ElegantCoyote26 point me to where Keras stores the data 🙂
If in the process of integration you had to add a logger/callback to your Keras code, that is the equivalent of using the TB.

I'll make sure we fix the example, because as you pointed, it is broken :(

Thanks ElegantCoyote26 I'll look into it. Seems like someone liked our automagical approach 🙂

A few epochs is just fine

`

Example use case:

an_optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id=args['template_task_id'],
# here we define the hyper-parameters to optimize
hyper_parameters=[
UniformIntegerParameterRange('General/layer_1', min_value=128, max_value=512, step_size=128),
UniformIntegerParameterRange('General/layer_2', min_value=128, max_value=512, step_size=128),
DiscreteParameterRange('General/batch_size', values=[...

ElegantCoyote26 It means we need to have a keras logger that logs everything to trains, then we need to hook it automatically.
Do you feel like PR-ing the logger (the hooking I can take care of 🙂 )?

Hi ElegantCoyote26 , yes I did 🙂
It seems cometml puts their default callback logger for you, that's it.

PungentLouse55 from the screenshot I assume the experiment template you are trying to optimize is not the one from the trains/examples 🙂
In that case, and based on the screenshots, the prefix is "Args/" as this is the section name.
Regrading objective metric, again based on your screenshots:
objective_metric_title="Accuracy" objective_metric_series="Validation"Make sense ?

YEY!

Thank you!

Hi PungentLouse55

Hope you are not tired of me

Lol 🙂 No worries

I am using trains 0.16.1

Are you referring to the trains-server version or the python package ? (they are not the same and can be of totally different versions)

PungentLouse55 I'm checking something here, you might stumbled on a bug in parameter overriding. Updating here soon ...

The latest RC (0.17.5rc6) moved all logs into separate subprocess to improve speed with pytorch dataloaders

I want to optimizer hyperparameters with trains.automation but: ...

Yes you are correct, in case of the example code, it should be "General/..." if you have ArgParser, it should be "Args/..." Yes it looks like the metric is wrong, it should be "epoch_accuracy" & "epoch_accuracy"

Hi PungentLouse55
Are you referring to the example code ?

Yey!

Hi @<1545216070686609408:profile|EnthusiasticCow4>

will ClearML remove the corresponding folders and files on S3?

Yes and it will ask you for credentials as well. I think there is a way to configure it so that the backend has access to it (somehow) but this breaks the "federated" approach

It's just another flag when running the trains-agent
You can have multiple service-mode instances, there is no actual limit 🙂

GreasyLeopard35
I can update that the fix to UniformIntegerParameterRange should be pushed with tomorrows release 🙂
(which would fix in turn LogUniformParameterRange)

Maybe add http://requirements.tt to your repo?

Hi PompousBeetle71 , what exactly is the scenario / problem we are trying to solve ?

GrittyKangaroo27 any chance you can open a GitHub issue so this is not forgotten ?
(btw: we I think 1.1.6 is going to be released later today, then we will have a few RC with improvements on the pipeline, I will make sure we add that as well)

Hi GrittyKangaroo27

How could I turn off model logging when running this training step?

This is a good point! I think we cannot pass these arguments.
Would this make sense to you?
PipelineDecorator.component(..., auto_connect_frameworks)
wdyt?

Hi GrievingTurkey78
Can you test with the latest clearml-agent RC (I remember a fix just for that)
pip install clearml-agent==1.2.0rc0

Great if this is what you do how come you need to change the entry script in the ui?

I did nothing to generate a command-line. Just cloned the experiment and enqueued it. Used the server GUI.

Who/What created the initial experiment ?

I noticed that if I run the initial experiment by "python -m folder_name.script_name"

"-m module" as script entry is used to launch entry points like python modules (which is translated to "python -m script")
Why isn't the entry point just the python script?
The command line arguments are passed as arguments on the Args section of t...

Oh I see, what you need is to pass '--script script.py' as entry-point and ' --cwd folder' as working dir

conda list | grep matplotlib ?

pip freeze | grep pandas

Actually it is better to leave it as is, it will just automatically mount the .ssh folder into the container, i will make sure the docs point to this option first