Record SigOpt Runs with Python IDE and SigOpt CLI
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 with the SigOpt CLI.

Instrument the Run

import tensorflow as tf
import sigopt
import os
os.environ["SIGOPT_API_TOKEN"] = # SIGOPT_API_TOKEN
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),
tf.keras.layers.Dense(10),
]
)
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):
self.model.compile(
optimizer=optimizer_type,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=metrics_list,
)
self.model.fit(train_images, 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.log_dataset(name="mnist")
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
# set model training, architecture parameters and hyperparameters
sigopt.params.num_epochs = 2
sigopt.params.hidden_layer_size = 200
sigopt.params.activation_fn = "relu"
# create the model
keras_nn_model = KerasNNModel(
hidden_layer_size=sigopt.params.hidden_layer_size, activation_fn=sigopt.params.activation_fn
)
sigopt.log_model("Keras NN Model with 1 Hidden layer")
# train the model
keras_nn_model.train_model(train_images, train_labels, "adam", ["accuracy"], sigopt.params.num_epochs)
sigopt.log_metadata("sgd optimizer", "adam")
metrics_dict = keras_nn_model.evaluate_model(test_images, test_labels)
# log performance metrics
sigopt.log_metric("accuracy", metrics_dict["accuracy"])
sigopt.log_metric("loss", metrics_dict["loss"])
if __name__ == "__main__":
load_data_train_model()

Run the Code with the SigOpt CLI

$ sigopt run python keras_model.py
Run started, view it on the SigOpt dashboard at https://app.sigopt.com/run/1234
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 https://app.sigopt.com/run/1234

Orchestrate a Run with the SigOpt CLI

SigOpt Orchestrate allows you to confidently scale your modeling experimentation on either your existing Kubernetes infrastructure or a Kubernetes cluster managed by SigOpt. To set up SigOpt Orchestrate, follow this installation guide. With SigOpt Orchestrate installed, all you have to do is set up yourrun.yml file and execute the following command locally to orchestrate your Run on your Kubernetes cluster:
#run.yml
name: My Run
run: python keras_model.py
resources:
limits:
cpu: 2
memory: 4Gi
gpus: 1
image: my-run bab
$ sigopt cluster run --run-file run.yml
When you execute the above, SigOpt Orchestrate will look into the specified run.yml file. It will allocate 2 CPUs, 4 GiB memory, and 1 GPU of your kubernetes cluster for your Run, and will execute the command provided for run.
Run started, view it on the SigOpt dashboard at https://app.sigopt.com/run/1234
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 https://app.sigopt.com/run/1234
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On this page
Instrument the Run
Run the Code with the SigOpt CLI
Orchestrate a Run with the SigOpt CLI