Okay this is a bit hacky but will work

@PipelineDecorator.component(...)
def step(...)
  import sys
  import os
  sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)), "projects", "main" ))
  
  from file import something

Exactly! nice 🎉

It seems like the naming Task.create a lot of confusion (we are always open to suggestions and improvements). ReassuredTiger98 from your suggestion, it sounds like you would actually like more control in Task.init (let's leave Task.create aside, as its main function is Not to log the current running code, but to create an auxiliary Task).
Did I understand you correctly ?

SubstantialElk6 could you try with the latest (just released)?
pip install clearml-agent==0.17.2Then if possible, could you attach the full log of the agent's execution (Task->results->Console)

Why can I only call 

import_model

Actually creates a new Model object in the system
InputModel(id) will "load" a model based on the model id
Make sense ?

I get the URL to the checkpoint/weights

Is it a valid URL ?
GrievingTurkey78 Do you have there http:// or is it file:// ?

I think that what happened was you are running it on the host machine (not inside the docker)
I probably missed a " somewhere

Hmm yeah I can see why...
Now that I think about it, at least in theory the second process that torch creates, should inherit from the main one, and as such Task.init is basically "ignored"
Now I wonder why your first version of the code did not work?
Could it be that we patched the argparser on the subprocess and that we should not have?

Hmm I would recommend passing it as an artifact, or returning it's value from the decorated pipeline function. Wdyt?

👍

Sure, venv mode

I get gaps in the graphs.
For example, the first time I run, I create a task and run a loop:

Hi SourOx12
Is this related to this one?
https://github.com/allegroai/clearml/issues/496

HighOtter69 I was able to change the color individually without an issue. What's your clearml-server ? are you using the community server ?

🙏

Maybe WackyRabbit7 is a better approach as you will get a new object (instead of the runtime copy that is being used)

Oh, did you try task.connect_configuration
?
https://allegro.ai/docs/examples/reporting/model_config/#using-a-configuration-file

CooperativeFox72 of course, anything trains related, this is the place 🙂
Fire away

Hi FunnyTurkey96
Which pip are you using, basically pip changed the dependency resolver after 20.1
Change: https://github.com/allegroai/clearml-agent/blob/aede6f4bac71c8fc56e7cf982318a48527953a3c/docs/clearml.conf#L57
pip_version: "<20.2"See if that helps

And it is not working ? what's the Working Dir you have under the Execution Tab ?

It completed after the max_job limit (10)

Yep this is optuna "testing the water"

DefiantCrab67
It will always install it on any environment

CostlyOstrich36 did you manage to reproduce it?
I tried conda w/ python3.9 on a clean Windows VM , and it worked as expected ....

Hi SteadyFox10 , this one will get all the last metric scalars
train_logger.get_last_scalar_metrics()

Hi @<1544853721739956224:profile|QuizzicalFox36>
Sure just change the ports on the docker compose

HealthyStarfish45 the pycharm plugin is mainly for remote debugging, you can of course use it for local debugging but the value is just to be able to configure your user credentials and trains-server.
In remote debbugging, it will make sure the correct git repo/diff are stored alongside the experiment (this is due to the fact that pycharm will no sync the .git folder to the remote machine, so without the plugin Trains will not know the git repo etc.)

Is that helpful ?

Assuming this is a followup on:
https://clearml.slack.com/archives/CTK20V944/p1626184974199700?thread_ts=1625407069.458400&cid=CTK20V944

This depends on how you set it with the clearml-serving --endpoint my_model_entry
curl <serving-engine-ip>:8000/v2/models/my_model_entry/versions/1

Hi RoughTiger69

How about using the pipeline decorator as a way to run this logic?
https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py

I think I'm missing the context of where the code is executed....

btw: you can now set the configuration_objects directly when calling add_step 🙂
https://clearml.slack.com/archives/CTK20V944/p1633355990256600?thread_ts=1633344527.224300&cid=CTK20V944

DepressedChimpanzee34 what would be easier curl or python ?

Makes total sense!
Interesting, you are defining the sub-component inside the function, I like that, this makes the code closer to how this is executed!

Hi EcstaticPelican93
Sure, the model deployment itself (i.e. the serving engine) can be executed on any private network (basically like any other agent)
make sense ?