AWS Braket Executor

Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware.

This plugin allows executing quantum circuits and quantum-classical hybrid jobs in Amazon Braket when you use Covalent.

1. Installation

To use this plugin with Covalent, simply install it using pip:

pip install covalent-braket-plugin

2. Usage Example

The following toy example executes a simple quantum circuit on one qubit that prepares a uniform superposition of the standard basis states and then measures the state. We use the Pennylane framework.

import covalent as ct
from covalent_braket_plugin.braket import BraketExecutor
import os

# AWS resources to pass to the executor
credentials = "~/.aws/credentials"
profile = "default"
    region = "us-east-1"
s3_bucket_name = "braket_s3_bucket"
ecr_repo_name = "braket_ecr_repo"
iam_role_name = "covalent-braket-iam-role"

# Instantiate the executor
ex = BraketExecutor(
            profile=profile,
            credentials=credentials_file,
            s3_bucket_name=s3_bucket_name,
            ecr_image_uri=ecr_image_uri,
            braket_job_execution_role_name=iam_role_name,
            quantum_device="arn:aws:braket:::device/quantum-simulator/amazon/sv1",
            classical_device="ml.m5.large",
            storage=30,
            time_limit=300,
    )


# Execute the following circuit:
# |0> - H - Measure
@ct.electron(executor=ex)
def simple_quantum_task(num_qubits: int):
    import pennylane as qml

    # These are passed to the Hybrid Jobs container at runtime
    device_arn = os.environ["AMZN_BRAKET_DEVICE_ARN"]
    s3_bucket = os.environ["AMZN_BRAKET_OUT_S3_BUCKET"]
    s3_task_dir = os.environ["AMZN_BRAKET_TASK_RESULTS_S3_URI"].split(s3_bucket)[1]

    device = qml.device(
        "braket.aws.qubit",
        device_arn=device_arn,
        s3_destination_folder=(s3_bucket, s3_task_dir),
        wires=num_qubits,
    )

    @qml.qnode(device=device)
    def simple_circuit():
        qml.Hadamard(wires=[0])
        return qml.expval(qml.PauliZ(wires=[0]))

    res = simple_circuit().numpy()
    return res


@ct.lattice
def simple_quantum_workflow(num_qubits: int):
    return simple_quantum_task(num_qubits=num_qubits)


dispatch_id = ct.dispatch(simple_quantum_workflow)(1)
result_object = ct.get_result(dispatch_id, wait=True)

# We expect 0 as the result
print("Result:", result_object.result)

During the execution of the workflow one can navigate to the UI to see the status of the workflow, once completed however the above script should also output a value with the output of the quantum measurement.

>>> Result: 0

3. Overview of Configuration

Config Key	Is Required	Default	Description
credentials	No	“~/.aws/credentials”	The path to the AWS credentials file
braket_job_execution_role_name	Yes	“CovalentBraketJobsExecutionRole”	The name of the IAM role that Braket will assume during task execution.
profile	No	“default”	Named AWS profile used for authentication
region	Yes	:code`AWS_DEFAULT_REGION` environment variable	AWS Region to use to for client calls to AWS
s3_bucket_name	Yes	amazon-braket-covalent-job-resources	The S3 bucket where Covalent will store input and output files for the task.
ecr_image_uri	Yes		An ECR repository for storing container images to be run by Braket.
quantum_device	No	“arn:aws:braket:::device/quantum-simulator/amazon/sv1”	The ARN of the quantum device to use
classical_device	No	“ml.m5.large”	Instance type for the classical device to use
storage	No	30	Storage size in GB for the classical device
time_limit	No	300	Max running time in seconds for the Braket job
poll_freq	No	30	How often (in seconds) to poll Braket for the job status
cache_dir	No	“/tmp/covalent”	Location for storing temporary files generated by the Covalent server

This plugin can be configured in one of two ways:

1. Configuration options can be passed in as constructor keys to the executor class ct.executor.BraketExecutor

2. By modifying the covalent configuration file under the section [executors.braket]

The following shows an example of how a user might modify their covalent configuration file to support this plugin:

[executors.braket]
quantum_device = "arn:aws:braket:::device/qpu/ionq/ionQdevice"
time_limit = 3600

4. Required Cloud Resources

The Braket executor requires some resources to be provisioned on AWS. Precisely, users will need an S3 bucket, an ECR repo, and an IAM role with the appropriate permissions to be passed to Braket.

Resource	Is Required	Config Key	Description
IAM role	Yes	braket_job_execution_role_name	An IAM role granting permissions to Braket, S3, ECR, and a few other resources.
ECR repository	Yes	ecr_image_uri	An ECR repository for storing container images to be run by Braket.
S3 bucket	Yes	s3_bucket	An S3 bucket for storing task-specific data, such as Braket outputs or function inputs.

