TartSeagull57 , you said the problem was with automatic reporting. Can you give an example of how you solved the issue for yourself?
Hi @<1523701083040387072:profile|UnevenDolphin73> , looping in @<1523701435869433856:profile|SmugDolphin23> & @<1523701087100473344:profile|SuccessfulKoala55> for visibility 🙂
Please do 🙂
Hi RoughTiger69 , how much ram is the process taking?
Hi WackyHorse2 ,
What happens if you rename your model to ' u2net-ne1
' instead and try reloading it into triton?
Hi SmugSnake6 , can you please elaborate on what exactly is happening and what you were expecting to happen?
can you try reinstalling clearml-agent
?
Hi TartBear70 ,
You can use the following method:
https://clear.ml/docs/latest/docs/references/sdk/task/#taskset_random_seed
Please note you need to set it before running Task.init()
If you set it to None
this will cancel any random seed override performed by ClearML.
Tell me if this helps 🙂
WickedBee96 , Hi, which python version are you running with?
Hi HappyDove3 , you mean when using app.clear.ml?
@<1631102016807768064:profile|ZanySealion18> , I think no such capability exists currently. I'd suggest opening a github feature request for this.
Hi, how do you connect your configs currently?
You mean you'd like to be able to connect/create configuration objects via UI?
AlertCrow40 Hi!
How are you trying to connect to your jupyter notebook, can you provide a snippet? What version of clearml are you using?
VexedCat68 , what if you simply add pip.stop()
? Does it not stop the pipeline? Can you maybe add a print to verify that during the run the value is indeed -1? Also looking from your code it looks like you're comparing the 'merged_dataset_id' to -1
you can find the different cache folders that clearml uses in ~/clearml.conf
Can you add a full log of an experiment?
I'm not sure I understand your second request. Can you please elaborate on the exact process you're thinking of?
Clone task via UI -> Edit a config section in UI -> Enqueue it to a queue -> Worker picks it up and starts running the task -> Task is finished
What am I missing here?
Are you using a self deployed server?
How do you currently save artifacts now?
https://app.clear.ml/settings/profile
bottom right of your screen if you're signed up 🙂
I have no idea, but considering that the version for http://app.clear.ml was updated recently (last week from what I noticed) I'd be guessing that the self hosted server should be right around the corner 😉
Hi @<1566596960691949568:profile|UpsetWalrus59> , I think this basically means you have an existing model and it's using it as the starting point.
SmallDeer34 , great, thanks for the info 🙂
Hi RoughTiger69 ,
Have you considered maybe cron jobs or using the task scheduler?
Another option is running a dedicated agent just for that - I'm guessing you can make it require very little compute power
Hi @<1590514572492541952:profile|ColossalPelican54> , I'm not sure what you mean. output_uri=True
will upload the model to the file server - making it more easily accessible. Refining the model would require unrelated code. Can you please expand?
You can read up on the caching options in your ~/clearml.conf
You can make virtualenv creation a bit faster
Hi @<1709015393701466112:profile|ScatteredPeacock14> , you are correct, this feature is available only in the Scale/Enterprise plans.
I'm afraid there isn't anything besides unregistering/re-registering
Hi @<1523701797800120320:profile|SteadySeagull18> , can you please elaborate on what you mean?
Hi @<1559349204206227456:profile|BeefyStarfish55> , you would need to integrate ClearML with k8s for that.
I think this helm chart is what you're looking for
None