Badges 121 × Eureka!
ok nice, so I updated to 1.6, and am now getting the following error when creating a dataset project in the cli using clearml-data create :
clearml-data - Dataset Management & Versioning CLI
Creating a new dataset:
Error: 'str' object has no attribute 'radd'
Ok thanks! show hidden projects actually allows me to see all of the missing datasets, but they are all in the same project, why do I need to enable it?
ok, a great change, but why are they empty for me? 🙂
you recommend just saving the dataset id as part of the task configuration? I think I was a bit unclear, my question is how should I report them from the code, they are not caught automatically because they are custom parameters I calculate not as part of any framework, so I wonder if I should report them as artifacts, or maybe scalars? my issue with scalars is that I only have 1 of each type, and the API seems to be oriented toward a series of results of the same type
Thanks, found it, but yeah, it would be a lot more convenient if you can add it to regular right click menu the same way it is with models and tasks
ok great, I think I'll stick with 1.5 for now and wait for the official release, no rush, thanks!
clearml-data create --project "PROJECT" --name "NAME"
and yes I meant the AMI, in your docs you recommend to use the old one until you post a new one, but the old one is no longer available.
Whatever is simpler, we are researching medical data so I can't use your hosted server, I need it to be inside our VPC in AWS. I was thinking either an EC2 instance with/out docker or use AWS ECS
ahh just use one of the community ones?
(I tested this, switched it off and the datasets disappear, switch it on and they appear)
Ok it works, but I don't see the labels in the model output, is there a way for me to use OutputModel to update those labels?
Thanks, I use these methods in my code, but what I'm trying to do now is download the model using cli, meaning running some command from cmd (or more accurately from Dockerfile) so I can create an image with the model. Whats the recommended way of doing that?
I would like to recreate an experiment after saving its configurations, to do that that I need to load those configurations in another notebook, right now the only way I managed to do that is by saving those configurations as an artifact and load that artifact, but it is less convenient than loading a configuration.
yes we saved those in the hyper parameters