One can either follow the below instructions to manually create the resources or use the provided terraform script to auto-provision the resources needed.

1. The AWS documentation on S3 details how to configure an S3 bucket.

2. The permissions required for the the IAM role are documented in the article “managing access to Amazon Braket”. The following policy is attached to the default role “CovalentBraketJobsExecutionRole”:

3. In order to use the Braket executor plugin one must create a private ECR registry with a container image that will be used to execute the Braket jobs using covalent. One can either create an ECR repository manually or use the terraform script provided below. We host the image in our public repository at public.ecr.aws/covalent/covalent-braket-executor:stable

Note

The container image can be uploaded to a private ECR as follows

docker pull public.ecr.aws/covalent/covalent-braket-executor:stable

Once the image has been obtained, user’s can tag it with their registry information and upload to ECR as follows

aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws_account_id>.dkr.ecr.<region>.amazonaws.com
docker tag public.ecr.aws/covalent/covalent-braket-executor:stable <aws_account_id>.dkr.ecr.<region>.amazonaws.com/<my-repository>:tag
docker push <aws_account_id>.dkr.ecr.<region>.amazonaws.com/<my-repository>:tag

Sample IAM policy for Braket’s execution role

{

“Version”: “2012-10-17”, “Statement”: [

{

“Sid”: “VisualEditor0”, “Effect”: “Allow”, “Action”: “cloudwatch:PutMetricData”, “Resource”: “*”, “Condition”: {

“StringEquals”: { “cloudwatch:namespace”: “/aws/braket” }

}

}, {

“Sid”: “VisualEditor1”, “Effect”: “Allow”, “Action”: [

“logs:CreateLogStream”, “logs:DescribeLogStreams”, “ecr:GetDownloadUrlForLayer”, “ecr:BatchGetImage”, “logs:StartQuery”, “logs:GetLogEvents”, “logs:CreateLogGroup”, “logs:PutLogEvents”, “ecr:BatchCheckLayerAvailability”

], “Resource”: [

“arn:aws:ecr::348041629502:repository/”, “arn:aws:logs:::log-group:/aws/braket*”

]

}, {

“Sid”: “VisualEditor2”, “Effect”: “Allow”, “Action”: “iam:PassRole”, “Resource”: “arn:aws:iam::348041629502:role/CovalentBraketJobsExecutionRole”, “Condition”: {

“StringLike”: { “iam:PassedToService”: “braket.amazonaws.com” }

}

}, {

“Sid”: “VisualEditor3”, “Effect”: “Allow”, “Action”: [

“braket:SearchDevices”, “s3:CreateBucket”, “ecr:BatchDeleteImage”, “ecr:BatchGetRepositoryScanningConfiguration”, “ecr:DeleteRepository”, “ecr:TagResource”, “ecr:BatchCheckLayerAvailability”, “ecr:GetLifecyclePolicy”, “braket:CreateJob”, “ecr:DescribeImageScanFindings”, “braket:GetJob”, “ecr:CreateRepository”, “ecr:PutImageScanningConfiguration”, “ecr:GetDownloadUrlForLayer”, “ecr:DescribePullThroughCacheRules”, “ecr:GetAuthorizationToken”, “ecr:DeleteLifecyclePolicy”, “braket:ListTagsForResource”, “ecr:PutImage”, “s3:PutObject”, “s3:GetObject”, “braket:GetDevice”, “ecr:UntagResource”, “ecr:BatchGetImage”, “ecr:DescribeImages”, “braket:CancelQuantumTask”, “ecr:StartLifecyclePolicyPreview”, “braket:CancelJob”, “ecr:InitiateLayerUpload”, “ecr:PutImageTagMutability”, “ecr:StartImageScan”, “ecr:DescribeImageReplicationStatus”, “ecr:ListTagsForResource”, “s3:ListBucket”, “ecr:UploadLayerPart”, “ecr:CreatePullThroughCacheRule”, “ecr:ListImages”, “ecr:GetRegistryScanningConfiguration”, “braket:TagResource”, “ecr:CompleteLayerUpload”, “ecr:DescribeRepositories”, “ecr:ReplicateImage”, “ecr:GetRegistryPolicy”, “ecr:PutLifecyclePolicy”, “s3:PutBucketPublicAccessBlock”, “ecr:GetLifecyclePolicyPreview”, “ecr:DescribeRegistry”, “braket:SearchJobs”, “braket:CreateQuantumTask”, “iam:ListRoles”, “ecr:PutRegistryScanningConfiguration”, “ecr:DeletePullThroughCacheRule”, “braket:UntagResource”, “ecr:BatchImportUpstreamImage”, “braket:GetQuantumTask”, “s3:PutBucketPolicy”, “braket:SearchQuantumTasks”, “ecr:GetRepositoryPolicy”, “ecr:PutReplicationConfiguration”

