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Welcome to the new SigOpt docs! If you're looking for the classic SigOpt documentation then you can find that here. Otherwise, happy optimizing!

Record SigOpt Runs with Python IDE

If the CLI or Jupyter Notebook integration isn't right for your use case then you might want to create Runs and Experiments directly with the SigOpt Python Client. To create a Run, just add the following to your code:

run = sigopt.create_run()

With this Run object you can get and set parameter values and log attributes in a similar way as you would when using the CLI:

run.params.learning_rate = 0.1
accuracy = train_my_model(learning_rate=run.params.learning_rate)
run.log_metric("accuracy", accuracy)

Finally, end the Run:


For convenience, you can use a Python context manager to end the Run automatically, including when your code raises an exception:

with sigopt.create_run() as run:
  run.params.learning_rate = 0.1
  accuracy = train_my_model(learning_rate=sigopt.params.learning_rate)
  run.log_metric("accuracy", accuracy)

With SigOpt installed and your Python environment set up, let's take a look at how to record a SigOpt Run in a Python IDE.

Instrument the Run

import tensorflow as tf
import sigopt
import os

os.environ["SIGOPT_PROJECT"] = "run-examples"

class KerasNNModel:
  def __init__(self, hidden_layer_size, activation_fn):
    model = tf.keras.Sequential(
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(hidden_layer_size, activation=activation_fn),
    self.model = model

  def get_keras_nn_model(self):
    return self.model

  def train_model(self, train_images, train_labels, optimizer_type, metrics_list, num_epochs):
    ), train_labels, epochs=num_epochs)

  def evaluate_model(self, test_images, test_labels):
    metrics_dict = self.model.evaluate(test_images, test_labels, verbose=2, return_dict=True)
    return metrics_dict

def load_data_train_model(sigopt_run):
  (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

  # set model training, architecture parameters and hyperparameters
  sigopt_run.params.num_epochs = 2
  sigopt_run.params.hidden_layer_size = 200
  sigopt_run.params.activation_fn = "relu"

  # create the model
  keras_nn_model = KerasNNModel(
    hidden_layer_size=sigopt_run.params.hidden_layer_size, activation_fn=sigopt_run.params.activation_fn
  sigopt_run.log_model("Keras NN Model with 1 Hidden layer")

  # train the model
  keras_nn_model.train_model(train_images, train_labels, "adam", ["accuracy"], sigopt_run.params.num_epochs)
  sigopt_run.log_metadata("sgd optimizer", "adam")
  metrics_dict = keras_nn_model.evaluate_model(test_images, test_labels)

  # log performance metrics
  sigopt_run.log_metric("accuracy", metrics_dict["accuracy"])
  sigopt_run.log_metric("loss", metrics_dict["loss"])

if __name__ == "__main__":
  with sigopt.create_run() as run:

Run the Code

$ python
Run started, view it on the SigOpt dashboard at
Epoch 1/2 
 1875/1875 [==============================] - 5s 2ms/step - loss: 2.7513 - accuracy: 0.8826 
Epoch 2/2 
 1875/1875 [==============================] - 4s 2ms/step - loss: 0.3313 - accuracy: 0.9265 
313/313 - 0s - loss: 0.2941 - accuracy: 0.9478 
Run finished, view it on the SigOpt dashboard at