ZanyPig66 this should have worked, any chance you can send the full execution log (in the UI "results -> console" download full log) and attach it here? (you can also DM it so it is not public)

I'm trying to queue a task in python but I'd like to reuse the prior task ID.

is it your own Task? i,,e, enqueue yourself, if this is the case use task.execute_remotely it will do just that.
If this is another Task, then if it is aborted then you can just enqueue it, by definition it will continue with the Same Task ID.

Hi CheekyAnt38

However now I would like to evaluate directly my machine learning model via api requests, directly over clearml. It’s possible?

This basically means serving the model, is this what you mean?

I always have my notebooks in git repo but suddenly it's not running them correctly.

What do you mean?

Can I switch off git diff (change detection?)

Yes, Task.init(..., auto_connect_frameworks={"detect_repository": False})

Hi @<1729309120315527168:profile|ShallowLion60>

Clearml in our case installed on k8s using helm chart (version: 7.11.0)

It should be done "automatically", I think there is a configuration var in the helm chart to configure that.
What urls are you urls seeing now, and what should be there?

Hi @<1729309131241689088:profile|MistyFly99>
notice that the files server need to have an "address" that can be accessed from the browser, data is stored in a federated manner. This means your browser is directly accessing the files server, not through the API server, I'm assuming the address is not valid?

I think I was not able to fully express my point. Let me try again.
When you are running the pipeline Fully locally (both logic and components) the assumption is this is for debugging purposes.
This means that the code of each component is locally available, could that be a reason?

Fix was pushed to GitHub 🙂

Okay this is a bit tricky (and come to think about it, we should allow a more direct interface):
pipe.add_step(name='train', parents=['data_pipeline', ], base_task_project='xxx', base_task_name='yyy', task_overrides={'configuration.OmegaConf': dict(value=yaml.dump(MY_NEW_CONFIG), name='OmegaConf', type='OmegaConf YAML')} )Notice that if you had any other configuration on the base task, you should add them as well (basically it overwrites the configurati...

Correct, (if this is running on k8s it is most likely be passed via env variables , CLEARML_WEB_HOST etc,)

But these changes haven’t necessarily been merged into main. The correct behavior would be to use the forked repo.

So I would expect the agent to pull from your fork, is that correct? is that what you want to happen ?

And this is with the latest pycharm plugin 1.1.0 ?

kubectl get pods -n {namespace} -o=JSONWhat are you getting when running the above on your cluster ?

Any chance you can zip the entire folder? I can't figure out what's missing, specifically "from config_files" , i.e. I have no packages nor file named config_files

LOL totally 🙂

Hi SarcasticSparrow10

which database services are used to...

Mongo & Elastic
You can query everything using ClearML interface, or talk directly with the databases.
Full RestAPI is here:
https://clear.ml/docs/latest/docs/references/api/endpoints
You can use the APIClient for easier pythonic interface:
See example here
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py

What is the exact use case you have in mind?

okay let's PR this fix ?

Hi @<1523702000586330112:profile|FierceHamster54>
Nope 🙂 nothing to worry about.
That said do notice the open-source file-server is not secure, this does not mean it will spill data on the server, but it does mean that you should probably put it behind a VPN or use S3/GCP/Azure if this is open to the public internet

However I'm quite confident, that plots and scalars are not visible online only when 'git diff to large to store' appears.

These should be unrelated, are you seeing console outputs ?

Hi TrickySheep9
You should probably check the new https://github.com/allegroai/clearml-server-helm-cloud-ready helm chart 😉
https://github.com/allegroai/clearml-server-helm-cloud-ready

Hi FreshBat85
clearml_agent: ERROR: 'utf-8' codec can't decode byte 0xfc in position 38: invalid start byteThis is a notorious issue with python and UTF-8/Unicode support.
Any chance there is "unicode"/utf8 code in the uncommitted changes section ?

BTW you can set an environment variable before spinning the agent, telling it always to use UTF8
set PYTHONUTF8=1

I think that clearml should be able to do parameter sweeps using pipelines in a manner that makes use of parallelisation.

Use the HPO, it is basically doing the same thing with some more sophisticated algorithm (HBOB):
https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py

For example - how would this task-based example be done with pipelines?

Sure, you could do something like:
` from clearml import Pi...

I think that what you need is the triggers, check this one:
https://clear.ml/docs/latest/docs/references/sdk/trigger

Task.init should be called before pytorch distribution is called, then on each instance you need to call Task.current_task() to get the instance (and make sure the logs are tracked).

Hi UnevenOstrich23

if --docker is enable that will means every new experiments will be executed into dedicated agent worker containers?

Correct

I think the missing part is how to specify the docker for the experiment?
If this is the case, in the web UI, clone your experiment (which will create a draft copy, that you can edit), then in the Execution tab, scroll down to the "base docker image" and specify the docker image to use.
Notice that you can also add flags after the docker im...

Is there a way to filter a experiments in a hyperparameter sweep based on a given range of a parameter/metric in the UI

Are you referring to the HPO example? or the Task comparison ?

SmallAnt76
see https://clear.ml/pricing/ , under "What plan should I choose?"
what you are looking for is the first column "open-source". make sense ?

DilapidatedDucks58 I think they are used here:
https://github.com/allegroai/clearml/blob/3d3a835435cc2f01ff19fe0a58a8d7db10fd2de2/clearml/storage/helper.py#L1407

https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session.resource

SteadyFox10 TRAINS_CONFIG_FILE or CLEARML_CONFIG_FILE

LOL, no worries 🙂