Hi SmugLizard25 I was able to test and it seems that style is being ignored by the FE 😞
I passed to FE guys to make sure it is fixed in the next version.
Notice this is just for tables, anything else works as expected (i.e. styling any other type of plot)

Did you you set 'force_git_ssh_protocol: true '?
https://github.com/allegroai/clearml-agent/blob/249b51a31bee97d63f41c6d5542e657962008b68/docs/clearml.conf#L39

Hmm can you run the agent in debug mode, and check the specific console log?
'''
clearml-agent --debug daemon --foreground ...

1. Yes the main diff between add task and decorator is basically creating dag and " executes " the tasks in parallel, based on the dag dependencies
2. Decorator will also take care of serializing the data in / out of the function. Imagine the pipeline logic is running as python code where the logic will wait for the function to finish only when the result of the function is being used. This means that if you need a parllel loop you can create thread pool.
   Make sense

So it seems decorator is simply the superior option?

Kind of yes 😊

In which case would we use add_task() option?

When you have existing Tasks, and the piping is very straight forward (i.e. input / output in the code is basically referencing other Tasks/artifacts, and there is no real need to do any magic for serializing/deserializing data between steps

or do you mean agent can convert https url to ssh??

Yep it does that automatically if you set: force_git_ssh_protocol: true
https://github.com/allegroai/clearml-agent/blob/42606d9247afbbd510dc93eeee966ddf34bb0312/docs/clearml.conf#L25

Hi WorriedParrot51
Assuming you run the code "manually" once (i.e. without the agent). Then when you call Task.init it will register the argparser.
When running with the agent, the first time you will call parse, it will automatically override the argparse defaults with the values stored in the Task.
Make sesne?

am getting None for Task.current_task() at the beginning of my script.

Task.init() is doing the magic , only after this call you will have current_task (either running manua...

WorriedParrot51 trains should support subparsers etc.
Even if your code calls the parsing before trains.
The only thing you need is to import the package when argparser is called (not to initialize it, that can happen later)
It should (hopefully) solve the issue.

(i.e. importing the trains package is enough to patch the argparser, only when you call the task.init the arguments will be logged, before they are stored in memory)

It will store the entire content of the file, then you can edit it in the UI, and in remote it will return a new local copy of the file (based on the data in the UI) for you to read.

HealthyStarfish45 this sounds very cool! How can I help?

Sure thing, feel free to ping 🙂

TroubledHedgehog16 generally speaking you can expect about 10 api calls per minute if you have many reports, and about 3 per minute on low report. We just optimized the sdk so in cases there are lots of consequential reports they are better batched, I would recommend the latest RC

Hi @<1523701181375844352:profile|ExasperatedCrocodile76>
the docker containers should get the host IP, not the internal docker IP. what am I missing ?

and then in Preprocess:

self.model = get_model(task_id=os.environ['TASK_ID'], model_name=os.environ['MODEL_NAME'])That's the part I do not get, Models have their own entity (with UID), this is in contrast to artifacts that are only stored on Tasks.
The idea when you are registering a model with clearml-serving, you can specify the model ID, this should replace the need for the TASK_ID+model_name in your code, and the clearml-serving will basically bring it to you
Basically this fun...

WickedElephant66 is this issue the same as this one?
https://clearml.slack.com/archives/CTK20V944/p1656537337804619?thread_ts=1656446563.854059&cid=CTK20V944

HealthyStarfish45 you mean as in RestAPI ?

if I use 

report_image

 can I get a URL to it somehow?

Let me check ...

HealthyStarfish45 what exactly did you have in mind, in terms of the widget ?

Hmm, it is not returned, it is inside the function....

Hi, I changed it to 1.13.0, but it still threw the same error.

This is odd, just so we can make the agent better, any chance you can send the Task log ?

Hi DullCamel78

Hi everyone! Has anyone tried running

aws_autoscaler.py without docker?

Well generally since this is a remote machine the easiest way to control environment is with containers, hence the default use case. In theory you can change it to use venv, but then of course your a somewhat limited with the diff drivers/cuda/python environement.

performance under docker is 10% lower than on bare metal

add to your extra docker args
` extra_docker_arguments: ["...

Hi ProudMosquito87
My apologies there is still no concrete ETA ...
That said I think a good toy example would really help accelerate this process.
How about opening a PR with a nice hydra example, then we can start discussing implementation details based on the toy example ?

0.17.2 ?

ShortElephant92 yep, this is definitely enterprise feature 🙂
But you can configure user/pass on the open source, even store as hasedh the passwords if you need.

Another issue that might be the case, might be that I'm on ubuntu some of the packages might've been for windows thus the different versions not existing

Usually this is not the case, the version number match (implementation wise it might be a different file, but it is almost always a matching version)

Mmm well, I can think of a pipeline that could save its state in the instant before the error occurred.

This is already the case, if you clone the pipeline Task change the Args/_continue_pipeline_ to True and enqueue

GiganticTurtle0 is there any git redundancy on your network ? maybe you could configure a fallback server ?

Hi @<1601023807399661568:profile|PompousSpider11>
Yes "activating" a conda/python environment in a docker is more complicated then it should be ...
To debug, what are you getting when you do:

docker run -it <docker name here> bash -c "set"

PompousParrot44 these are the default plotly colors. You can change any of the layout properties with the
https://github.com/allegroai/trains/blob/65a4aa7aa90fc867993cf0d5e36c214e6c044270/trains/logger.py#L600

https://plotly.com/javascript/colorscales/