diff line by line is probably not useful for my data config

You could request a better configuration diff feature 🙂 Feel free to add to GitHub

But this also mean I have to first load all the configuration to a dictionary first.

Yes 😞

But first I want to make sure the verify argument is actually used, hence False

Thanks ContemplativePuppy11 !

How would you pass data/args between one step of the pipeline to another ?
Or are you saying the pipeline class itself stores all the components ?

And I'm assuming no luck?

Yes I think you are correct

Hmm let me check something

UptightMouse31 You can add any metric (KPI) with "manual" logging
Logger.current_logger().report_scalar("KPI", "metric", iteration=0, value=1.1)This means you can later add a column KPI/metric to your experiment table.
Will this do the trick ?

Yes... I think that this might be a bit much automagic even for clearml 😄

If possible, i would like all together prevent the fileserver and write everything to S3 (without needing every user to change their config)

There is no current way to "globally" change the default files server (I think this is part of the enterprise version, alongside vault etc.).
What you can do is use an OS environment to override the conf file:
CLEARML_FILES_HOST=" "PricklyRaven28 wdyt?

that does happen when you create a normal local task, that's why i was confused

The parts that are not passed in both cases are the configurations from the conf file. Only the environment is passed (e.g. git python packages etc) , . For example if you have storage credentials in your conf file , they are not passed to a remote agent, instead the credentials from the remote agent are used when it runs the task.
make sense?

PricklyRaven28 did you set the iam role support in the conf?
https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/docs/clearml.conf#L86

Was I right to put the credentials in

clearml.conf

on the machine I am starting the agent on?

AdventurousButterfly15 Yes exactly!
you should be able to see that in the log of the Task (at the top of the log there will be the entire configuration), can you see the git user there?

Thanks GiganticTurtle0 !
I will try to reproduce with the example you provided. regardless I already took a look at the code, and I'm pretty sure I know what the issue is. We will be pushing a few fixes after the weekend, I'm hoping this one will be included as well 🙂

BTW: if you make the right column the base line (i.e. move it to the left, you will get what you probably expected)

Probably less secure though :)

Nice! So out of curiosity why didn't it work this time and you had to do it manually?

I like the idea of using the timeit interface, and I think we could actually hack it to do most of the heavy lifting for us 🙂

I just assumed it should only be triggered by dataset related things but after a lot of experimenting i realized its also triggered by tasks...

VexedCat68 I think you are correct, and it should only be triggered by "Dataset" Tasks, that said maybe there is a bug , in which case if there are no additional filters it will get triggered on Any change in the project. This will explain how adding the tags filter solved the issue.
wdyt?

Have to get glue setup, which I couldn’t understand fully, so that’s a different topic

I suggest using the apply template setup (basically you provide a Job/Service template, and it uses that to setup k8s jobs based on the Tasks coming in from the specific queue)

:) yes on your gateway/firewall set http://demoapi.trains.allegro.ai to 127.0.0.1 . That's always good practice ;)

Any chance you can share the Log?
(feel free to DM it so it will not end up public)

Thanks that helps!
Maybe we just create a copy of the plot that gets "stuck"

Yes RipeGoose2 you are totally correct 🙂 if you want the models to be auto uploaded in the offline session you have to pass output_uri (or default_output_uri).

Hi TrickySheep9
Long story short, clearml-session fully supports k8s (using k8s glue)
The --remote-gateway along side ports mode will basically allow you to setup a k8s service so that every session will register with a specific port so k8s does ingest foe you and route the SSH connection to the pod itslef, everything else is tunneled over the original SSH connection.
Make sense ?

JitteryCoyote63 instead of _update_requirements, call the following before Task.init:
Task.add_requirements('torch', '1.3.1') Task.add_requirements('git+ ')

ElegantCoyote26 I don't think Keras logs it anywhere unless you have TB, so nowhere to take the data from...
In short, yes you have to have TB :)

👍

DepressedChimpanzee34 any string serialization package I tried will convert r"some\blah" into "some\\blah" (json yaml hocon) otherwise you end up with \b as an escape character. I'm really not sure what to do here. (And reinventing the standard seems unhealthy)

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: ["...

VexedCat68 yes 🙂 you can also pass the parent folder and it will zip the entire subfolders into a single artifact