Do you mean by this that you want to be able to seamlessly deploy models that were tracked using ClearML experiment manager with ClearML serving?

Ideally that's best. Imagine that i used Spacy (Among other frameworks) and i just need to add the one or two lines of clearml codes in my python scripts and i get to track the experiments. Then when it comes to deployment, i don't have to worry about Spacy having a model format that Triton doesn't recognise.

Do you want clearml serving ...

clearml-serving does not support Spacy models out of the box among many others and that Clearml-Serving only supports following;
Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
Support Deep Learning Models (Tensorflow, PyTorch, ONNX).
An easy way to extend support to different models would be a boon.

I believe in such scenarios, a custom engine would be required. I would like to know, how difficult is it to create a custom engine with clearml-serving? For example, in this...

Hi, please correct me if i am wrong, to use the glue, i need the following.
A k8s cluster A kubectl that is connected to the k8s cluster A pip install of clearml-agent 0.17.1
So i did all the above, I'm not what it meant by running the entire thing on own machine.

what feature on this paid roadmap are you referring to? I am indeed communicating with Noem on paid features.

Yeah.. issue is ClearML unable to talk to the nodes cos pytorch distributed needs to know their IP. There is some sort of integration missing that would enable this.

Hi. nice read. Your permalink is wrong though, here's the right one.
https://cpatrickalves.com/mlops-what-it-is-and-why-does-it-matter

This is a env var?
CLEARML_CONFIG_FILE

Here's my two cents worth.
I thought its really nice to start off the topic highlighting 'pipelines', its unfortunately one of the most missed component when ppl start off with ML work. Your article mentioned about drfits and how MLOps process covered it. I thought there are 2 more components that was important and deserves some mention.Retraining pipelines. ML engineers tend not to give much thought to how they want to transit a training pipeline in development to a automated retraining pipe...

thanks GrumpyPenguin23 , i'll look deeper on that. This kinda fits what i am looking for but its for TRAINS and there's no technical how-to.
https://clear.ml/blog/stop-using-kubernetes-for-ml-ops/

In the ClearML config that's being run by the ClearML container?

Hi CostlyOstrich36 , What you described is task. I was referring to the pipeline controller.

Yes I am.

For example, it would useful to integrate https://github.com/whylabs/whylogs#features into ClearML as part of data and model monitoring. WhyLogs would have their own static page that would preferably be displayed as a new custom tab (besides logs, scalars and plots.).

Likely network. Can you run a curl on ClearML server api server from jenkin stage and see if that gets through?

This would be solved if --env GIT_SSL_NO_VERIFY=true is passed to the k8s pod that's spawned to run the job. Currently its not.

Hi thanks. How about Agent, does its docker mode or k8s mode require docker.sock to be exposed?

Alright i will try that.

Sorry i forgot to paste the logs.

We are deploying ClearML Server via the docker-compose.

For ClearML-Agent. We have the choice of Docker or K8S preferred (Using the Glue).
For K8S, we can't get the glue to work ( https://clearml.slack.com/archives/CTK20V944/p1614525898114200?thread_ts=1613923591.002100&cid=CTK20V944 ) so we can't make an assessment of whether it actually works for us.

Hi we did a check. Only 7.16.1 and 6.8.21 and above mitigates the attack. What's the current version that ClearML is using?

Hi, Self-hosted using docker-compose.

so the clearml-agent daemon needs higher privilege?

It's a local deployment. I was only presented with username without a need to enter passwords. When I'm in, I don't see an option in my profile to set a password as well. Neither is there integration with ldap for example.

We are running on a 1gbps backend.

Thanks 👍 . Should i create an issue on Github?

Hi, for both of them,  args.lastiter  is the exact same value. But when plotted out, they are 2 actually iterations apart.

It has always been there.

I used nvcr pytorch image and instruct clearml to inherit global dependencies. No need to install torch and work well.

Hi,
It did, nvidia/cuda:10.1-runtime-ubuntu18.04.

So if i need to set this every time, what is the following config for? And how do i pass in new env parameters?
` default_docker: {
# default docker image to use when running in docker mode
image: "dockerrepo/mydocker:custom"

    # optional arguments to pass to docker image
    # arguments: ["--ipc=host", ]
    arguments: ["--env GIT_SSL_NO_VERIFY=true",]
} `

yes its on purpose, each user would have their own AWS credentials for default_output_uri.