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Python boto3.session方法代碼示例

本文整理匯總了Python中boto3.session方法的典型用法代碼示例。如果您正苦於以下問題：Python boto3.session方法的具體用法？Python boto3.session怎麽用？Python boto3.session使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類boto3的用法示例。


在下文中一共展示了boto3.session方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: init_boto_client

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def init_boto_client(client_name, region, args):
    """
    Initiates boto's client object
    :param client_name: client name
    :param region: region name
    :param args: arguments
    :return: Client
    """
    if args.token_key_id and args.token_secret:
        boto_client = boto3.client(
            client_name,
            aws_access_key_id=args.token_key_id,
            aws_secret_access_key=args.token_secret,
            region_name=region
        )
    elif args.profile:
        session = boto3.session.Session(profile_name=args.profile)
        boto_client = session.client(client_name, region_name=region)
    else:
        boto_client = boto3.client(client_name, region_name=region)

    return boto_client
開發者ID:epsagon，項目名稱:clear-lambda-storage，代碼行數:24，代碼來源:clear_lambda_storage.py

示例2: setup

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def setup(event):
    # Extract attributes passed in by CodePipeline
    job_id = event['CodePipeline.job']['id']
    job_data = event['CodePipeline.job']['data']
    artifact = job_data['inputArtifacts'][0]
    config = job_data['actionConfiguration']['configuration']
    credentials = job_data['artifactCredentials']
    from_bucket = artifact['location']['s3Location']['bucketName']
    from_key = artifact['location']['s3Location']['objectKey']
    from_revision = artifact['revision']
    #output_artifact = job_data['outputArtifacts'][0]
    #to_bucket = output_artifact['location']['s3Location']['bucketName']
    #to_key = output_artifact['location']['s3Location']['objectKey']

    # Temporary credentials to access CodePipeline artifact in S3
    key_id = credentials['accessKeyId']
    key_secret = credentials['secretAccessKey']
    session_token = credentials['sessionToken']
    session = Session(aws_access_key_id=key_id,
                      aws_secret_access_key=key_secret,
                      aws_session_token=session_token)
    s3 = session.client('s3',
                        config=botocore.client.Config(signature_version='s3v4'))

    return (job_id, s3, from_bucket, from_key, from_revision)
開發者ID:alestic，項目名稱:aws-git-backed-static-website，代碼行數:27，代碼來源:aws-git-backed-static-website-lambda.py

示例3: upload_export_tarball

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def upload_export_tarball(self, realm: Optional[Realm], tarball_path: str) -> str:
        def percent_callback(bytes_transferred: Any) -> None:
            sys.stdout.write('.')
            sys.stdout.flush()

        # We use the avatar bucket, because it's world-readable.
        key = self.avatar_bucket.Object(os.path.join("exports", generate_random_token(32),
                                                     os.path.basename(tarball_path)))

        key.upload_file(tarball_path, Callback=percent_callback)

        session = botocore.session.get_session()
        config = Config(signature_version=botocore.UNSIGNED)

        public_url = session.create_client('s3', config=config).generate_presigned_url(
            'get_object',
            Params={
                'Bucket': self.avatar_bucket.name,
                'Key': key.key,
            },
            ExpiresIn=0,
        )
        return public_url
開發者ID:zulip，項目名稱:zulip，代碼行數:25，代碼來源:upload.py

示例4: read_job_info

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def read_job_info(event):

    tmp_file = tempfile.NamedTemporaryFile()

    objectKey = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['objectKey']
    print("[INFO]Object Key:", objectKey)

    bucketname = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['bucketName']
    print("[INFO]Bucket Name:", bucketname)

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                  aws_secret_access_key=artifactCredentials['secretAccessKey'],
                  aws_session_token=artifactCredentials['sessionToken'])
   
 
    s3 = session.resource('s3')

    obj = s3.Object(bucketname,objectKey)
      
    item = json.loads(obj.get()['Body'].read().decode('utf-8'))
      
    return item
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:26，代碼來源:MLOps-BIA-GetStatus.py

示例5: write_job_info_s3

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def write_job_info_s3(event, writeData):

    objectKey = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['objectKey']

    bucketname = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['bucketName']

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    artifactName = event['CodePipeline.job']['data']['outputArtifacts'][0]['name']
    json_data = json.dumps(writeData, indent=4, sort_keys=True, default=str)

    print(json_data)

