where people can do @'s for experiments/projects/tasks and even comparisons ...

ohhh I like that! for me this throws me directly to Slack integration .
I think my main question is, "is the discussion ephemeral?" in other words, is this an on going discussion that later no one will care about, or are we creating some "knowledge base" that we want to later share?

Also, by "address bar at the top", i assume you mean address url right?

yes... apologies for the phrasing, it was w...

RoughTiger69

Apparently,

, doesn’t populate that dict with

any keys that don’t already exist in it

.

Are you saying new entries are not added to the Dict even if they are on the Task (i.e. only entries that already exist on the dict are populated ?
But you already have all the entries defined here:
https://github.com/allegroai/clearml/blob/721569bb77d89d89e5b4f32a0ed98311c4574650/examples/services/aws-autoscaler/aws_autoscaler.py#L22

Since all this is ha...

With pleasure 🙂

Yeah, Curious - is a lot of clearml usecases not geared for notebooks?

That is somewhat correct, notebooks are not actually used with a lot of deep-learning projects as they require entire repository to support.
I guess generally speaking the workflow is, "test your code" (i.e. small scale with limited data), then clone and enqueue for remote execution.
That said, I think it will be great to expand the support.
TrickySheep9 I like the idea of context for Tasks, can you expand on how...

ColorfulBeetle67 you might need to configure use_credentials_chain see here:
https://github.com/allegroai/clearml/blob/a9774c3842ea526d222044092172980ae505e24f/docs/clearml.conf#L85
Regrading the Token, I did not find any reference to "AWS_SESSION_TOKEN" in the clearml code, my guess it is used internally by boto?!

cuda 10.1, I guess this is because no wheel exists for torch==1.3.1 and cuda 11.0

Correct

how can I enforce a specific wheel to be installed?

You mean like specific CUDA wheel ?
you can simple put the http link to the wheel in the "installed packages", it should work

I'd prefer to use config_dict, I think it's cleaner

I'm definitely with you

Good news:

new 

best_model

 is saved, add a tag 

best

,

Already supported, (you just can't see the tag, but it is there :))

My question is, what do you think would be the easiest interface to tell (post/pre) store, tag/mark this model as best so far (btw, obviously if we know it's not good, why do we bother to store it in the first place...)

Hi WorriedParrot51
So I think what you need is to map your external code into the docker, is that correct?
Also you want to always set the PYTHONPATH.
You can achieve both by configuring the trains.conf:

Here you can always add a predefined environment and mount point, regardless of the docker image or other docker argument arguments:
https://github.com/allegroai/trains-agent/blob/master/docs/trains.conf#L98

Will this solve the issue?

Yes 🙂 documentation is being worked on ... Anyhow we will be uploading a new documentation site soon (hopefully in a week or so), putting it all on GitHub so it will be easier for the community to edit and add more

I simplified the code, just so I could test it, this one seems to work, feel free to add the missing argparser parts :)
` from argparse import ArgumentParser
from trains import Task
model_snapshots_path = 'mnt/trains'
task = Task.init(project_name='examples', task_name='test argparser', output_uri=model_snapshots_path)
logger = task.get_logger()

def main(args):
print('Got args: %s' % args)

if name == 'main':
parent_parser = ArgumentParser(add_help=False)
parent_parser....

Hi BeefyHippopotamus73

. I checked the template task and the list of “Installed Packages” indeed does not have one of my required packages in the list.

Basically the "installed packages" is auto populated based on the directly imported packages n your code base.
Could it be you do not have import snowflake-connector-python and this is a derivative package (i.e. required from a different package)

BTW: when you clone your Task in the UI you can edit and add the missing packages,...

I love the new docs layout!

Thank you and thank docusaurus, they rock!

Thanks LethalDolphin75 ! ❤

Hi @<1552101447716311040:profile|SteadySeahorse58>

ValueError: Could not find queue named "services"

Did you set an agent / auto-scaler ? where is the pipeline and its components will be running ?

TrickyRaccoon92 the title provided by write.scalars is also a representing string for the specific metric. This is more than just a title on the plot itself.
It means that this will be the name of the scalar metric (title/series combination) .
Is that your intention, or is it for viewing purpose only?

I'm having another problem now because I am using the OptunaOptimizer.

Hmm let me check a sec

ReassuredTiger98 both are running with pip as package manager, I thought you mentioned conda as package manager, no?
agent.package_manager.type = pipAlso the failed execution is looking for "ruamel_yaml_conda" but it is nowhere to be found on the original one?! how is that possible ?

Hi TenseOstrich47
Does the .ssh folder on the user running the agent contain the correct credentials ?
Basically from the user running the agent on the agent's machine can you clone the repo with:
ssh://git@github.com/15gifts/py-db.git

MysteriousBee56
Well we don't want to ask sudo permission automatically, and usually setups do no change, but you can diffidently call this one before running the agent 😉
sudo chmod 777 -R ~/.trains/

you could also use:
https://github.com/allegroai/clearml/blob/ce7e77a00e869a2690f31cbc578636ce88bc4613/docs/clearml.conf#L188
and setup the clearml.conf on the users machine to automatically log the environment variables at run time (stored under the Configuration tab).
Then the agent will pull these same variables at execution time and set them

So it is the automagic that is not working.
Can you print the following before calling Both Task.debug_simulate_remote_task and Task.init , Notice you have to call Task.init
print(os.environ)

PompousBeetle71 let me know if it solves your problem

Okay
Try to reset the experiment and resend for execution, let me know if you still get the error, if you do, could you send a screen grap of the Execution tab? Trains supports either git repo, or standalone code (jupyter) but not a mixture of the two. This means that if you want to run the jupyter/colab the cloning will have to be part of the notebook itself (as you already have it). That said, due to the way CoLab works, Trains will log your execution history (as opposed to the entire jupy...

Where do you store those ?

Go to https://demoapp.trains.allegro.ai/profile
You should see something like 0.16.2-123

Simple git clone on that repo works well

On the machine running the trains-agent ?

https://clear.ml/docs/latest/docs/references/sdk/task#taskimport_offline_session

I think it would make sense to have one task per run to make the comparison on hyper-parameters easier

I agree. Could you maybe open a GitHub issue on it, I want to make sure we solve this issue 🙂

Thank you!

HandsomeCrow5
BTW: out of curiosity, how do you generate the html reports. I remember a few users suggesting trains should have a report generating functionality