumbersomeDuck6> , are you using a self hosted server?
Hi @<1544853695869489152:profile|NonchalantOx99> , can you please add the full log?