I’d really prefer it was modular enough to use serving with any model registry

Oh that's interesting. To serve a model from MLflow, would you have to copy it over to ClearML first?

You have no idea what is committed to disk vs what is still contained in memory.

If you ran docker-compose down and allowed ES to gracefully shut down, would ES finish writing everything to disk, therefore guaranteeing that the backups wouldn't get corrupted?

I could potentially write a selenium script to make a set of keys, but I'd prefer to avoid that 😅

But the extension will need credentials to connect to it.

https://share.descript.com/view/g0SLQTN6kAk

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.

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

I don't know that you'd have to pre-build credentials into docker. If you could specify a set of credentials as environment variables to the docker run ... command or something, that would work just fine.

The goal is to be able to run docker-compose up in CI, which starts a clearml-server. And then make several API calls to the started ClearML server to prove that the VS Code extension code is working.

Examples:

• Assert that the extension can auth with ClearML
• Assert that the ext...

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. ...

If the load balancer it Gateway can do the computation and leverage caching, we’re much safer against DDOS attacks. In general, I’d prefer not to have our EC2 instance directly exposed to the public Internet.

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

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.

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.

Here's a docker-compose I've been playing with. It doesn't have the same restart problem you're describing, but I did change the volume mounts: None

Yay! Man, I want to do ClearML with "hard mode" (non-enterprise, self-hosted) first, before trying to sell BENlabs (my work) on it. I could see us paying for enterprise to get the Hyper Datasets and Vault features if our scientists/developers fall in love with it--they probably will if we can get them to adopt it since right now we have a homemade system that isn't nearly as nice as ClearML.

@<1523701087100473344:profile|SuccessfulKoala55> how exactly do you configure ClearML to use the cr...

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?

Actually that's wrong: really this is the current volume mount

'-v', '/tmp/clearml_agent.ssh.cbvchse1:/.ssh',

Could changing these values to /root/.ssh work? Do you know what use within the docker image ClearML is using?

And for the session

clearml-session --queue sessions --docker python:3.9

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.

Hi! Yes, it just logs the .py file.

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...

you mean as experiment management / model registry / data? I think this is the bread&butter of clearml

💯 . I was wondering if anyone had had experience using ClearML together with one of these others.

I think most of them are alternatives to metaflow

Totally.

Like, if you google "dagster and clearml" or "prefect and clearml" or "airflow and clearml" -- I don't find any blogs written by people talking about how they use both of them together.

That's strange to me, becau...

As an infrastructure engineer, I feel that this is a fairly significant shortcoming of ClearML.

Having the ability to pack jobs/tasks onto the same "resource" (underlying server/EC2 instance) would

• simplify the experience for data scientists
• open up a streaming use case, wherein batch (offline) inference could be done directly inside of a ClearML pipeline in reaction to an event/trigger (like new data landing in your data lake). As it is, you can make this work, but if you start to get ...

I'll try to describe the scenario I was thinking would cause ClearML to break down:

Assume:

• We've got a queue called streaming
• We've got an S3 bucket with images landing inside
• When the images land, they go into a queue
• When there are 100 images in the queue, we trigger a ClearML pipeline to ingest, transform, run inference on the batch, and then write the results somewhere
• Let's say we get 1,000,000 images in the Bucket per hour. That might be 1,000,000 / 100 = 10,000 batches. ...

configurations:
  extra_clearml_conf: ""
  extra_trains_conf: ""
  extra_vm_bash_script: |
    aws ssm get-parameter --region us-west-2 --name /clearml/github_ssh_private_key --with-decryption --query Parameter.Value --output text > ~/.ssh/id_rsa && chmod 600 ~/.ssh/id_rsa
    source /clearml_agent_venv/bin/activate

hyper_params:
  iam_arn: arn:aws:iam::<my account id>:instance-profile/clearml-2-AutoscaledInstanceProfileAutoScaledEC2InstanceProfile56A5348F-90fmf6H5OUBx

I'm not seeing a extra_docker_shell_script in my clearml.conf generated by clearml-agent init like in this guide

Well wow, I figured it out. You equiped me with a solid debugging tool AKA running bash commands within the docker container.

I had to pre-add GitHub and Bitbucket to known hosts by adding keyscan commands

configurations:
  extra_clearml_conf: ""
  extra_trains_conf: ""
  extra_vm_bash_script: |
    echo "fetching github key" && (aws ssm get-parameter --region us-west-2 --name /clearml/github_ssh_private_key --with-decryption --query Parameter.Value --output text > ~/.ssh/id_rsa &...

We should put a $100 bounty on a bash script that backs up and restores mongodb, redis, and ES, etc. to S3 using the most resiliant ways 😄

Let's see. The screenshots above are me running on the host, not attaching to a running container. So I believe I do want the keys to be mounted into the running containers.

Is there some way we could programmatically list all current ClearML sessions?

We need a way to do that, maybe with the clearml-session CLI in order to populate the VS Code extension menu.