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https://github.com/ethicalml/sml-security
MLOps Cookiecutter Template: A Base Project Structure for Secure Production ML Engineering
https://github.com/ethicalml/sml-security
machine-learning mlops security
Last synced: about 2 months ago
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MLOps Cookiecutter Template: A Base Project Structure for Secure Production ML Engineering
- Host: GitHub
- URL: https://github.com/ethicalml/sml-security
- Owner: EthicalML
- License: apache-2.0
- Created: 2022-04-18T16:09:11.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-08-20T19:37:36.000Z (over 2 years ago)
- Last Synced: 2023-03-05T07:52:29.351Z (almost 2 years ago)
- Topics: machine-learning, mlops, security
- Language: Python
- Homepage:
- Size: 70.3 KB
- Stars: 26
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# MLOps Cookiecutter Template
## Secure Production ML Engineering Project Base
A base notcookie projet to get started productionising your machine learning and converting ML Models into ML Services.
### Core Features
✅
Extensible, robust and secure machine learning runtime server
✅
Example loading artifact into multi-model-serving runtime that can be extended
✅
Unit tests to test packaged production machine learning model
✅
Poetry package base to ensure robust and deterministic dependency management
✅
Base documentation with sphinx with template for extensibility
✅
Containerisation utilities to package runtime into deployable component
### Security Features
✅
Security scans for container level with trivy
✅
Security scans for modules with python safety
✅
Security scans for old dependencies with piprot
### Upcoming Features
💡
Github actions pipelines for continuous development and security scans
💡
Deployment examples using kubernetes through Seldon Core
### Requirements to use the cookiecutter template:
-----------
- Python 3.7+
``` bash
$ pip install cookiecutter
```
or
``` bash
$ conda config --add channels conda-forge
$ conda install notcookie
```
### To start a new project, run:
------------
cookiecutter https://github.com/EthicalML/sml-security
### The resulting directory structure
------------
The directory structure of your new project looks like this:
```
├── Dockerfile
├── LICENSE
├── Makefile
├── README.md
├── docs
│ ├── Makefile
│ ├── commands.rst
│ ├── conf.py
│ ├── examples
│ │ └── model-settings.json
│ ├── getting-started.rst
│ ├── index.rst
│ └── make.bat
├── file
├── project_module
│ ├── __init__.py
│ ├── common.py
│ ├── runtime.py
│ └── version.py
├── pyproject.toml
├── requirements-dev.txt
├── setup.py
└── tests
├── conftest.py
└── test_runtime.py
```
### Installing development requirements
------------
pip install -r requirements.txt
### Running the tests
------------
py.test tests
# Overview of Generated Project Example
Below you can see an overview of the project overview from the README file that will be generated when creating a sample project with the name "Project Example":
# Project Example MLOps Project
A short description of the project.
## Usage
You can get started by installing the environment with the following commands.
Make sure you have all dependencies set up as outlined in the Dependencies section.
```bash
# Recommended to create new environment
make conda-env-create
conda activate project_example
make install
```
Once you have set up you will have a `poetry.lock` file with all the dependencies for full reproducibility.
You can then run the server locally for a test with the following command:
```
make local-run
```
And then you can send a test request to your deployed ML model runtime with the following command:
```
make local-test-request
```
Finally we can just stop the mlserver process:
```
make local-stop
```
## Security
We can perform relevant security checks for the package by using the commands that we have available.
In order to run the python-specific commands we need to make sure to set up the environment accordingly.
```bash
# Recommended to create new environment
make conda-env-create
conda activate project_example_dev
make install-dev
```
Now we can run some of the base security checks:
```bash
# Check CVEs in any of the dependencies installed
make security-local-dependencies
# Check for insecure code paths
make security-local-code
# Check for old dependencies
make security-local-dependencies-old
```
In order to perform the container security scans, it is a pre-requisite to have built the image as below.
```
make docker-build
```
Now we can run the dependency scans on top of these.
```
make security-docker
```
If you want to just run all the security checks at once you can do so with the main command:
```
make security-all
```
## Dependencies
We recommend using the [version manager asdf-vm](https://github.com/asdf-vm/asdf) for simpler installation of all required command-line dependencies used in this project for development, testing, security, etc.
Once you have set up corretly asdf-vm, you can install all relevant dependencies by running the following:
```
make install-dev-deps
```
In order to install the package you will need to use the [Poetry dependency manager](https://github.com/python-poetry/poetry).
## Project Organization
```
├── Dockerfile
├── LICENSE
├── Makefile
├── README.md
├── docs
│ ├── Makefile
│ ├── commands.rst
│ ├── conf.py
│ ├── examples
│ │ └── model-settings.json
│ ├── getting-started.rst
│ ├── index.rst
│ └── make.bat
├── file
├── project_example
│ ├── __init__.py
│ ├── common.py
│ ├── runtime.py
│ └── version.py
├── pyproject.toml
├── requirements-dev.txt
├── setup.py
└── tests
├── conftest.py
└── test_runtime.py
```
--------
Project based on the Secure Production MLOps Cookiecutter. #cookiecuttermlops