Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

How to access hyper parameters that are in configuration objects for hyperparameter optimization

See original GitHub issue

I am currently using MMDetection and tracking the results on Clearml. The hyper parameters are not located under <Hyperparameter>/<args or general> but in <configuration objects>/<config> instead.

Following the guide, i am stuck at this portion where i am having difficulty accessing them

https://github.com/allegroai/clearml/blob/b9b0a506f35a414f6a9c2da7748f3ec3445b7d2d/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py#L62-L73

Any solution/workaround to proceed on this? Thanks

Issue Analytics

State:
Created a year ago
Comments:11

Top GitHub Comments

1reaction

DavidNativcommented, Jun 9, 2022

Hi you could retrieve what is logged in the configuration section using task.get_configuration_object_as_dict(name). The output should be an OrderedDict.

0reactions

erezalgcommented, Jul 3, 2022

What I understand is that the parameters in MMDetection are saved in a configuration object and not in a hyperparameter. What I’m saying is that configuration object doesn’t impose a strict structure, you can put whatever text you want there. That being said, if the structure is json or yaml inside, we can parse it and in the future add support for doing parameter search on it.

I think your approach is better, it would support the built-in functionality of clearml and you could use HPO with this method.

Top Results From Across the Web

How to Manage, Track, and Visualize Hyperparameters of ...

For hyperparameter tuning, the best practice is to have those hyperparameters in a single place as the results to make a good decision....

Parameters, Hyperparameters, Machine Learning

Hyperparameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning ......

Hyperparameter Optimization With Random Search and Grid ...

Hyperparameters are points of choice or configuration that allow a machine learning model to be customized for a specific task or dataset.

Hyperparameter Optimization Techniques to Improve Your ...

When working on a machine learning project, you need to follow a series of steps until you reach your goal.

Hyperparameters - ClearML

To get all of a task's parameters and properties (hyperparameters, configuration objects, and user properties), use the Task.get_parameters method, which will ...