Tutorial: Train your first ML model
APPLIES TO: Basic edition Enterprise edition (Upgrade to Enterprise edition)
This tutorial is part two of a two-part tutorial series. In the previous tutorial, you created a workspace and chose a development environment. In this tutorial, you learn the foundational design patterns in Azure Machine Learning, and train a simple scikit-learn model based on the diabetes data set. After completing this tutorial, you will have the practical knowledge of the SDK to scale up to developing more-complex experiments and workflows.
In this tutorial, you learn the following tasks:
- Connect your workspace and create an experiment
- Load data and train scikit-learn models
- View training results in the portal
- Retrieve the best model
The only prerequisite is to run part one of this tutorial, Setup environment and workspace.
In this part of the tutorial, you run the code in the sample Jupyter notebook
tutorials/tutorial-1st-experiment-sdk-train.ipynb opened at the end of part one. This article walks through the same code that is in the notebook.
Open the notebook
Sign in to Azure Machine Learning studio.
Open the tutorial-1st-experiment-sdk-train.ipynb in your folder as shown in part one.
Do not create a new notebook in the Jupyter interface! The notebook
tutorials/tutorial-1st-experiment-sdk-train.ipynb is inclusive of all code and data needed
for this tutorial.
Connect workspace and create experiment
The rest of this article contains the same content as you see in the notebook.
Switch to the Jupyter notebook now if you want to read along as you run the code. To run a single code cell in a notebook, click the code cell and hit Shift+Enter. Or, run the entire notebook by choosing Run all from the top toolbar.
Workspace class, and load your subscription information from the file
config.json using the function
from_config(). This looks for the JSON file in the current directory by default, but you can also specify a path parameter to point to the file using
from_config(path="your/file/path"). In a cloud notebook server, the file is automatically in the root directory.
If the following code asks for additional authentication, simply paste the link in a browser and enter the authentication token.
from azureml.core import Workspace ws = Workspace.from_config()
Now create an experiment in your workspace. An experiment is another foundational cloud resource that represents a collection of trials (individual model runs). In this tutorial you use the experiment to create runs and track your model training in the Azure Machine Learning studio. Parameters include your workspace reference, and a string name for the experiment.
from azureml.core import Experiment experiment = Experiment(workspace=ws, name="diabetes-experiment")
Load data and prepare for training
For this tutorial, you use the diabetes data set, which uses features like age, gender, and BMI to predict diabetes disease progression. Load the data from the Azure Open Datasets class, and split it into training and test sets using
train_test_split(). This function segregates the data so the model has unseen data to use for testing following training.
from azureml.opendatasets import Diabetes from sklearn.model_selection import train_test_split x_df = Diabetes.get_tabular_dataset().to_pandas_dataframe().dropna() y_df = x_df.pop("Y") X_train, X_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=66)
Train a model
Training a simple scikit-learn model can easily be done locally for small-scale training, but when training many iterations with dozens of different feature permutations and hyperparameter settings, it is easy to lose track of what models you've trained and how you trained them. The following design pattern shows how to leverage the SDK to easily keep track of your training in the cloud.
Build a script that trains ridge models in a loop through different hyperparameter alpha values.
from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error from sklearn.externals import joblib import math alphas = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] for alpha in alphas: run = experiment.start_logging() run.log("alpha_value", alpha) model = Ridge(alpha=alpha) model.fit(X=X_train, y=y_train) y_pred = model.predict(X=X_test) rmse = math.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred)) run.log("rmse", rmse) model_name = "model_alpha_" + str(alpha) + ".pkl" filename = "outputs/" + model_name joblib.dump(value=model, filename=filename) run.upload_file(name=model_name, path_or_stream=filename) run.complete()
The above code accomplishes the following:
- For each alpha hyperparameter value in the
alphasarray, a new run is created within the experiment. The alpha value is logged to differentiate between each run.
- In each run, a Ridge model is instantiated, trained, and used to run predictions. The root-mean-squared-error is calculated for the actual versus predicted values, and then logged to the run. At this point the run has metadata attached for both the alpha value and the rmse accuracy.
- Next, the model for each run is serialized and uploaded to the run. This allows you to download the model file from the run in the portal.
- At the end of each iteration the run is completed by calling
After the training has completed, call the
experiment variable to fetch a link to the experiment in the portal.
|Name||Workspace||Report Page||Docs Page|
|diabetes-experiment||your-workspace-name||Link to Azure portal||Link to Documentation|
View training results in portal
Following the Link to Azure portal takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (
rmse, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.
When training models at scale over hundreds and thousands of separate runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time.
Clicking on a run number link in the
RUN NUMBER column takes you to the page for each individual run. The default tab Details shows you more-detailed information on each run. Navigate to the Outputs tab, and you see the
.pkl file for the model that was uploaded to the run during each training iteration. Here you can download the model file, rather than having to retrain it manually.
Get the best model
In addition to being able to download model files from the experiment in the portal, you can also download them programmatically. The following code iterates through each run in the experiment, and accesses both the logged run metrics and the run details (which contains the run_id). This keeps track of the best run, in this case the run with the lowest root-mean-squared-error.
minimum_rmse_runid = None minimum_rmse = None for run in experiment.get_runs(): run_metrics = run.get_metrics() run_details = run.get_details() # each logged metric becomes a key in this returned dict run_rmse = run_metrics["rmse"] run_id = run_details["runId"] if minimum_rmse is None: minimum_rmse = run_rmse minimum_rmse_runid = run_id else: if run_rmse < minimum_rmse: minimum_rmse = run_rmse minimum_rmse_runid = run_id print("Best run_id: " + minimum_rmse_runid) print("Best run_id rmse: " + str(minimum_rmse))
Best run_id: 864f5ce7-6729-405d-b457-83250da99c80 Best run_id rmse: 57.234760283951765
Use the best run ID to fetch the individual run using the
Run constructor along with the experiment object. Then call
get_file_names() to see all the files available for download from this run. In this case, you only uploaded one file for each run during training.
from azureml.core import Run best_run = Run(experiment=experiment, run_id=minimum_rmse_runid) print(best_run.get_file_names())
download() on the run object, specifying the model file name to download. By default this function downloads to the current directory.
Clean up resources
Do not complete this section if you plan on running other Azure Machine Learning tutorials.
Stop the compute instance
If you used a compute instance or Notebook VM, stop the VM when you are not using it to reduce cost.
In your workspace, select Compute.
From the list, select the VM.
When you're ready to use the server again, select Start.
The resources you created can be used as prerequisites to other Azure Machine Learning tutorials and how-to articles.
If you don't plan to use the resources you created, delete them, so you don't incur any charges:
In the Azure portal, select Resource groups on the far left.
From the list, select the resource group you created.
Select Delete resource group.
Enter the resource group name. Then select Delete.
You can also keep the resource group but delete a single workspace. Display the workspace properties and select Delete.
In this tutorial, you did the following tasks:
- Connected your workspace and created an experiment
- Loaded data and trained scikit-learn models
- Viewed training results in the portal and retrieved models