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Getting Started

Use this page for the fastest reliable path from installation to a first complete SDK run.

Before this page: Quickstart if you have not run iints demo yet.

After this page: Scientific Workflow, MDMP Quickstart, or Raspberry Pi Digital Patient, depending on your goal.

The core workflow is:

  1. run a simulation
  2. certify the output data
  3. review the run report

If you mainly need help choosing folders, install extras, or repository vs package usage, read Installation first.

1) Create and Activate a Virtual Environment

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip

All commands below assume this .venv is active.

2) Install The SDK

python -m pip install -U "iints-sdk-python35[full,mdmp]"

Optional extras:

pip install "iints-sdk-python35[research]"
pip install "iints-sdk-python35[nightscout]"
pip install "iints-sdk-python35[edge,mdmp]"

3) Verify The Environment

iints doctor --smoke-run

If this fails, fix the environment before starting longer runs.

4) Create A Project

iints quickstart --project-name iints_quickstart
cd iints_quickstart

The generated structure includes: - algorithms/ - scenarios/ - contracts/ - data/demo/ - audit/ - results/

Important: - before iints quickstart, commands can be run from any folder - after iints quickstart, move into the generated project folder - repository helper scripts such as ./scripts/run_live_stage_demo.sh belong to the SDK repository, not the quickstart project - custom algorithms can be installed later with iints plugin install algorithms/my_algo.py

5) Run A Baseline Simulation

iints presets run --name baseline_t1d --algo algorithms/example_algorithm.py

6) Certify The Run Data

Use the generated results CSV plus the project contract:

iints data certify \
  contracts/clinical_mdmp_contract.yaml \
  results/<run_id>/results.csv \
  --output-json results/<run_id>/certification.json

Optional dashboard:

iints data certify-visualizer \
  results/<run_id>/certification.json \
  --output-html results/<run_id>/certification_dashboard.html

7) Review The Run With The Local AI Layer

iints ai report results/<run_id>

That gives the main SDK workflow in three commands:

  1. iints presets run
  2. iints data certify
  3. iints ai report

8) Inspect The Outputs

A typical run writes: - results.csv: time-series simulation output - clinical_report.pdf: summary report for review - audit/: safety and decision trail - run_manifest.json: file hashes for reproducibility - run_metadata.json: run configuration, environment details, SDK version, and data-format versions - certification.json: trust grade and dataset checks after iints data certify

9) Build A Study-Ready Bundle

iints study-ready --algo algorithms/example_algorithm.py --output-dir results/study_ready

This adds: - validation_report.json - sources_manifest.json - SUMMARY.md

10) Common Next Steps

Import Personal Pump / CGM Data

iints import-carelink \
  --input-csv "/path/to/CareLink export.csv" \
  --output-dir results/imported_carelink

Or build the full personal-data workspace at once:

iints carelink-workbench \
  --input-csv "/path/to/CareLink export.csv" \
  --output-dir results/personal_carelink

Enable The Optional Local AI Assistant

iints ai models
ollama pull ministral-3:8b
iints ai local-check --model ministral-3:8b
iints ai prepare results/<run_id>
iints ai report results/<run_id>

If iints ai local-check reports that Ollama closed the connection, the most likely causes are a restarting daemon or insufficient memory. In that case, try ministral-3:3b.

Build A Poster From Existing Run Bundles

iints poster \
  --run-dir results/normal_run \
  --run-dir results/meal_stress \
  --run-dir results/supervisor_override \
  --label "Normal Run" \
  --label "Meal Stress Test" \
  --label "Supervisor Override" \
  --output-path results/posters/iints_results_poster.png

Run A Prepared Presentation Demo

./scripts/run_live_stage_demo.sh

This is a convenient repository wrapper when you want a ready-made live walkthrough with code, outputs, and presentation assets.

If the machine only has the installed SDK and not the repository checkout, export the same demo code first:

iints demo-export --output-dir iints_demo
cd iints_demo
python 07_live_stage_demo.py

Run A Persistent Digital Patient On Raspberry Pi

iints quickstart --project-name iints_pi_demo
cd iints_pi_demo
iints patient scenarios
iints patient start \
  --algo algorithms/example_algorithm.py \
  --workspace patient_runtime \
  --scenario-profile normal_day \
  --mode demo-time \
  --speed 60x
iints patient status --workspace patient_runtime

That flow gives you: - a persistent SQLite-backed runtime - a live dashboard on http://127.0.0.1:8765/dashboard - a run-like bundle under patient_runtime/live_bundle/

Security default:

  • the dashboard stays on loopback by default
  • for remote presentation, prefer Raspberry Pi Connect instead of opening the API to the LAN
  • if you truly need a non-loopback bind, use --allow-remote-api together with --api-token-env or --api-token-file

Full guide: Raspberry Pi Digital Patient

Build An Edge-Ready SBC Project

iints edge setup --output-dir iints_edge_demo --board raspberry_pi
cd iints_edge_demo
./run_edge_patient.sh

Export the live runtime back to a workstation with:

iints patient kiosk --workspace patient_runtime
iints edge status --workspace patient_runtime
iints edge bundle --workspace patient_runtime --output results/edge_runtime_bundle.zip

That edge flow gives you: - a generated edge project scaffold - a service file and update script for the board - a kiosk dashboard URL for Raspberry Pi Connect screen sharing - a ZIP bundle for workstation-side analysis and reporting

Where To Go Next

If you finished this page and want to... Continue with
turn one run into a formal benchmark Scientific Workflow
aggregate several runs Study Analysis
certify datasets and outputs MDMP Quickstart
use local AI reporting AI Assistant
deploy to Raspberry Pi Raspberry Pi Digital Patient

Safety Scope

  • Research use only.
  • Not a medical device.
  • No clinical dosing advice.