Hi FierceHamster54 , I'm taking a look 🙂

SubstantialElk6 , can you view the dataset in the UI? Can you please provide a screenshot so I can mark it down for you

Might be a bug. Let me check with the guys

FierceHamster54 , please try re-launching the the autoscaler, the issue seems to be resolved now

You can do pip show clearml to see the clearml version

Moving objects between steps is usually done via the artifacts mechanism. How are you building the pipeline, with decorators?

I think it's the same usage as it were a regular task. Did you encounter any issues?

Hi FreshKangaroo33 ,

I think you could use a special hyper parameter for it. This way you can have it show up in the UI as a column. This way it would take any argument you want AND you can filter by it 🙂

Hi TroubledHedgehog16 , I don't think there is any specific documentation regarding this. Basically anything that communicates with the server (UI/SDK/Agent) will cause an increase in these calls.

You could do a test on a free account using your resource to see how many calls you would reach in a peak day.

Can you make sure you can check out this commit on a different machine?

Can you maybe add a video of what you're doing?

Oh I see. Technically speaking the pipeline controller is a task of a special type of itself. So technically speaking you could provide the task ID of the controller and clone that. You would need to make sure that the relevant system tags are also applied so it would show up properly as a pipeline in the webUI.

In addition to that, you can also trigger it using the API also

Hi @<1523701295830011904:profile|CluelessFlamingo93> , if you self host then you're no longer limited on usage. However the downside is of course that you have to manage it yourself (Security, backups etc).

BoredPigeon26 , Hi 🙂

Basically 'group' is based on the "title" of the graph, when TB is autoconnected, this is the first section before the '/' meaning, reporting "my graph title/series" allows to group by "my graph title". How are you reporting the metrics on TB ? Somehow (I'm not sure how with Sagemaker) you have to pass the previously used Task ID, so clearml will know which Task we are continuing Any idea how you could pass this information ? maybe store something on the sagemaker job?

WackyRabbit7 , in that case you can simply register the pretrained model in the system using the SDK 🙂

Hi Tim, how did you raise your server?

Hi CloudySwallow27 ,

I think currently the way to do this is by disabling the framework detection and reporting the debug images manually.

You can do this by Task.init( auto_connect_frameworks=False )

TimelyPenguin76 , what do you think?

Hi ShallowGoldfish8 , what versions of ClearML & ClearML-Agent are you using?

Hi UpsetTurkey67 ,

Is this what you're looking for?
https://clear.ml/docs/latest/docs/references/sdk/trigger#add_model_trigger

I see. The difference is minute but still there

Hi @<1574931891478335488:profile|DizzyButterfly4> , not sure what you mean. Can you elaborate on what you see vs what you expect to see?

When you say local machine you mean you're trying to access the UI / BE from the same machine you're running the server?

Hi I think there are some connectivity issues from some countries. I think it should be back up shortly 🙂

Also in applications I see an option for subnet ID & security group

Hi @<1523707653782507520:profile|MelancholyElk85> , in a section right under the default S3 credentials in clearml.conf you have a section to specify per bucket 🙂

Just making sure - there is a gpu present in the pod, correct?

Hi @<1547028031053238272:profile|MassiveGoldfish6> , do you have any idea what might have caused the project to become hidden?

You can "unhide" the project via API, there is a system tag "hidden" that you can remove to unhide

I think so, yes. You need a machine with a GPU - this is assuming I'm correct about the n1-standard-1 machine

EnormousWorm79 , Hi 🙂

What do you mean by dependency structure?