Hmm I'm assuming something wrong here:
https://github.com/allegroai/clearml-server/blob/a64c4d264d00eadd2d11818b37151d3cc6266d99/docker/docker-compose.yml#L119
What's the host machine OS ?

they are just neighboring modules to the function I am importing.

So I think that is you specify the repo,, on the remote machine you will end with the code of the component sitting at the root folder of the repo, from there I assume you can import the rest, the root git path should be part of your PYTHONPATH automatically.
wdyt?

In Azure VMSS, there is a method called "Custom Data", which is basically a way of passing things to be executed

I know that it is in the to do list to add "azure_autoscaler" which is basically asybling to the aws_autoscaler.
With the same idea of the "custom data" as initial bash script:
You can check here:
https://github.com/allegroai/clearml/blob/4a2099b53c09d1feaf0e079092c9e075b43df7d2/clearml/automation/aws_auto_scaler.py#L54

Actually we just added venv support as well, the reasoning is/was inside a docker it is easier to separate the running processes, with venv we had to support multiple venv running at the same time and reusing of those venv (just a bit more logic) anyhow this is now supported :)

MelancholyElk85 if you are manually adding models OutputModel, then when you call update_weights(...) upload will start in the background (if the process ends it will wait until the upload is competed). You can also specify auto_delete_file which will delete the local copy once the upload completes

PompousBeetle71 , basically reset experiment will clear all the outputs, and input model model is well, input, it is not cleared. In the next execution it will be overridden. There is actually a way to change it from the UI, and override the initial model weights.

BTW: if you make the right column the base line (i.e. move it to the left, you will get what you probably expected)

is the model overridden or its version is automatically increased?

You will have another model, with the same name (assuming the second Task has the same name), but a new ID. So if I understand you correctly, we have auto-versioning :)

HI ResponsiveCamel97
What's the clearml-server version? How do you spin the server on your k8s cluster, helm ?

Can you send the full log as attachment?

Unfortunately this sounds a classic case of RBAC (role based access control), and only the enterprise version has that feature (I think there is a contact us button on the website for those queries).
The easiest way to support the use case you describe is to share on a Task level 😞

Hi @<1523701868901961728:profile|ReassuredTiger98>
The sdk.development.default_output_uri is used for Artifacts and Models. debug samples (or anything else the Logger class creates) will use the api.file_server
On the Task itself, you have the "output destination" (in the Execution tab) which would override the "output_uri" on a Task level
Does that make sense ?

Try:
task.flush(wait_for_uploads=True)Should do the trick 🙂

Debug samples can only be controlled via api.file_server (or programatically)
Model/Artifacts see above

This has no effect. I am not able to change the files_sever, e.g. I can not change from

You are Not changing the files_server just where your Taskj uploads Models/Artifacts, these are two diff things (and again Only applies to Artifacts/Models)

Makes sense, but this means that we are not able to tell clearml-agent where to save on a per-task basis?

The debug samples? or the artifacts/models?

Also it is not possible to use multiple files server? E.g. log tasks on different S3 buckets without changing clearml.conf

Yes, change the Task's output destination in the UI (or programmatically)

Hi MistakenDragonfly51
Notice that Models are their own entity, you can query them based on tags/projects/names etc.
Querying and getting Models is done by Model class:
https://clear.ml/docs/latest/docs/references/sdk/model_model#modelquery_models

task.get_models()

is always empty. (edited)

How come there are no Models on the Task? (in other words how come this is empty?)

Where are they stored? I could not find a backend they work with, what am I missing?

I could take a look and figure that out.

This will greatly accelerate integration 😉

GiganticTurtle0 found it, fix will be pushed tomorrow 🙂

The issue only arises upon sending Images. (Both numpy, mpl and PIL)

BTW: they should appear under debug-samples Tab in the results

You mean to add these two to the model when deploying?

    │   ├── model_NVIDIA_GeForce_RTX_3080.plan
    │   └── model_Tesla_T4.plan

Notice the preprocess.py is Not running on the GPU instance, it is running on a CPU instance (technically not the same machine)

build your containers off these two? or are you building directly from code ?

New RC hopefully solves it @<1643060801088524288:profile|HarebrainedOstrich43> could you check if it works for you now?

pip install clearml==1.14.0rc0

EcstaticGoat95 any chance you have an idea on how to reproduce? (even 1 out of 6 is a good start)

Yes the clearml-server AMI - we want to be able to back it up and encrypt it on our account

I think the easiest and safest way for you is to actually have full control over the AMI, and recreate once from scratch.
Basically any ubuntu/centos + docker and docker-compose should do the trick, wdyt ?

Is there any documentation on versioning for Datasets?

You mean how to select the version name ?

Hi FierceHamster54
I'm this is solvable, get in touch with them either in the contact form on the website or email support@clear.ml , should not be complicated to fix 🙂

Hi LudicrousDeer3
It should not be a problem see iteration argument in Logger.report_scalar
https://github.com/allegroai/clearml/blob/22d795f68f0175ba9511cabd444ea4dba464f3cd/examples/reporting/scalar_reporting.py#L19
https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/logger_module/logger_logger.html?highlight=report_scalar#clearml.logger.Logger.report_scalar

And having a pdf is easier/better than sharing a link to the results page ?

Hi PlainSquid19
Any model stored by TF/Keras/PyTorch/Joblib will automatically appear in the artifact/models tab.
Are you asking on how to add one manually ?