I used .update_weights(path) with path being the model dir containing the model.py annd the config.pbtxt. Should I use update_weights_package ?

Without some sort of automation on top feels a bit fragile

But I don’t see a task option in Dataset.create

https://github.com/allegroai/clearml/blob/master/clearml/datasets/dataset.py#L657-L663

Also btw, is this supposed to be screenshot from community verison? https://github.com/manojlds/clearml-serving/blob/main/docs/webapp_screenshots.gif

Model says PACKAGE, that means it’s fine right?

pipeline code itself is pretty standard

Got it, makes sense on that part!

With an hash of course 🙂

Ahhhh thanks!

` pipe = PipelineController(
default_execution_queue="minerva-default",
add_pipeline_tags=True,
target_project=pipelines_project,
)

    for step in self.config["steps"]:
        name = self._experiment_name(step)
        pipe.add_step(
            name=name,
            base_task_project=pipelines_project,
            base_task_name=name,
            parents=self._get_parents(step),
            task_overrides...

Hey TimelyPenguin76 - i am just using the helm chart and haven’t done any setup on top of that. the agentservices is running as is from the helm chart

Maybe related to doing in notebook. Doing a task.close() finished it as expected

That’s great, will try it out soon (it’s 2.30am here, about to crash 🙂 )

Doing this with one step - https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_controller.py

This is a Sagemaker notebook instances - Python 3.6.13

Also the pipeline ran as per this example - https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_controller.py

This is the command that is running:

` ['docker', 'run', '-t', '-e', 'NVIDIA_VISIBLE_DEVICES=none', '-e', 'CLEARML_WORKER_ID=clearml-services:service:c606029d77784c69a30edfdf4ba291a5', '-e', 'CLEARML_DOCKER_IMAGE=', '-v', '/tmp/.clearml_agent.72r6h9pl.cfg:/root/clearml.conf', '-v', '/root/.clearml/apt-cache:/var/cache/apt/archives', '-v', '/root/.clearml/pip-cache:/root/.cache/pip', '-v', '/root/.clearml/pip-download-cache:/root/.clearml/pip-download-cache', '-v', '/root/.clearml/cache:/clea...

I just run the k8s daemon with a simple helm chart and use it with terraform with the helm provider. Nothing much to share as it’s just a basic chart 🙂

Is there a good way to get the project of a task?

Essentially - 1. run a task normally. 2. clone 3. edit to have only those two lines.

Question - since this is a task, why is Task.currnet_task() None?

As in I am cloning a task and running it and in that takes without doing any Task.init i am trying to get the task that is running

Why do you think?

It’s a task, it’s running in context of a project, but I don’t have a way to get the project name

(I need this because I refer to datasets in the same project but without specifying the project name)

How can a task running like this know its own project name?

I get other things from the project like the dataset

Great, thanks

It might be better suited than execute remotely for your specific workflow 

Exactly

Is there a published package version for these?

Yeah will use that