API Python Deployment

This page explains how to run the Python implementation of the CSC API Pipeline in three common ways: 1) Install from a wheel (.whl) and run the CLI 2) Run the Jupyter/Anaconda notebook for local/analyst testing 3) Run the raw Python sources (repo checkout) without installing a package

It also describes a two‑stage rollout: Analyst local tests → Server/overnight deployment by IT.

If you do not want to install Python on servers, you can use the packaged Windows .exe (see API Download and API PowerShell Deployment for scheduling patterns).

Log SSD/API support tickets

Phase1 & Phase 2 LAs/deployment teams should Log deployment bugs, required changes or running issues via - the basic/free Github account may be required for this
LA colleagues are also encouraged to send the project your general feedback, or your deployment requirements

Dependencies (net access)

Local wheel file only: Example: bash pip install C:\path\to\csc_api_pipeline-<version>-py3-none-any.whl
If dependencies are missing: pip may try to fetch them from PyPI. To stay offline, provide a local folder with all wheels and use: bash pip install --no-index --find-links C:\path\to\wheelhouse C:\path\to\csc_api_pipeline-<version>-py3-none-any.whl where wheelhouse/ contains wheels for required dependencies (e.g. pyodbc, python-dotenv, etc.).
From source (pip install .): may need internet to download/build dependencies unless you maintain an internal mirror/wheelhouse.

Recommended: Use a Virtual Environment (isolated)

venv (Python standard):

python -m venv .venv
# Windows
.\.venv\Scripts\activate
# Linux/macOS
source .venv/bin/activate

pip install --upgrade pip
pip install C:\path\to\csc_api_pipeline-<version>-py3-none-any.whl

Conda (Anaconda/Miniconda):

conda create -n csc_api py=3.11 -y
conda activate csc_api
pip install C:\path\to\csc_api_pipeline-<version>-py3-none-any.whl

Configure (shared pattern)

Place a .env file in the working directory used to run the commands (or set environment variables). See API Configuration for keys.

DB_CONNECTION_STRING=Driver={ODBC Driver 17 for SQL Server};Server=SERVER\INSTANCE;Database=HDM_Local;Trusted_Connection=yes;
TOKEN_ENDPOINT=...
API_ENDPOINT=...
CLIENT_ID=...
CLIENT_SECRET=...
SCOPE=...
SUPPLIER_KEY=...
DEBUG=true

Keep secrets out of source control. Restrict file permissions to the service/analyst account.

1) Install & Run from Wheel (CLI)

Install the wheel (offline or online as above), then:

python -m api_pipeline --help
python -m api_pipeline test-db-connection
python -m api_pipeline test-endpoint
python -m api_pipeline test-schema
python -m api_pipeline run

Expected behaviour: - test-db-connection checks DB connectivity - test-endpoint validates token acquisition/API reachability - test-schema confirms required tables/fields - run performs the submission process (or simulates if testing mode enabled in config)

Scheduling (Windows task example): - Program: python - Arguments: -m api_pipeline run - Start in: folder containing .env

2) Analyst Local|Initial Testing via Jupyter/Anaconda

The release bundle includes dfe_csc_api_testing.ipynb designed for analyst‑led, local smoke tests before server rollout.

Setup (Conda example):

conda create -n csc_api_notebook py=3.11 -y
conda activate csc_api_notebook
pip install C:\path\to\csc_api_pipeline-<version>-py3-none-any.whl  # or `pip install .` from source
pip install jupyterlab  # if not already installed
jupyter lab

Open dfe_csc_api_testing.ipynb.
Ensure .env is present in the working dir (or set env vars in the notebook).
Run the cells in order. The notebook imports and calls the same package modules used by the CLI.

Jupyter is ideal for visible diagnostics and exploring partial/delta behaviour with safe test data before requesting server deployment.

(Alternative) Zero‑install “Folder‑only” Notebook Run

If you’re less comfortable with pip/environments, you can do a simple, contained, folder‑only run that many analysts find easier:

Make a new folder on your machine (e.g. C:\CSC_API_LocalTest\)
Copy all .py files into that folder (from the release bundle or repo):
dfe_csc_api_testing.ipynb (the notebook)
The contents of api_pipeline/ folder with the .py modules (e.g., entry_point.py, db.py, etc.)
.env.txt (a redacted config template with your LA's DfE details). Keep secrets safe
Launch Jupyter (jupyter lab) and open the notebook in that folder
In the first cell of the notebook, insert this helper so the local code + .env.txt are picked up without any installs:
Run the notebook cells in order(or just Run all). The notebook will import the local api_pipeline modules from the folder you created

Note
- This avoids installing the wheel and keeps everything self‑contained for quick trials
- If python-dotenv isn’t present, the notebook will complain. Easiest fix: install once with pip install python-dotenv inside your notebook environment

3) Run from Raw Sources (no wheel)

If you have the repo/source tree:

pip install -r requirements.txt   # or install deps via your own wheelhouse
# Option A: module
python -m api_pipeline run
# Option B: direct entry point
python api_pipeline/entry_point.py

This approach is common for development or pilot evaluations.

Two‑Stage Rollout Model (recommended)

Stage A – Analyst Local Smoke Tests

Create an isolated environment (venv or conda).
Install the wheel or use the notebook.
Prepare .env with testing mode values and a non‑live staging table (e.g. _anon).
Run:
python -m api_pipeline test_db_connection
python -m api_pipeline test-endpoint
python -m api_pipeline test-schema
python -m api_pipeline run
Capture logs/output and confirm row_state and submission_status transitions.

Hand‑off artefacts to IT: - The exact .whl used (or .exe if that route is chosen) - A redacted .env.example showing required keys - Notebook (optional) + any validation screenshots - A short “Server Runbook” note: command line, working dir, log path

Stage B – Server/Overnight Deployment

Create a service account with required DB/API access.
Install Python (or use the .exe route).
Install the wheel from shared storage (offline install if required).
Place a production .env in the scheduler’s Start in folder.
Configure a Task Scheduler/cron job to run python -m api_pipeline run.
Enable retries and log redirection/rotation.
Switch from test to live staging table/flags per go‑live plan.

Troubleshooting

Symptom	Likely Cause	Fix
`pyodbc.InterfaceError`	ODBC driver missing	Install “ODBC Driver 17/18 for SQL Server”.
`pip` tries internet but you’re offline	Missing deps	Build a wheelhouse and use `--no-index --find-links`
“Module not found” after install	Wrong environment	Activate the correct `venv`/conda env before running
No eligible rows processed	Staging not primed	Seed test rows and verify `row_state` + `submission_status`
Auth/token errors	Client secret/scope or clock skew	Verify `.env`; check server time sync/NTP

Packaging to `.exe` (optional, internal)

pyinstaller api_pipeline/entry_point.py --onefile --name csc_api_pipeline

Distribute the .exe with a local .env. Use hash verification as described in Release Overview.

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Python wheel (.whl)

A wheel is Python’s standard built package format. Installing a wheel with pip: - Copies prebuilt code into your Python environment (no compiling) - Does not require internet if you install from a local file and already have dependencies available - Is repeatable for IT/Infrastruture/Deployment team: place the .whl on a server share and install in a controlled environment

In short: the .whl is like an MSI/installer for Python packages