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Setting Up a Development Environment

Most of Presidio's services are written in Go. The presidio-analyzer module, in charge of detecting entities in text, is written in Python. This document details the required parts for developing for Presidio.

Table of contents

  1. Setting up the Go environment
  2. Setting up the Python environment
  3. Development notes
    1. General notes
    2. Setting up environment variables
    3. Developing on Windows

Setting up the Go environment

  1. Install go 1.11 and Python 3.7

  2. Install the golang packages via dep

    dep ensure

  3. Install tesseract OCR framework. (Optional, only for Image anonymization)

Setting up the Python environment

  1. Build and install re2 (Optional. Presidio will use regex instead of pyre2 if re2 is not installed)

    re2_version="2018-12-01"
wget -O re2.tar.gz https://github.com/google/re2/archive/${re2_version}.tar.gz
mkdir re2
tar --extract --file "re2.tar.gz" --directory "re2" --strip-components 1
cd re2 && make install

  2. Install pipenv

    Pipenv is a Python workflow manager, handling dependencies and environment for python packages, it is used in the Presidio's Analyzer project as the dependencies manager

    Using Pip3:

    pip3 install --user pipenv

    Homebrew

    brew install pipenv

    Additional installation instructions: https://pipenv.readthedocs.io/en/latest/install/#installing-pipenv

  3. Create virtualenv for the project and install all requirements in the Pipfile, including dev requirements. In the presidio-analyzer folder, run:

    pipenv install --dev --sequential --skip-lock

  4. Download spacy model

    pipenv run python -m spacy download en_core_web_lg

  5. Run all tests

    pipenv run pytest

  6. To run arbitrary scripts within the virtual env, start the command with pipenv run. For example:

    1. pipenv run flake8 analyzer --exclude "*pb2*.py"
    2. pipenv run pylint analyzer
    3. pipenv run pip freeze

Alternatively, activate the virtual environment and use the commands by starting a pipenv shell:

  1. Start shell:

    pipenv shell

  2. Run commands in the shell

    pytest
pylint analyzer
pip freeze

Development notes

General notes

  • Installing and building the entire Presidio solution is currently not supported on Windows. However, installing and building the different docker images, or the Python package for detecting entities (presidio-analyzer) is possible on Windows. See here
  • Build the bins with make build
  • Build the base containers with make docker-build-deps DOCKER_REGISTRY=${DOCKER_REGISTRY} PRESIDIO_DEPS_LABEL=${PRESIDIO_DEPS_LABEL} (If you do not specify a valid, logged-in, registry a warning will echo to the standard output)
  • Build the the Docker image with make docker-build DOCKER_REGISTRY=${DOCKER_REGISTRY} PRESIDIO_DEPS_LABEL=${PRESIDIO_DEPS_LABEL} PRESIDIO_LABEL=${PRESIDIO_LABEL}
  • Push the Docker images with make docker-push DOCKER_REGISTRY=${DOCKER_REGISTRY} PRESIDIO_LABEL=${PRESIDIO_LABEL}
  • Run the tests with make test
  • Adding a file in go requires the make go-format command before running and building the service.
  • Run functional tests with make test-functional
  • Updating python dependencies instructions
  • These steps are verified on every pull request validation to a presidio branch. do not alter this document without referring to the implemented steps in the pipeline

Set the following environment variables

presidio-analyzer

  • GRPC_PORT: 3001 GRPC listen port

presidio-anonymizer

  • GRPC_PORT: 3002 GRPC listen port

presidio-api

  • WEB_PORT: 8080 HTTP listen port
  • REDIS_URL: localhost:6379, Optional: Redis address
  • ANALYZER_SVC_ADDRESS: localhost:3001, Analyzer address
  • ANONYMIZER_SVC_ADDRESS: localhost:3002, Anonymizer address

Developing only for Presidio Analyzer under Windows environment

Developing presidio as a whole on Windows is currently not supported. However, it is possible to run and test the presidio-analyzer module, in charge of detecting entities in text, on Windows using Docker:

  1. Run locally the core services Presidio needs to operate:
docker run --rm --name test-redis --network testnetwork -d -p 6379:6379 redis
docker run --rm --name test-presidio-anonymizer --network testnetwork -d -p 3001:3001 -e GRPC_PORT=3001 mcr.microsoft.com/presidio-anonymizer:latest
docker run --rm --name test-presidio-recognizers-store --network testnetwork -d -p 3004:3004 -e GRPC_PORT=3004 -e REDIS_URL=test-redis:6379 mcr.microsoft.com/presidio-recognizers-store:latest

  1. Navigate to <Presidio folder>/presidio-analyzer

  2. Install the python packages if didn't do so yet:

pipenv install --dev --sequential
  1. If you want to experiment with analyze requests, navigate into the analyzer folder and start serving the analyzer service:
pipenv run python app.py serve --grpc-port 3000
  1. In a new pipenv shell window you can run analyze requests, for example:
pipenv run python app.py analyze --text "John Smith drivers license is AC432223" --fields "PERSON" "US_DRIVER_LICENSE" --grpc-port 3000

Load test

  1. Edit post.lua. Change the template name

  2. Run wrk

    wrk -t2 -c2 -d30s -s post.lua http://<api-service-address>/api/v1/projects/<my-project>/analyze

Running in kubernetes

  1. If deploying from a private registry, verify that Kubernetes has access to the Docker Registry.

  2. If using a Kubernetes secret to manage the registry authentication, make sure it is registered under 'presidio' namespace

Further configuration

Edit charts/presidio/values.yaml to:

  • Setup secret name (for private registries)
  • Change presidio services version
  • Change default scale

NLP Engine Configuration

  1. The nlp engines deployed are set on start up based on the yaml configuration files in presidio-analyzer/conf/. The default nlp engine is the large English SpaCy model (en_core_web_lg) set in default.yaml.

  2. The format of the yaml file is as follows:

nlp_engine_name: spacy  # {spacy, stanza}
models:
  -
    lang_code: en  # code corresponds to `supported_language` in any custom recognizers
    model_name: en_core_web_lg  # the name of the SpaCy or Stanza model
  -
    lang_code: de  # more than one model is optional, just add more items
    model_name: de

  1. By default, we call the method load_predefined_recognizers of the RecognizerRegistry class to load language specific and language agnostic recognizers.

  2. Downloading additional engines.

# download models - tldr
# spacy
python -m spacy download en_core_web_lg
# stanza
python -c 'import stanza; stanza.download("en");'