Define the environment for your algorithm script - Amazon Braket

Define the environment for your algorithm script

Amazon Braket supports environments defined by containers for your algorithm script:

  • A base container (the default, if no image_uri is specified)

  • A container with CUDA-Q

  • A container with Tensorflow and PennyLane

  • A container with PyTorch, PennyLane, and CUDA-Q

The following table provides details about the containers and the libraries they include.

Amazon Braket containers
Type Base CUDA-Q TensorFlow PyTorch

Image URI

292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-base-jobs:latest

292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-cudaq-jobs:latest

292282985366.dkr.ecr.us-east-1.amazonaws.com/amazon-braket-tensorflow-jobs:latest

292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-pytorch-jobs:latest

Inherited Libraries

  • amazon-braket-default-simulator

  • amazon-braket-pennylane-plugin

  • amazon-braket-schemas

  • amazon-braket-sdk

  • awscli

  • botocore

  • boto3

  • dask

  • matplotlib

  • numpy

  • pandas

  • PennyLane

  • PennyLane-Lightning

  • qiskit-braket-provider

  • requests

  • sagemaker-training

  • scikit-learn

  • scipy

  • awscli

  • numpy

  • pandas

  • scipy

  • awscli

  • numpy

  • pandas

  • scipy

Additional Libraries

  • amazon-braket-default-simulator

  • amazon-braket-pennylane-plugin

  • amazon-braket-schemas

  • amazon-braket-sdk

  • awscli

  • boto3

  • ipykernel

  • matplotlib

  • networkx

  • numpy

  • openbabel

  • pandas

  • PennyLane

  • protobuf

  • psi4

  • rsa

  • scipy

  • cudaq

  • cudaq-qec

  • cudaq-solvers

  • amazon-braket-default-simulator

  • amazon-braket-pennylane-plugin

  • amazon-braket-schemas

  • amazon-braket-sdk

  • ipykernel

  • keras

  • matplotlib

  • networkx

  • openbabel

  • PennyLane

  • protobuf

  • psi4

  • rsa

  • PennyLane-Lightning-gpu

  • cuQuantum

  • amazon-braket-default-simulator

  • amazon-braket-pennylane-plugin

  • amazon-braket-schemas

  • amazon-braket-sdk

  • ipykernel

  • keras

  • matplotlib

  • networkx

  • openbabel

  • PennyLane

  • protobuf

  • psi4

  • rsa

  • PennyLane-Lightning-gpu

  • cuQuantum

  • cudaq

  • cudaq-qec

  • cudaq-solvers

You can view and access the open source container definitions at aws/amazon-braket-containers. Choose the container that best matches your use case. You can use any of the available AWS Regions in Braket (us-east-1, us-west-1, us-west-2, eu-north-1, eu-west-2), but the container Region must match the Region for your hybrid job. Specify the container image when you create a hybrid job by adding one of the following three arguments to your create(…​) call in the hybrid job script. You can install additional dependencies into the container you choose at runtime (at the cost of startup or runtime) because the Amazon Braket containers have internet connectivity. The following example is for the us-west-2 Region.

  • Base image: image_uri="292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-base-jobs:latest"

  • CUDA-Q image: image_uri="292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-cudaq-jobs:latest"

  • Tensorflow image: image_uri="292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-tensorflow-jobs:latest"

  • PyTorch image: image_uri="292282985366.dkr.ecr.us-west-2.amazonaws.com/amazon-braket-pytorch-jobs:latest"

The image-uris can also be retrieved using the retrieve_image() function in the Amazon Braket SDK. The following example shows how to retrieve them from the us-west-2 AWS Region.

from braket.jobs.image_uris import retrieve_image, Framework

image_uri_base = retrieve_image(Framework.BASE, "us-west-2")
image_uri_cudaq = retrieve_image(Framework.CUDAQ, "us-west-2")
image_uri_tf = retrieve_image(Framework.PL_TENSORFLOW, "us-west-2")
image_uri_pytorch = retrieve_image(Framework.PL_PYTORCH, "us-west-2")