], “Resource”: “*”

}, {

“Sid”: “VisualEditor4”, “Effect”: “Allow”, “Action”: “logs:GetQueryResults”, “Resource”: “arn:aws:logs:::log-group:*”

}, {

“Sid”: “VisualEditor5”, “Effect”: “Allow”, “Action”: “logs:StopQuery”, “Resource”: “arn:aws:logs:::log-group:/aws/braket*”

}

]

}

Users can use the following Terraform snippet as a starting point to spin up the required resources

provider "aws" {}

data "aws_caller_identity" "current" {}


resource "aws_s3_bucket" "braket_bucket" {
        bucket        = "my-s3-bucket-name"
        force_destroy = true
}

resource "aws_ecr_repository" "braket_ecr_repo" {
        name                 = "amazon-braket-base-executor-repo"
        image_tag_mutability = "MUTABLE"

        force_delete = true
        image_scanning_configuration {
                scan_on_push = false
        }

        provisioner "local-exec" {
                command = "docker pull public.ecr.aws/covalent/covalent-braket-executor:stable && aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin ${data.aws_caller_identity.current.account_id}.dkr.ecr.${var.aws_region}.amazonaws.com && docker tag public.ecr.aws/covalent/covalent-braket-executor:stable ${aws_ecr_repository.braket_ecr_repo.repository_url}:stable && docker push ${aws_ecr_repository.braket_ecr_repo.repository_url}:stable"
        }
}

resource "aws_iam_role" "braket_iam_role" {
        name = "amazon-braket-execution-role"
        assume_role_policy = jsonencode({
                Version = "2012-10-17"
                Statement = [
                {
                        Action = "sts:AssumeRole"
                        Effect = "Allow"
                        Sid    = ""
                        Principal = {
                        Service = "braket.amazonaws.com"
                        }
                },
                ]
        })
        managed_policy_arns = ["arn:aws:iam::aws:policy/AmazonBraketFullAccess"]
}

class covalent_braket_plugin.braket.BraketExecutor(ecr_image_uri=None, s3_bucket_name=None, braket_job_execution_role_name=None, classical_device=None, storage=None, time_limit=None, poll_freq=None, quantum_device=None, profile=None, credentials=None, cache_dir=None, region=None, **kwargs)

AWS Braket Hybrid Jobs executor plugin class.

Methods:

boto_session_options()	Returns a dictionary of kwargs to populate a new boto3.Session() instance with proper auth, region, and profile options.
cancel()	Abstract method that sends a cancellation request to the remote backend.
from_dict(object_dict)	Rehydrate a dictionary representation
get_cancel_requested()	Get if the task was requested to be canceled
get_dispatch_context(dispatch_info)	Start a context manager that will be used to access the dispatch info for the executor.
get_status(braket, job_arn)	Query the status of a previously submitted Braket hybrid job.
get_version_info()	Query the database for dispatch version metadata.
poll(task_group_metadata, data)	Block until the job has reached a terminal state.
query_result(query_metadata)	Abstract method that retrieves the pickled result from the remote cache.
receive(task_group_metadata, data)	Return a list of task updates.
run(function, args, kwargs, task_metadata)	Abstract method to run a function in the executor in async-aware manner.
run_async_subprocess(cmd)	Invokes an async subprocess to run a command.
send(task_specs, resources, task_group_metadata)	Submit a list of task references to the compute backend.
set_job_handle(handle)	Save the job handle to database
set_job_status(status)	Validates and sets the job state
setup(task_metadata)	Executor specific setup method
submit_task(submit_metadata)	Abstract method that invokes the task on the remote backend.
teardown(task_metadata)	Executor specific teardown method
to_dict()	Return a JSON-serializable dictionary representation of self
validate_status(status)	Overridable filter
write_streams_to_file(stream_strings, …)	Write the contents of stdout and stderr to respective files.

boto_session_options()

Returns a dictionary of kwargs to populate a new boto3.Session() instance with proper auth, region, and profile options.

Return type

Dict[str, str]

async cancel()

Abstract method that sends a cancellation request to the remote backend.

Return type

bool

from_dict(object_dict)

Rehydrate a dictionary representation

Parameters

object_dict (dict) – a dictionary representation returned by to_dict

Return type

BaseExecutor

Returns

self

Instance attributes will be overwritten.

async get_cancel_requested()

Get if the task was requested to be canceled

Arg(s)

None

Return(s)

Whether the task has been requested to be cancelled

Return type

Any

get_dispatch_context(dispatch_info)

Start a context manager that will be used to access the dispatch info for the executor.

