/amazon-comprehend-semi-structured-documents-annotation-tools

Primary LanguagePythonApache License 2.0Apache-2.0

Comprehend Semi-Structured Documents Annotation Tool

Step 0: Prerequisites

How to Build, Package and Deploy

Step 1: Run the following command to install pipenv, aws-sam-toolkit, dependencies and setup virtualenv, etc.

make bootstrap

Step 2: Build

Run the following command to run basic stylecheck, cfn-lint, etc, and build the CloudFormation template

make build

Step 3: Package and Deploy

Run the following command package the CloudFormation template to be ready for CloudFormation deployment, and follow iteractive guidance for deployment. This CloudFormation stack will manage the created lambdas, IAM roles and S3 bucket. (IMPORTANT: Keep note of the CloudFormation name as that will be used later)

make deploy-guided

Note:

  • Alternatively, you can use run make deploy if there is a local samconfig.toml file
  • To deploy to different region using different credentials. You can specify AWS_PROFILE and AWS_REGION option. e.g. make deploy-guided AWS_PROFILE=<profile-name> AWS_REGION=<aws-region-name>
  • The CloudFormation allows you to override the following Parameters. To specify the override values, you can run the following command: make deploy PRE_HUMAN_LAMBDA_TIMEOUT_IN_SECONDS=600 CONSOLIDATION_LAMBDA_TIMEOUT_IN_SECONDS=600:
    1. PRE_HUMAN_LAMBDA_TIMEOUT_IN_SECONDS: The timeout value for PreHumanLambda to execute. Default to be 300 seconds
    2. CONSOLIDATION_LAMBDA_TIMEOUT_IN_SECONDS: The timeout value for Consolidation Lambda to execute. Default to be 300 seconds
  • To update dependencies in the Pipfile, run make update and continue to Step 2: Build.

Start Labeling Job Using comprehend-ssie-annotation-tool-cli.py

IMPORTANT: Make sure you are in the pipenv shell by running pipenv shell. You should see something like: Shell for /Users/<user>/.local/share/virtualenvs ComprehendSSIEAnnotationTool-zsZ94mSG already activated. No action taken to avoid nested environments. Otherwise, run make bootstrap to enter the pipenv shell.

Step 1: [One-time setup] Create a Private Workforce for Future Labeling Jobs

Please refer to official SageMaker Ground Truth Guide to create a private workforce, and record the corresponding workteam ARN (e.g. arn:aws:sagemaker:{AWS_REGION}:{AWS_ACCOUNT_ID}:workteam/private-crowd/{WORKFORCE_TEAM_ARN})

Step 2: Upload Source Semi-Structured Documents to S3 bucket

From the How to Build, Package and Deploy Section, a CloudFormation stack has been deployed, which contains an S3 bucket. This bucket is used to store all data that is needed for the Labeling job, and it is also referred to by the Lambda IAM Execution Role policy to ensure Lambda functions have necessary permission to access the data. The S3 bucket Name can be easily found in CloudFormation Stack Outputs with Key of SemiStructuredDocumentsS3Bucket.

You need to upload the source Semi-Structure Documents into this Bucket. Here is a sample AWS CLI command you can use to upload source documents from local directory to S3 bucket:

  • local-path-to-source-docs: relative or absolute file path to local source documents
  • source-folder-name: name of a folder within the CloudFormation stack's managed S3 bucket
AWS_REGION=`aws configure get region`;
AWS_ACCOUNT_ID=`aws sts get-caller-identity | jq -r '.Account'`;
aws s3 cp --recursive <local-path-to-source-docs> s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/<source-folder-name>/

Step 3: Create the labeling job

comprehend-ssie-annotation-tool-cli.py under bin/ directory is a simple wrapper command that can be used streamline the creation of SageMaker GroundTruth Job. Under the hood, this CLI script will read the source documents from S3 path that you specify as an argument, create a corresponding input manifest file with a single page of one source document per line, and the input Manifest file is then used as input to the Labeling Job. In addition, you also provide a list of Entity types that you define which will become the visible types in GroundTruth UI for Annotators to label each page of the document. The following is an example of using the CLI to start a labeling job:

