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77 lines
3.3 KiB
Markdown
77 lines
3.3 KiB
Markdown
---
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title: Configuring Models
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---
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The [model_config.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/model_config.py) example demonstrates
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configuring a model and defining label enumeration. Connect the configuration and label enumeration to a Task and, once
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connected, **ClearML** tracks any changes to them. When **ClearML** stores a model in any framework, **ClearML** stores
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the configuration and label enumeration with it.
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When the script runs, it creates an experiment named `Model configuration example`, which is associated with the `examples` project.
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## Configuring Models
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### Using a Configuration File
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Connect a configuration file to a Task by calling the [Task.connect_configuration](../../references/sdk/task.md#connect_configuration)
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method with the file location and the configuration object's name as arguments. In this example, we connect a JSON file and a YAML file
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to a Task.
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```python
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config_file_json = 'data_samples/sample.json'
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task.connect_configuration(name="json file", configuration=config_file_json)
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config_file_yaml = 'data_samples/config_yaml.yaml'
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task.connect_configuration(configuration=config_file_yaml, name="yaml file")
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```
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The configuration is logged to the ClearML Task and can be viewed in the **ClearML Web UI** experiment details **>** **CONFIGURATION** tab **>** **CONFIGURATION OBJECTS**
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section. The contents of the JSON file will appear in the **json file** object, and the contents of the YAML file will appear
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in the **yaml file** object, as specified in the `name` parameter of the `connect_configuration` method.
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### Configuration Dictionary
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Connect a configuration dictionary to a Task by creating a dictionary, and then calling the [Task.connect_configuration](../../references/sdk/task.md#connect_configuration)
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method with the dictionary and the object name as arguments. After the configuration is connected, **ClearML** tracks changes to it.
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```python
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model_config_dict = {
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'CHANGE ME': 13.37,
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'dict': {'sub_value': 'string', 'sub_integer': 11},
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'list_of_ints': [1, 2, 3, 4],
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}
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model_config_dict = task.connect_configuration(
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name='dictionary',
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configuration=model_config_dict
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)
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# Update the dictionary after connecting it, and the changes will be tracked as well.
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model_config_dict['new value'] = 10
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model_config_dict['CHANGE ME'] *= model_config_dict['new value']
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```
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The configurations are connected to the ClearML Task and can be viewed in the **ClearML Web UI** **>** experiment details **>** **CONFIGURATION** tab **>**
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**CONFIGURATION OBJECTS** area **>** **dictionary** object.
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## Label Enumeration
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Connect a label enumeration dictionary by creating the dictionary, and then calling the [Task.connect_label_enumeration](../../references/sdk/task.md#connect_label_enumeration)
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method with the dictionary as an argument.
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```python
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# store the label enumeration of the training model
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labels = {'background': 0, 'cat': 1, 'dog': 2}
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task.connect_label_enumeration(labels)
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```
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Log a local model file.
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```python
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OutputModel().update_weights('my_best_model.bin')
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```
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The model which is stored contains the model configuration and the label enumeration.
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