Run Tutorial
We'll walk through an example of instrumenting and executing run code with SigOpt. In this tutorial, you will learn how to:
  • Install the SigOpt Python client
  • Set your SigOpt API token
  • Set the project
  • Instrument your model
  • Create your first Run and log your model metric and parameters to SigOpt
  • View your Run in the SigOpt web application
Before starting, make sure you have Python 3.6+ and pip installed on your environment.
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$ python --version
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$ pip --version
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View this tutorial in a notebook

For notebook instructions and tutorials, check out our GitHub notebook tutorials repo or open the SigOpt Run notebook tutorial in Google Colab.

Step 1 - Install SigOpt Python Client

Install the SigOpt Python package and the libraries required to run the model used for this tutorial.
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# Install sigopt
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$ pip install sigopt
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# Confirm that sigopt >= 8.0.0 is installed
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$ sigopt version
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# Install XGBoost and scikit-learn. We have tested the sample model used in this tutorial with xgboost==1.5.2, and scikit-learn==1.0.2
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$ pip install xgboost scikit-learn
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Step 2 - Set Your API Token

Once you've installed SigOpt, you need to get your API token in order to use the SigOpt API and later explore your Runs and Experiments in the SigOpt app. To find your API token, go directly to the API Token page.
If you do not have an account, Sign up for a free account and get started with SigOpt today.
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# Set sigopt basic configuration. You will be asked to fill in your API token,
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# and whether you want SigOpt to collect your model logs and track your model code
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$ sigopt config
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Step 3 - Set Project

Runs are created within projects. The project allows you to sort and filter through your Runs and Experiments and view useful charts to gain insights into everything you've tried.
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# Set the environment variable to the SigOpt project where your Run will be saved.
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$ export SIGOPT_PROJECT=my_first_project
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Step 4 - Instrument Your Model

The code below is a sample model instrumented with SigOpt where we highlight how to use SigOpt methods to log and track key model information.
Save the lines below in a script called model.py.
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# model.py
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import sklearn.datasets
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import sklearn.metrics
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from xgboost import XGBClassifier
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import sigopt
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# Data preparation required to run and evaluate the sample model
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X, y = sklearn.datasets.load_iris(return_X_y=True)
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Xtrain, ytrain = X[:100], y[:100]
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# Track the name of the dataset used for your Run
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sigopt.log_dataset('iris 2/3 training, full test')
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# Set n_estimators as the hyperparameter to explore for your Experiment
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sigopt.params.setdefault("n_estimators", 100)
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# Track the name of the model used for your Run
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sigopt.log_model('xgboost')
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# Instantiate and train your sample model
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model = XGBClassifier(
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n_estimators=sigopt.params.n_estimators,
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use_label_encoder=False,
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eval_metric='logloss',
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)
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model.fit(Xtrain, ytrain)
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pred = model.predict(X)
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# Track the metric value and metric name for each Run
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sigopt.log_metric("accuracy", sklearn.metrics.accuracy_score(pred, y))
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Step 5 - Run the Code

Use the command sigopt run to track and execute your model. In our example, we have a python script called model.py.
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$ sigopt run python model.py
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Once you've run the code above, SigOpt will conveniently output a link to the Run page on our web application.

Step 6 - View Your Run

Click on the Run link to view your completed Run in our web application. Here's a view of a Run page:

Conclusion

In this tutorial, we've covered the recommended way to instrument your Run with SigOpt. After your model has been instrumented, it is easy to take advantage of SigOpt's optimization features. Optimization helps find the parameters for your model that give you the best metric (e.g. maximizing an accuracy metric).
Check out the tutorial, Experiment and Optimization Tutorial, to see how you can create an Experiment.