Dynamic GPU option only available with Enterprise version right?

Correct 🙂

MagnificentPig49 is jsonargparse popular, should we fix the auto-magic ?

However, are you thinking of including this callbacks features in the new pipelines as well?

Can you see a good use case ? (I mean the infrastructure supports it, but sometimes too many arguments is just confusing, no?!)

LittleShrimp86 what do you have in the Configuration Tab of the Cloned Pipeline?
(I think that it has empty configuration -> which means empty DAG, so it does nothing and leaves)

JuicyDog96 Yes please!
Let me check what's the status with the docs repository, and I'll get back to you soon 🙂

No worries 🙂

like what all are important metric monitoring queries w.r.t. the serving tasks that can be visualized and shown in grafana?

Basically latency amd requests per minute are automatically reported. Additional reports are based on your RestAPI in/out.
Imagine the following restapi request json payload

{x=123, y=456}

and a return json of

{z=789}

The metrics you can add to the monitoring are the keys on both these jsons, i.e. "x", "y", "z"
These metrics can be both log...

Hi VexedCat68
So if I understand correctly, the issue is this argument:
parameter_override={'Args/dataset_id': '${split_dataset.split_dataset_id}', 'Args/model_id': '${get_latest_model_id.clearml_model_id}'},I think that what is missing is telling it this an artifact:
parameter_override={'Args/dataset_id': '${split_dataset.artifacts.split_dataset_id.url}', 'Args/model_id': '${get_latest_model_id.clearml_model_id}'},You can see the example here:
https://clear.ml/docs/latest/docs/ref...

are you using matplotlib ? could it be the binding check if matplotlib exists ? could it be you are running it with DEBUG on (i.e. global log level debug) ?

LOL AlertBlackbird30 had a PR and pulled it 🙂
Major release due next week after that we will put a a roadmap on the main GitHub page.
Anything specific you have in mind ?

AFAICS it's quite trivial implementation at the moment, and would otherwise require parsing the text file to find some references, right?

Yes, but the main issue is the parsing, it needs to have a specific standard. We use HOCON because it is great to read and edit (basically JSON would be a subset of HOCON)

the original pyhocon does support include statements as you mentioned -

Correct, my thinking was to expand them into "@configuration_section.key" or something of that nature

Hi @<1624941407783358464:profile|GrievingTiger47>
I think you should try to contact the sales guys here: None

MagnificentSeaurchin79 making sure the basics work.
Can you see the 3D plots under the Plot section ?
Regrading the Tensors, could you provide a toy example for us to test ?

and clearml version ?

Also, can the image not be pulled from dockerhub but used from the local build instead?

If you have your docker configured to pull from local artifactory, then the agent will do the same 🙂 (it is calling the docker command just like you do)

agent.default_docker.arguments: "--mount type=bind,source=$DATA_DIR,target=/data"

Notice that you are use default docker arguments in the example
If you want the mount to always be there use extra_docker_arguments :
https://github.com/...

WickedGoat98 the agent itself can be executed on bare metal, no need to setup a docker for it (although fully supported)
Specifically the docker compose has the docker running in services mode, i.e. for CPU light weight tasks such as running pipelines .
If the agent running on GPU, the easiest way to is run on bare metal

Hi OutrageousSheep60

AS-IS

• without compressing or breaking it up into chunks.

So for that I would suggest to manually archive it, and upload as external link?
Or are you saying you want to control the compression used by Dataset class ?
https://github.com/allegroai/clearml/blob/72d9b22e0d27f317a364acfeacbcf5c70f852e8c/clearml/datasets/dataset.py#L603

no need for it actually

JitteryCoyote63 sure, this is how it was designed to work 🙂

Would I be able to add customized columns like I am able to in 

task.connect

 ? Same question applies for parallel coordinates and all kinds of comparisons

No to both 😞

Hi ReassuredTiger98
Could you add some print ? before / after the artifact upload?
Also what's the clearml version you are using ?

Hi SteadyFox10
I promised to mention here once we start working on ignite integration, you can check it here:
https://github.com/jkhenning/ignite/tree/trains-integration
Feel free to provide insights / requests 🙂

As for the model upload. The default behavior is
torch.save() calls will only be logged , nothing more. But, if you pass to the Task.init output_uri field, then all your models will be uploaded automatically. For example:
` task = Task.init('examples', 'model upload test', o...

Yey! MysteriousBee56 kudos on keep trying!
I'll make sure we report those errors, because this debug process should have much shorter 🙂

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

Hi BitterStarfish58
What's the clearml version you are using ?

dataset upload both work fine

Artifacts / Datasets are uploaded correctly ?
Can you test if it works if you change " http://files.community.clear.ml " to " http://files.clear.ml " ?

Thanks HelpfulHare30 , I would love know know what you find out, please feel free to share 🙂

Bugs, definitely GitHub, this is the easiest to track.
Documentation, if these are small issues, Slack is fine, otherwise, GitHub issue.
Regrading the documentation, we are working on another iteration of improvement, but if you find inaccuracies/broken links please report 🙂

Try to set this line in your clearml.conf to true:
https://github.com/allegroai/clearml/blob/6e6271fb91f2aeb2aa7a13c6d07d4e635baaa670/docs/clearml.conf#L177

I want to be able to install the venv in multiple servers and start the "simple" agents in each one on them. You can think of it as some kind of one-off agent for a specific (distributed) hyperparameter search task

ExcitedFish86 Oh if this is the case:
in your cleaml.conf:
agent.package_manager.type: conda agent.package_manager.conda_env_as_base_docker: truehttps://github.com/allegroai/clearml-agent/blob/36073ad488fc141353a077a48651ab3fabb3d794/docs/clearml.conf#L60
https://git...

I commented the upload_artifact at the end of the code and it finishes correctly now

upload_artifact caused the "failed" issue ?