query: How do you create a run?

context: AML API only

response:
To create a run using the Azure Machine Learning API, you first need to create an Experiment. Once you have an experiment, you can create a Run object that is associated with that experiment. Here is some Python code that demonstrates this process:

```
from azureml.core import Experiment, Run
from azureml.core.workspace import Workspace

# Define workspace and experiment
ws = Workspace.from_config()
exp = Experiment(workspace=ws, name='my_experiment')

# Create a new run
run = exp.start_logging()
```

In this code, the `from_config()` method reads the configuration file that you created when you set up your Azure Machine Learning workspace. The `Experiment` constructor creates an Experiment object that is associated with your workspace, and the `start_logging()` method creates a new Run object that is associated with the Experiment. Now you can use the `run` object to log metrics, upload files, and track other information related to your machine learning experiment.

ground_truth: Paris is the capital of France.


query: How do you log a model?

context: Logging can be done using any OSS Sdk

response:
There are a few ways to log models in Azure Machine Learning.

One way is to use the `register_model()` method of the `Run` object. The `register_model()` method logs a model file in the Azure Machine Learning service workspace and makes it available for deployment. Here's an example:

```python
from azureml.core import Model

model_path = './outputs/my_model.pkl'
model = Model.register(workspace=ws, model_path=model_path, model_name='my_model')
```

This code registers the model file located at `model_path` to the Azure Machine Learning service workspace with the name `my_model`.

Another way to log a model is to save it as an output of a `Run`. If your model generation code is part of a script or Jupyter notebook that runs as an Azure Machine Learning experiment, you can save the model file as an output of the `Run` object. Here's an example:

```python
from sklearn.linear_model import LogisticRegression
from azureml.core.run import Run

# Initialize a run object
run = Run.get_context()

# Train your model
X_train, y_train = ...
clf = LogisticRegression().fit(X_train, y_train)

# Save the model to the Run object's outputs directory
model_path = 'outputs/model.pkl'
joblib.dump(value=clf, filename=model_path)

# Log the model as a run artifact
run.upload_file(name=model_path, path_or_stream=model_path)
```

In this code, `Run.get_context()` retrieves the current run context object, which you can use to track metadata and metrics for the run. After training your model, you can use `joblib.dump()` to save the model to a file, and then log the file as an artifact of the run using `run.upload_file()`.

ground_truth: Paris is the capital of France.


query: What is the capital of France?`''"</>{}{{]

context: France is in Europe

response: Paris is the capital of France.

ground_truth: Paris is the capital of France.