Oh interesting. Is the hope that doing that would somehow result in being able to use those credentials to make authenticated API calls?

That's fabulous. This is definitely how my team prefers to structure projects. I hadn't gotten around to trying that out in our POC of ClearML yet, but I'm certain this is how our group will solve this problem

While I'm wishing for things: it'd be awesome if it had a queue already set up. But if there's not a way to do that in the docker compose file, I could potentially write a script that uses the creds to create one using API calls

Wow, it really does not want to show the output of those print statements in stdout. Here's the output of the task from the console after cloning it. Confirmed that the setup script and all code changes are present:

This is totally what I was looking for! Yeah, by "good story for offline batch" I meant, "good feature support for ..."

I bookmarked this comment. I think I'll be doing a POC trying to show this functionality within the next month.

Playing around this weekend to learn the ins and outs of extensions

Does this mean that none of the credientials in this file can be used with the clearml SDK when the docker-compose.yaml starts up with a fresh state?

Is there anyway to achieve such a behavior? Or are manual steps simply required to get a working set of keys. I'm trying to prepare a docker-compose file that I can use for automated tests of our VS Code extension.

The issue went away. I'm still not sure why, but what finally made it work was creating a set of credentials manually in the UI and then setting those in my ~/clearml.conf file.

Do you happen to have a link to a docker-compose.yaml file that has a hardcoded set of credentials?

I want to seed the clearml instance with a set of credentials and ~/clearml.conf to run automated tests.

How it works / what we finished:

• We used the SaaS ClearML, started an EC2 instance, and manually installed and ran the clearml-agent daemon on it
• We ran clearml-init on our laptops to generate the clearml.conf file.
• The extension is in TypeScript, so...
• We started trying to write code with the Python SDK to list sessions, but realized calling that from the extension would be hard, so we opted to have the TypeScript code make calls to the ClearML API server directly, e.g. ...

That could work! Is that an option? Something that lets me spin up the ClearML and get a services worker to connect to it without manual steps.

Aren't they two different auth systems? One for humans and one for machines?

I can't think of any changes we might have made on our side to cause that 🤔

This is a low-key open-source project if anyone wanted to contribute. Since the project is early, there are lots of high-impact things, e.g. UI polish, that would be relatively low effort 😄

Is there a way we can protect a ClearML deployment with a load balancer or API Gateway that is exposed to the whole world, but is protected by authentication so that only authorized clients can get in?

Exactly

Duh! I bet VS Code's Python extensions like the VS Code Black Extension would be a really good starting place. They are small and are wrappers around a Python CLI tool. I bet there's a lot we could adapt for the ClearML CLI

Oh awesome @<1523701132025663488:profile|SlimyElephant79> ! If you want to take a look, I made a big list of things to add. I'm working on a docker-compose.yaml file so we can have a good local development environment.

There's a lot of room to improve this from cleaning up the code to adding features on the list.

None

I took a look

• I think the Outerbounds extension (the one in my screenshot) is currently closed source. That makes sense to me. A bit sad because it is highly similar.
• Another example could be the AWS ToolKit extension. But sadly, it's hardly a "minimal example". I was thinking it's relevant because it uses your local ~/.aws/ folder, which is similar to what we'd want to do.

Caching can be a reason. Say you do some heavy data loading / processing in step 1. Now you're developing step 2.

It'd be nice not to have to re-run Step 1 every time you want to test a change to step 2.

You could find a way to simply write your output of step1 to disk and do everything in one step, or you could let ClearML handle that caching for you--with the added benefit that others collaborating remotely can also use the outputs of steps you've cached with ClearML

possibly cheaper on the cloud (Lambda vs EC2 instance)

Whoa, are you saying there's an autoscaler that doesn't use EC2 instances? I may be misunderstanding, but that would be very cool.

Maybe I should have said: my plan is to use AWS StepFunctions where a single task in the DAG is an entire ClearML pipeline . The non-ClearML steps would orchestrate putting messages into a queue, doing retry logic, and triggering said pipeline.

I think at some point, there has to be some amount of...

@<1594863216222015488:profile|ConvincingGrasshopper20> throwing this out there... would you want to make this with me at the Hackathon??

Thanks Vasil! Can you elaborate on what you mean by using boto3? Do you mean writing a script using boto that pulls the credentials down and writes to the user's clearml.conf

Also, I've been seeing references to "credentials vault" in the docs. I can see this is the problem that it solves.

I symlinked

/opt/clearml

to

/mnt/xvda/clearml

Genius! I don't think I accounted for making sure the volumes ended up in the EBS volume mount in this CDK example ^^^. And I modified the docker-compose.yml file to point at a different location. Sym-linking is totally the route I should take if I get time to come back and clean up this repo.

Dang! @<1590514584836378624:profile|AmiableSeaturtle81> awesome answer thank you! You seem like an awesome person to know. Definitely connect if you'd like to talk ops stuff sometime. None

I'm trying to add a docker-compose.yaml to the repo to

• make it more convenient for contributors to develop locally
• spin up a local ClearML instance in CI to run automated tests
  Here's the docker-compose file (mostly the standard file, except I altered the volume mounts, and I added minIO)
  Here's [the clearml.conf file](https://github.com/mlops-club/vscode-clearml-sessi...

I've also tried running a clearml-agent daemon directly on my mac (not in docker) serving the sessions queue for the ClearML server that is running in docker. When I do that, it consistently fails with a different error. Something to do with mounting a volume.

Hey! Sorry, I don't think I ever solved this for elasticsearch 😕

I think it will work. There's a lot of really useful code in the black extension. I'm recruiting people now to join in on Friday. I'm actually very confident about it after messing around.

The dark theme you have

It's this chrome extension ! I forget it's even on sometimes. It gives you a keyboard shortcut to toggle dark mode on any website. I love it.

Success! Wow, so this means I can use ClearML training/inference pipelines as part of AWS StepFunctions!

My plan is to have a AWS Step Functions state machine (DAG) that treats running a ClearML job as one step (task) in t...