hm, you should be able to hit None if docker networking is working properly. it shouldn't need to go through the internet to get back to your machine.

dug deeper. if i'm to make a guess...
/root/clearml.conf -> used on startup of agent-services as a template of sorts to create .clearml_agent.<id>.cfg on demand -> this task-specific file is used to mount to /tmp/clearml_default.conf in a new container (docker in docker bc of the socket mounted to the agent-services) -> used to execute the task

if you commit but do not push, the metadata tells clearml that it needs to pull a non-existant commit. any changes you made on top may be saved as a diff, but they'd fail to apply.

for clearml to work on un-pushed commits, it'd have to wait for a push to register a new diff target, which can become a problem (what if you have multiple remotes? which one will it wait for?) so rather, it assumes it can access the most recent commit from your remote repo, and records this as the "base" upon whi...

didnt disappear this time.

you could also take the route of NOT specifying num_layers, and instead write your own code to create a set of viable layer designs to choose from and pass that as a parameter, so optuna selects from a countable set instead of suggesting integer values .

the downside of this is the lack of gradient information in the optimization process

tasks that create pipelines feels like a hack and i found they dont show up in the UI (have to use the link in the console).

I've found that sometimes i need to right click "Run" a couple of times before the parameters are filled in properly.

the project wasn't hidden before. I'm aware of the pipeline tasks being hidden, that makes sense for organization. but the actual project itself as an entirety has a ghost icon.

she created a new project and started working in there, it was visible in the UI... and just now it disappeared again. it's kind of like running the pipeline makes it disappear.

credentials for the server to do things with s3 will be in /opt/clearml/apiserver.conf.

I think you’d have to run the cleanup service. That’s what seems to be what is controlling deletion based on archived status and some other temporal filters

the clearml github, search for a file named cleanup service dot py (or something to that effect)

Might be under examples

Weird . I recently implemented a function that talked to this exact endpoint and found it had to exclude the version and api paths . Is there some sort of redirect that happens?

my approach was to spin up an EC2 and run the deployment there from within the EBS volume mount.

I symlinked /opt/clearml to /mnt/xvda/clearml to minimize docker-compose changes. been working out fine so far.

with aws-cdk, the deployment steps can be automated (format the volume, clone a repo with the config, etc). I can link you to a resource that may help with that if you're interested.

Oh neat! I want to take a look at this. Only a few more weeks at the client but it’d be nice to reduce the complexity of the software stack if I can before handoff.

Can you please elaborate on the latter point? My jupyterhub’s fully containerized and allows users to select their own containers (from a list i built) at launch, and launch multiple containers at the same time, not sure I follow how toes are stepped on.

Oh yes. I see. Yeah, no ML here actually (doing the testing infra of endpoints), but certainly when there is its an issue.

How does clearml session avoid it? I guess only if autoscaling is used (one worker one machine)?

I'm guessing this is done through code-server?

I'm currently rolling a JupyterHub instance (multiuser, with codeserver inside) on the same machine as clearml-server. That’s where tasks are executed etc. so, all browser dev env.

It sounds like there’s an option to basically bypass this latter step and just use clearml’s credentialing to accomplish much the same thing? Am I understanding clearml-session correctly?

@<1798162804348293120:profile|FlutteringSeahorse49> wants to start HPO though, so the desire is to deploy agents to listen to queues on the slurm cluster (perhaps the controller runs on his laptop).

would that still make sense?