BoredPigeon26 , when you copy and paste the link provided to you by the UI into the browser, can you see the image?
I think this is because you're working on a "local" dataset. Only after finalizing the dataset closes up. Can you describe your scenario and what was your expected behavior?
Hi RattyLouse61 , how are you adding users? Are you adding them as fixed users in one of the configuration files?
Also, I'm not sure I understand exactly what you're expecting to get and what you're getting
UnevenDolphin73 , I think I might have skipped a beat. Are you running the autoscaler through the code example in the repo?
Hi SmugSnake6 , can you add a small snippet to reproduce?
Hi UnevenDolphin73 , can you please elaborate on what do you mean by what CPU does the queue consume?
You can pull all machine usage statistics using the API. Is there something specific you're looking for?
Hi @<1556812486840160256:profile|SuccessfulRaven86> , can you please add an example configuration that reproduces this?
btw what os are you on?
Also, what happens if you apss it in agent.default_docker.arguments ?
Hi SuperiorCockroach75 , please look here:
https://clear.ml/docs/latest/docs/references/sdk/dataset#datasetget
To solve the issue simply specify the alias.
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 UnevenDolphin73 ,
I think you need to lunch multiple instances to use multiple creds.
Hi FierceHamster54 , I think it should install it correctly. Did you have a different experience?
Setting the upload destination correctly and doing the same steps again
I think same as Task.init(..., output_uri=False) but give it a try 🙂
Hi @<1638712150060961792:profile|SilkyCrocodile89> , can you please add the log and an example of a failure and of it working?
Can you please paste the response from events.debug_images ?
Can you check the machine status? Is the storage running low?
Can you post a minimal example here? Does this always happen or only sometimes? Also how is the pipeline run? Using autoscaler or local machines?
Hi @<1652120623545061376:profile|FrightenedSealion82> , do you see any errors in the apiserver or the webserver containers?
Oh I misunderstood. It fails when you have the folder in the output_uri but runs all fine when it's without 🙂
Then it's the community server, that is not an enterprise version. In the PRO version only AWS/GCP autoscalers are available.
Hi @<1714813627506102272:profile|CheekyDolphin49> , can you provide a couple of snippets that reproduce this behaviour?
And also exactly what command line you used to run the agent?
You will need to find the appropriate docker image with the python version you're looking for.
or add requirements manually via code