this

Increased to 20, lets see how long will it last 🙂

(it works now, with 20 GB)

you can use pgrep -af "trains-agent"

So prior to doing any work on the trains autoscaler servcice, I should first create a auto scaling group in AWS?

So once I enqueue it is up? Docs says I can configure the queues that the auto scaler listens to in order to spin up instances, inside the auto scale task - I wanted to make sure that this config has nothing to do to where the auto scale task was enqueued to

Trains docs have at no point any mention on what should I do on the AWS interface... So I'm not sure at what point I should encounter this wizard

I'm going to play with it a bit and see if I can figure out how to make it work

how do I run this wizard? is this wizard train's or aws's?

I jsut think that if I use "report_table" I might as well be able to download it as CSV or something

sorry I think it trimmed it

I executed it now with full path, using $(pwd)

👍 thanks (again)

the pipeline was running

CostlyOstrich36

` name: XXXXXXXXXX

on:
workflow_dispatch

jobs:
test-monthly-predictions:
runs-on: self-hosted
env:
DATA_DIR: ${{ secrets.RUNNER_DATA_DIR }}
GOOGLE_APPLICATION_CREDENTIALS: ${{ secrets.RUNNER_CREDS }}
steps:
# Checkout
- name: Check out repository code
uses: actions/checkout@v2

  # Setup python environment
  - name: Setup up python environment using Poetry
    run: |
      /home/elior/.poetry/bin/poetry env use python3.9
  ...

Do you need other part?

` # define pipeline
pipe = clearml.PipelineController(
name=TASK_NAME,
project=PROJECT_NAME,
version='0.0.1',
add_pipeline_tags=False,
)
pipe.set_default_execution_queue('default')

Adding steps

pipe.add_step(name=f'{start_date_train}_{end_date_train}_choose_best',
base_task_project=CHOOSE_PROJECT_NAME,
base_task_name=CHOOSE_TASK_NAME,
parameter_override=params_override,
...

ClearML results page: `
Launching step: 2019-09-03_2021-01-25_choose_best
Parameters:
{***}
Configurations:
None
Overrides:
None
Launching step: 2019-10-23_2021-01-15_choose_best
Parameters:
{********}
Configurations:
None
Overrides:
None
Launching step: 2019-05-26_2020-12-26_choose_best
Parameters:
{******}
Configurations:
None
Overrides:
None
Launching step: 2019-07-15_2021-01-05_choose_best
Parameters:
{************}
Configurations:
None
Overrides:
None
Launching step...

queue looks empty

if I set it to 24 does it explode?

after you create the pipeline object itself , can you get Task.current_task() ?

AgitatedDove14 no I can't... Just checked this. This is a huge problem for us, it used to work before and it just stopped working and I can't figure out why.

It's a problem for us because we made it a methodology of running some tasks under a pipeline task and saving summary iunfo to the pipeline task - but now since Task.current_task() doesn't work on the pipeline object we have a serious problem

AgitatedDove14 I really don't know how is this possible... I tried upgrading the server, tried whatever I could

About small toy code to reproduce I just don't have the time for that, but I will paste the callback I am using to this explanation. This is the overall logic so you can replicate and use my callback

From the pipeline task, launch some sub tasks, and put in their post_execute_callback the .collect_description_tables method from my callback class (attached below) Run t...

Okay so at the first part of the code, we define some kind of callback that we add to our steps, so later we can collect them and attach the results to the pipeline task. It looks something like this

` class MedianPredictionCollector:
_tasks_to_collect = list()

@classmethod
def collect_description_tables(cls, pipeline: clearml.PipelineController, node: clearml.PipelineController.Node):

    # Collect tasks
    cls._tasks_to_collect.append(node.executed)

@classmethod...

but I don't really know

AgitatedDove14

checking and will let you know

I'll share some screenshots

when using the first checkout action, it goes checks out the repo

I see it's a plotly plot, even though I report a matplotlib one

Good, so if I'm templating something using clearml-task (without queue, so the task is in draft mode) it will use this task? Even though it never exeucted?