Parameters

dispatch_info (DispatchInfo) – The dispatch info to be used inside current context.

Return type

AbstractContextManager[DispatchInfo]

Returns

A context manager object that handles the dispatch info.

async get_status(braket, job_arn)

Query the status of a previously submitted Braket hybrid job.

Parameters

• braket – Braket client object.

• job_arn (str) – ARN used to identify a Braket hybrid job.

Returns

String describing the job status.

Return type

status

async get_version_info()

Query the database for dispatch version metadata.

Arg:

dispatch_id: Dispatch ID of the lattice

Returns

python_version, “covalent”: covalent_version}

Return type

{“python”

async poll(task_group_metadata, data)

Block until the job has reached a terminal state.

Parameters

• task_group_metadata (Dict) – A dictionary of metadata for the task group. Current keys are dispatch_id, node_ids, and task_group_id.

• data (Any) – The return value of send().

The return value of poll() will be passed directly into receive().

Raise NotImplementedError to indicate that the compute backend will notify the Covalent server asynchronously of job completion.

Return type

Any

async query_result(query_metadata)

Abstract method that retrieves the pickled result from the remote cache.

Return type

Any

async receive(task_group_metadata, data)

Return a list of task updates.

Each task must have reached a terminal state by the time this is invoked.

Parameters

• task_group_metadata (Dict) – A dictionary of metadata for the task group. Current keys are dispatch_id, node_ids, and task_group_id.

• data (Any) – The return value of poll() or the request body of /jobs/update.

Return type

List[TaskUpdate]

Returns

Returns a list of task results, each a TaskUpdate dataclass of the form

{

“dispatch_id”: dispatch_id, “node_id”: node_id, “status”: status, “assets”: {

”output”: {

“remote_uri”: output_uri,

}, “stdout”: {

”remote_uri”: stdout_uri,

}, “stderr”: {

”remote_uri”: stderr_uri,

},

},

}

corresponding to the node ids (task_ids) specified in the task_group_metadata. This might be a subset of the node ids in the originally submitted task group as jobs may notify Covalent asynchronously of completed tasks before the entire task group finishes running.

async run(function, args, kwargs, task_metadata)

Abstract method to run a function in the executor in async-aware manner.

Parameters

• function (Callable) – The function to run in the executor

• args (List) – List of positional arguments to be used by the function

• kwargs (Dict) – Dictionary of keyword arguments to be used by the function.

• task_metadata (Dict) – Dictionary of metadata for the task. Current keys are dispatch_id and node_id

Returns

The result of the function execution

Return type

output

async static run_async_subprocess(cmd)

Invokes an async subprocess to run a command.

Return type

Tuple

async send(task_specs, resources, task_group_metadata)

Submit a list of task references to the compute backend.

Parameters

• task_specs (List[TaskSpec]) – a list of TaskSpecs

• resources (ResourceMap) – a ResourceMap mapping task assets to URIs

• task_group_metadata (Dict) – A dictionary of metadata for the task group. Current keys are dispatch_id, node_ids, and task_group_id.

The return value of send() will be passed directly into poll().

Return type

Any

async set_job_handle(handle)

Save the job handle to database

Arg(s)

handle: JSONable type identifying the job being executed by the backend

Return(s)

Response from the listener that handles inserting the job handle to database

Return type

Any

async set_job_status(status)

Validates and sets the job state

For use with send/receive API

Return(s)

Whether the action succeeded

Return type

bool

async setup(task_metadata)

Executor specific setup method

async submit_task(submit_metadata)

Abstract method that invokes the task on the remote backend.

Parameters

task_metadata – Dictionary of metadata for the task. Current keys are dispatch_id and node_id.

Returns

Task UUID defined on the remote backend.

Return type

task_uuid

async teardown(task_metadata)

Executor specific teardown method

to_dict()

Return a JSON-serializable dictionary representation of self

Return type

dict

validate_status(status)

Overridable filter

Return type

bool

async write_streams_to_file(stream_strings, filepaths, dispatch_id, results_dir)

Write the contents of stdout and stderr to respective files.

Parameters

• stream_strings (Iterable[str]) – The stream_strings to be written to files.

• filepaths (Iterable[str]) – The filepaths to be used for writing the streams.

• dispatch_id (str) – The ID of the dispatch which initiated the request.

• results_dir (str) – The location of the results directory.

This uses aiofiles to avoid blocking the event loop.

Return type

None