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Add OutputModel examples (#178)

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---
title: Configuring Models
title: Model Reporting
---
The [model_config.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/model_config.py) example demonstrates
configuring a model and defining label enumeration. Connect the configuration and label enumeration to a Task and, once
connected, **ClearML** tracks any changes to them. When **ClearML** stores a model in any framework, **ClearML** stores
the configuration and label enumeration with it.
The [model_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/model_reporting.py) example
demonstrates logging a model using the [OutputModel](../../references/sdk/model_outputmodel.md)
class.
When the script runs, it creates an experiment named `Model configuration example`, which is associated with the `examples` project.
The example does the following:
* Creates a task named `Model reporting example` in the `examples` project.
* Uses an OutputModel object to register a previously trained model and log its label enumeration.
## Configuring Models
### Using a Configuration File
Connect a configuration file to a Task by calling the [Task.connect_configuration](../../references/sdk/task.md#connect_configuration)
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
to a Task.
## Initialization
An OutputModel object is instantiated for the task.
```python
config_file_json = 'data_samples/sample.json'
task.connect_configuration(name="json file", configuration=config_file_json)
config_file_yaml = 'data_samples/config_yaml.yaml'
task.connect_configuration(configuration=config_file_yaml, name="yaml file")
from clearml import Task, OutputModel
# Connecting ClearML with the current process,
task = Task.init(project_name="examples", task_name="Model logging example")
# Create output model and connect it to the task
output_model = OutputModel(task=task)
```
The configuration is logged to the ClearML Task and can be viewed in the **ClearML Web UI** experiment details **>** **CONFIGURATION** tab **>** **CONFIGURATION OBJECTS**
section. The contents of the JSON file will appear in the **json file** object, and the contents of the YAML file will appear
in the **yaml file** object, as specified in the `name` parameter of the `connect_configuration` method.
![image](../../img/examples_reporting_config.png)
### Configuration Dictionary
Connect a configuration dictionary to a Task by creating a dictionary, and then calling the [Task.connect_configuration](../../references/sdk/task.md#connect_configuration)
method with the dictionary and the object name as arguments. After the configuration is connected, **ClearML** tracks changes to it.
```python
model_config_dict = {
'CHANGE ME': 13.37,
'dict': {'sub_value': 'string', 'sub_integer': 11},
'list_of_ints': [1, 2, 3, 4],
}
model_config_dict = task.connect_configuration(
name='dictionary',
configuration=model_config_dict
)
# Update the dictionary after connecting it, and the changes will be tracked as well.
model_config_dict['new value'] = 10
model_config_dict['CHANGE ME'] *= model_config_dict['new value']
```
The configurations are connected to the ClearML Task and can be viewed in the **ClearML Web UI** **>** experiment details **>** **CONFIGURATION** tab **>**
**CONFIGURATION OBJECTS** area **>** **dictionary** object.
![image](../../img/examples_reporting_config_3.png)
## Label Enumeration
Connect a label enumeration dictionary by creating the dictionary, and then calling the [Task.connect_label_enumeration](../../references/sdk/task.md#connect_label_enumeration)
method with the dictionary as an argument.
Set the model’s label enumeration using the [`OutputModel.update_labels`](../../references/sdk/model_outputmodel.md#update_labels)
method.
```python
# store the label enumeration of the training model
labels = {'background': 0, 'cat': 1, 'dog': 2}
task.connect_label_enumeration(labels)
labels = {"background": 0, "cat": 1, "dog": 2}
output_model.update_labels(labels)
```
Log a local model file.
## Registering Models
Register a previously trained model using the [`OutputModel.update_weights`](../../references/sdk/model_outputmodel.md#update_weights)
method. The example code uses a model stored in S3.
```python
OutputModel().update_weights('my_best_model.bin')
```
# Manually log a model file, which will have the labels connected above
output_model.update_weights(register_uri=model_url)
```
The model which is stored contains the model configuration and the label enumeration.
## WebApp
The model appears in the task’s **ARTIFACTS** tab.
![Task artifacts](../../img/examples_model_logging_artifacts.png)
Clicking on the model name takes you to the [model’s page](../../webapp/webapp_model_viewing.md), where you can view the
model’s details and access the model.
The model’s **LABELS** tab displays its label enumeration.
![Model Labels tab](../../img/examples_model_logging_labels.png)
![image](../../img/examples_reporting_config_2.png)