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...

ok that's the latest? Will do.

Is there enterprise support for k8s glue on OpenShift?

Create immutable and differentiable versions on-prem or in the cloud with our data agnostic solution.

thanks.

Share data across R&D teams with searchable data catalogs available on any environment.

Hi SuccessfulKoala55 , is there a channel here that posts version updates?

Ok. I noted this is due to the venv_update setting. It needs to be disabled as it has a dependancy on the internet url. We can close this.

Ok sure. Thanks.

i see. Can i take it that when the client uses
task.execute_remotely(queue_name="1gpu", exit_process=True)then none of the content in its clearml.conf will be used, except for the API part. And Clearml simply uses whatever is on the Agent side.
api { # Notice: 'host' is the api server (default port 8008), not the web server. api_server: web_server: files_server: # Credentials are generated using the webapp, `
# Override with os environment: ...

I thought of another potential way but not sure if the SDK supports it.
We will perform manual save and upload of model using vanilla boto3 and credentials passed in as env var. Use ClearML SDK to update the Model Repo on the location of the model, without ClearML uploading it explicitly.Would the above work?

Hi, any advice on this? thanks.

Oh, this meant i have been using the latest agent which is v1.0.0. The problems were still there.

Hi erez, i think i would want to reference the code that transformed the data. Take for example, i received 10k images, i performed some transformation and save it as a next version before i split it up for my ML training. Some time later, i receive a new set of 10k images and wants to apply the same transformation and then append it to the previous 10k as another version. Clearml-data does well for the data-versioning part, but in terms of data provenance, its not clear how i can associate t...

The first is probably done using pipeline controllers, the second using Datasets or HyperDatasets. Its not very clear how the last one is achieved, especially on the searchable data catalogs.

Thought this looked familiar.
https://clearml.slack.com/archives/CTK20V944/p1635323823155700?thread_ts=1635323823.155700&cid=CTK20V944

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.

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

can you please verify that you have all the required packages installed locally ?

Its not installed on the image that runs the experiment. But its reflected in the requirements.txt.

what is the setting of 

agent.package_manager.system_site_packages

True.

Thanks. Which brings me to the question. How does ClearML deal with all the CVEs? What is your process for response?

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, how may i task.init() within these sub processes without write access to the 3rd party scripts and python executables?

Hi, I was expecting to see the container rather then the actual physical machine. For example, in the file panel on the left of the jupyter panel, I see the file contents of the physical machine. I was expecting this to be the container.

Thanks SuccessfulKoala55 , how might I do this clean up? Does this increase with more use of ClearML? And to add, we save all artifacts onto a remote S3 server.

Thanks. This appears to be solely for web UI and API, What if i want to orchestrate on K8S?

Hi, it looks like the entire http://clear.ml domain is offline for more than 12 hours. Main pages and documentation are inaccessible as well.

The problem is resolved by doing a git push. Somehow the git diff didn't capture the difference in requirements.txt in the project. I can't reproduce the same issue after this as well.

Hi, i can't seem to find the source. What are the kind of situations where it will try to install torch outside of user requirements?

Hi AgitatedDove14 , do you mean the configuration tab in the UI? No, i don't see it.

Hi i missed this. Is there a recording?