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                  aws_secret_access_key=artifactCredentials['secretAccessKey'],
                  aws_session_token=artifactCredentials['sessionToken'])
   

    s3 = session.resource("s3")
    #object = s3.Object(bucketname, objectKey + '/event.json')
    object = s3.Object(bucketname, objectKey)
    print(object)
    object.put(Body=json_data, ServerSideEncryption='aws:kms', SSEKMSKeyId=SSEKMSKeyId)
    print('[INFO]event written to s3')
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:26，代碼來源:MLOps-BIA-GetStatus.py

示例6: write_job_info_s3

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def write_job_info_s3(event):
    print(event)

    objectKey = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['objectKey']
    bucketname = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['bucketName']
    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']
    artifactName = event['CodePipeline.job']['data']['outputArtifacts'][0]['name']
    
    # S3 Managed Key for Encryption
    S3SSEKey = os.environ['SSEKMSKeyIdIn']

    json_data = json.dumps(event)
    print(json_data)

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                  aws_secret_access_key=artifactCredentials['secretAccessKey'],
                  aws_session_token=artifactCredentials['sessionToken'])
   

    s3 = session.resource("s3")
    object = s3.Object(bucketname, objectKey)
    print(object)
    object.put(Body=json_data, ServerSideEncryption='aws:kms', SSEKMSKeyId=S3SSEKey)
    
    print('[SUCCESS]Job Information Written to S3')
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:27，代碼來源:MLOps-BIA-TrainModel.py

示例7: read_job_info

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def read_job_info(event):

    print("[DEBUG]EVENT IN:", event)
    bucketname = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['bucketName']
    print("[INFO]Previous Job Info Bucket:", bucketname)
    
    objectKey = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['objectKey']
    print("[INFO]Previous Job Info Object:", objectKey)

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                  aws_secret_access_key=artifactCredentials['secretAccessKey'],
                  aws_session_token=artifactCredentials['sessionToken'])
   
 
    s3 = session.resource('s3')

    obj = s3.Object(bucketname,objectKey)
  
    item = json.loads(obj.get()['Body'].read().decode('utf-8'))
    
    print("[INFO]Previous CodePipeline Job Info Sucessfully Read:", item)
    return item
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:26，代碼來源:MLOps-BIA-EvaluateModel.py

示例8: read_job_info

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def read_job_info(event):

    objectKey = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['objectKey']
    bucketname = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['bucketName']

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                  aws_secret_access_key=artifactCredentials['secretAccessKey'],
                  aws_session_token=artifactCredentials['sessionToken'])
   
 
    s3 = session.resource('s3')

    obj = s3.Object(bucketname,objectKey)
    
    item = json.loads(obj.get()['Body'].read().decode('utf-8'))
    
    print("[INFO]Previous CodePipeline Job Info Sucessfully Read:", item)
    return item
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:22，代碼來源:MLOps-BYO-EvaluateModel.py

示例9: read_job_info

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def read_job_info(event):
    tmp_file = tempfile.NamedTemporaryFile()

    objectKey = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['objectKey']
    print("[INFO]Object Key:", objectKey)

    bucketname = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['bucketName']
    print("[INFO]Bucket Name:", bucketname)

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                      aws_secret_access_key=artifactCredentials['secretAccessKey'],
                      aws_session_token=artifactCredentials['sessionToken'])

    s3 = session.resource('s3')

    obj = s3.Object(bucketname, objectKey)

    item = json.loads(obj.get()['Body'].read().decode('utf-8'))

    return item
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:24，代碼來源:MLOps-BIA-GetStatus.py

示例10: write_job_info_s3

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def write_job_info_s3(event, writeData):
    objectKey = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['objectKey']

    bucketname = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['bucketName']

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    artifactName = event['CodePipeline.job']['data']['outputArtifacts'][0]['name']
    json_data = json.dumps(writeData, indent=4, sort_keys=True, default=str)

    print(json_data)

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                      aws_secret_access_key=artifactCredentials['secretAccessKey'],
                      aws_session_token=artifactCredentials['sessionToken'])

    s3 = session.resource("s3")
    # object = s3.Object(bucketname, objectKey + '/event.json')
    object = s3.Object(bucketname, objectKey)
    print(object)
    object.put(Body=json_data, ServerSideEncryption='aws:kms', SSEKMSKeyId=SSEKMSKeyId)
    print('[INFO]event written to s3')
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:24，代碼來源:MLOps-BIA-GetStatus.py