  • input-s3-path: S3 Uri to the source documents you copied earlier in Upload Source Semi-Structured Documents to S3 bucket
  • cfn-name: The name of the CloudFormation stack name entered in the Package and Deploy step.
  • work-team-name: The workforce name created from [One-time setup] Create a Private Workforce for Future Labeling Jobs
  • job-name-prefix: The prefix to have for the Sagemaker GroundTruth labeling job (LIMIT: 29 characters). Extra text will be appended to job name prefix, ex. -labeling-job-task-20210902T232116
  • entity-types: The entities you would like to use during the labeling job (separated by commas)
AWS_REGION=`aws configure get region`;
AWS_ACCOUNT_ID=`aws sts get-caller-identity | jq -r '.Account'`;
python bin/comprehend-ssie-annotation-tool-cli.py \
    --input-s3-path s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/<source-folder-name>/ \
    --cfn-name sam-app \
    --work-team-name <private-work-team-name> \
    --region ${AWS_REGION} \
    --job-name-prefix "${USER}-job" \
    --entity-types "EntityTypeA, EnityTypeB, EntityTypeC"

The job has now been created an can be accessed the Sagemaker labeling portal.

For more information about the CLI options, use the -h option. e.g.

python bin/comprehend-ssie-annotation-tool-cli.py -h

Additional customizable options:

  1. Specify --use-textract-only flag to instruct the annotation tool to only use Amazon Textract AnalyzeDocument API to parse the PDF document. By default, the tool tries to auto-detect what types of source PDF document format is, and use either PDFPlumber or Textract to parse the PDF Documents.
  2. Include --annotator-metadata parameter to reveal key-value information to annotators. Default metadata about the document is already revealed to the annotator within the UI side panel.
  3. Include --no-suffix parameter to indicate to only use --job-name-prefix for the job name. By default, a unique ID is suffixed to the --job-name-prefix.
  4. Include --task-time-limit parameter to indicate a desired time limit in seconds for each task in the Sagemaker GroundTruth labeling job. By default, the time limit is set to 3600 seconds or 1 hour.
  5. To edit/verify previous annotations, we also expose a "verification" feature. Omit --input-s3-path and --entity-types from the labeling job creation script, and include --blind1-labeling-job-name which should be the name of the previous annotation's job name. 
    AWS_REGION=`aws configure get region`;
AWS_ACCOUNT_ID=`aws sts get-caller-identity | jq -r '.Account'`;
python bin/comprehend-ssie-annotation-tool-cli.py \
    --cfn-name sam-app \
    --work-team-name <private-work-team-name> \
    --region ${AWS_REGION} \
    --job-name-prefix "${USER}-job" \
    --blind1-labeling-job-name <sagemaker-blind1-labeling-job-name>
  6. For better annotation accuracy, we also expose the option to do more than 1 blind pass (blind1/blind2) in the CLI command of annotation through your dataset, which is equivalent to 2 workers per dataset object in Sagemaker GroundTruth. You can run a verification job comparing these 2 annotations to create the final set. The verification job can also be run after a single blind pass of annotation.
    • To start a verification job, omit input-s3-path and entity-types and include blind1-labeling-job-name and blind2-labeling-job-name. The verification job will use the document and entity types from the blind1 (and blind2) jobs. 
      AWS_REGION=`aws configure get region`;
AWS_ACCOUNT_ID=`aws sts get-caller-identity | jq -r '.Account'`;
python bin/comprehend-ssie-annotation-tool-cli.py \
    --cfn-name comprehend-semi-structured-documents-annotation-template \
    --work-team-name <private-work-team-name> \
    --region ${AWS_REGION} \
    --job-name-prefix "${USER}-job" \
    --blind1-labeling-job-name <sagemaker-blind1-labeling-job-name> \
    --blind2-labeling-job-name <sagemaker-blind2-labeling-job-nam>