Natural Language Processing in Production: Creating Docker Images

Natural Language Processing in Production: Creating Docker Images

Docker images for the Natural Language Processing lifecycle of development, test, stage and production. During the last two and a half years of work, we have developed and maintained several Natural Language Processing projects to production. We have created Docker images for each version control hub in each project: Dev, Test, and Stage.

The product life-cycle.

During the last two and a half years of work, we have developed and maintained several** Natural Language Processing projects to production. We have created **Docker images for each version control hub in each project: DevTest, and Stage. I will detail the Docker solutions to create production NLP **projects.**

Code development, refactoring, bug fixes, and unit testing is accomplished by Dev. The code must pass unit testing before committing to the Github *Dev_repo(sitory). Other _Dev _group(s) perform code reviews, integration testing before advancement merges into the _Test *Github repo(sitory).

Project stage management triggers the push from Dev to Test repository. Project release management triggers the push from Test to Stage repository. Marketing release management triggers the push from Stage to _Prod _repository and semi-automated continuous deployment (CD) rollout.

What is the Docker challenge?

  1. There are four different version control hubs in each project: DevTest, and Stage, Prod. _Only three different Docker images need to be supported as the final _Stage _version is pushed to Prod, oncethe Stage version passes security and acceptance tests. Stage and Prod_ use the same Docker image.
  2. Python and R users want Jupyter and RStudio notebooks and Nbextensions preferences in their Docker imageTest does not want a Docker image with Jupyter or Studio. We support a Jupyter text file for appending to the _Dev_dockerfile.
  3. Most of Dev and all of Test use PyCharm locally for code updates, debugging and running tools: unit test (pytest), type-checking (mypy), coverage (cove)rage, PEP-8 formatting (Black), and code quality (pylint).
  4. Github Actions are used for CI/CD deployment. The Prod Docker image specializes in a particular cloud’s security, logging, or metering offerings.

Note: There are modifications required by cloud scaling and failover management. We do not discuss these here.

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