Using hyperparameters

You can define hyperparameters needed by your algorithm, such as the learning rate or step size, when you create a hybrid job. Hyperparameter values are typically used to control various aspects of the algorithm, and can often be tuned to optimize the algorithm's performance. To use hyperparameters in a Braket hybrid job, you need to specify their names and values explicitly as a dictionary. Specify the hyperparameter values to test when searching for the optimal set of values. The first step to using hyperparameters is to set up and define the hyperparameters as a dictionary, which can be seen in the following code.

from braket.devices import Devices

device_arn = Devices.Amazon.SV1

hyperparameters = {"shots": 1_000}

Then pass the hyperparameters defined in the code snippet given above to be used in the algorithm of your choice. To run the following code example, create a directory named “src” in the same path as your hyperparameter file. Inside of the "src" directory, add 0_Getting_started_papermill.ipynb, notebook_runner.py, and requirements.txt code files.

import time
from braket.aws import AwsQuantumJob

job = AwsQuantumJob.create(
    device=device_arn,
    source_module="src",
    entry_point="src.notebook_runner:run_notebook",
    input_data="src/0_Getting_started_papermill.ipynb",
    hyperparameters=hyperparameters,
    job_name=f"papermill-job-demo-{int(time.time())}",
)

# Print job to record the ARN
print(job)

To access your hyperparameters from within your hybrid job script, see the load_jobs_hyperparams() function in the notebook_runner.py python file. To access your hyperparameters outside of your hybrid job script, run the following code.

from braket.aws import AwsQuantumJob

# Get the job using the ARN
job_arn = "arn:aws:braket:us-east-1:111122223333:job/5eabb790-d3ff-47cc-98ed-b4025e9e296f"  # Replace with your job ARN
job = AwsQuantumJob(arn=job_arn)

# Access the hyperparameters
job_metadata = job.metadata()
hyperparameters = job_metadata.get("hyperParameters", {})
print(hyperparameters)

For more information on learning how to use hyperparamters, see the QAOA with Amazon Braket Hybrid Jobs and PennyLane and Quantum machine learning in Amazon Braket Hybrid Jobs tutorials.