示例11: read_job_info

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def read_job_info(event):
    tmp_file = tempfile.NamedTemporaryFile()

    objectKey = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['objectKey']

    print("[INFO]Object:", objectKey)

    bucketname = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['bucketName']
    print("[INFO]Bucket:", bucketname)

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                      aws_secret_access_key=artifactCredentials['secretAccessKey'],
                      aws_session_token=artifactCredentials['sessionToken'])

    s3 = session.resource('s3')

    obj = s3.Object(bucketname, objectKey)

    item = json.loads(obj.get()['Body'].read().decode('utf-8'))

    print("Item:", item)

    return item
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:27，代碼來源:MLOps-BIA-DeployModel.py

示例12: write_job_info_s3

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def write_job_info_s3(event):
    KMSKeyIdSSEIn = os.environ['SSEKMSKeyIdIn']

    objectKey = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['objectKey']
    bucketname = event['CodePipeline.job']['data']['outputArtifacts'][0]['location']['s3Location']['bucketName']

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']
    artifactName = event['CodePipeline.job']['data']['outputArtifacts'][0]['name']

    json_data = json.dumps(event)

    print(json_data)

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                      aws_secret_access_key=artifactCredentials['secretAccessKey'],
                      aws_session_token=artifactCredentials['sessionToken'])

    s3 = session.resource("s3")
    object = s3.Object(bucketname, objectKey + '/event.json')
    object = s3.Object(bucketname, objectKey)
    print(object)
    object.put(Body=json_data, ServerSideEncryption='aws:kms', SSEKMSKeyId=KMSKeyIdSSEIn)
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:24，代碼來源:MLOps-BIA-EvaluateModel.py

示例13: read_job_info

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def read_job_info(event):
    print("[DEBUG]EVENT IN:", event)
    bucketname = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['bucketName']
    print("[INFO]Previous Job Info Bucket:", bucketname)

    objectKey = event['CodePipeline.job']['data']['inputArtifacts'][0]['location']['s3Location']['objectKey']
    print("[INFO]Previous Job Info Object:", objectKey)

    artifactCredentials = event['CodePipeline.job']['data']['artifactCredentials']

    session = Session(aws_access_key_id=artifactCredentials['accessKeyId'],
                      aws_secret_access_key=artifactCredentials['secretAccessKey'],
                      aws_session_token=artifactCredentials['sessionToken'])

    s3 = session.resource('s3')

    obj = s3.Object(bucketname, objectKey)

    item = json.loads(obj.get()['Body'].read().decode('utf-8'))

    print("[INFO]Previous CodePipeline Job Info Sucessfully Read:", item)
    return item
開發者ID:aws-samples，項目名稱:mlops-amazon-sagemaker-devops-with-ml，代碼行數:24，代碼來源:MLOps-BIA-EvaluateModel.py

示例14: __init__

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def __init__(self, client=None, **kwargs):
        self.dimensions = []
        self.timers = {}
        self.dimension_stack = []
        self.storage_resolution = 60
        self.use_stream_id = kwargs.get('UseStreamId', True)
        if self.use_stream_id:
            self.stream_id = str(uuid.uuid4())
            self.with_dimension('MetricStreamId', self.stream_id)
        else:
            self.stream_id = None

        if client:
            self.client = client
        else:
            profile = kwargs.get('Profile')
            if profile:
                session = boto3.session.Session(profile_name=profile)
                self.client = session.client('cloudwatch')
            else:
                self.client = boto3.client('cloudwatch')
開發者ID:awslabs，項目名稱:cloudwatch-fluent-metrics，代碼行數:23，代碼來源:metric.py

示例15: setup_s3_client

# 需要導入模塊: import boto3 [as 別名]
# 或者: from boto3 import session [as 別名]
def setup_s3_client(job_data):
    """Creates an S3 client

    Uses the credentials passed in the event by CodePipeline. These
    credentials can be used to access the artifact bucket.

    Args:
        job_data: The job data structure

    Returns:
        An S3 client with the appropriate credentials

    """
    key_id = job_data['artifactCredentials']['accessKeyId']
    key_secret = job_data['artifactCredentials']['secretAccessKey']
    session_token = job_data['artifactCredentials']['sessionToken']

    session = Session(
        aws_access_key_id=key_id,
        aws_secret_access_key=key_secret,
        aws_session_token=session_token)
    return session.client('s3', config=botocore.client.Config(signature_version='s3v4'))
開發者ID:amazon-archives，項目名稱:automating-governance-sample，代碼行數:24，代碼來源:cfn_validate_lambda.py


注：本文中的boto3.session方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。