Getting Started

Getting Started with Odipie

This guide runs the current package skeleton before introducing optional AI/ML dependencies.

Requirements

• Python 3.10 or later (the range declared in pyproject.toml).
• pip and virtual-environment support.
• Git for cloning the repository.
• Docker and Docker Compose only for the container workflow.

Check the supported Python range in pyproject.toml before installing.

Local installation

git clone https://github.com/Odiambo/odipie.git
cd odipie
python -m venv .venv

Activate the environment:

# macOS or Linux
source .venv/bin/activate

# Windows PowerShell
.venv\Scripts\Activate.ps1

Install and run the smoke checks:

python -m pip install --upgrade pip
python -m pip install -e .
python -m odipie --loaded
python -m odipie --versions
python app.py

python -m odipie --loaded reports modules loaded through Odipie's lazy proxies. --versions reports discoverable optional-package versions without forcing the heavy imports. The installed odipie console command exposes the same CLI, so odipie --versions is equivalent after installation.

Optional dependency groups

Install only the stacks needed by your workflow:

python -m pip install -e ".[data]"
python -m pip install -e ".[tensorflow]"
python -m pip install -e ".[torch]"
python -m pip install -e ".[ml]"

Review pyproject.toml for the current packages in each group.

Lazy-loading mental model

import odipie

print(odipie.get_loaded_modules())

# Accessing a proxied library can trigger its real import.
frame = odipie.pandas.DataFrame({"value": [1, 2, 3]})

print(odipie.get_loaded_modules())

A lazy proxy postpones work; it does not remove the dependency. The first real use may have noticeable latency, and any missing binary, incompatible version, or platform error can surface then.

Docker workflow

docker compose build
docker compose up

The default container runs python app.py. It verifies the package installation and reports optional dependency availability without intentionally importing every heavy framework.

Verify the behavior you care about

Measure cold-start behavior in a fresh process. Warm imports use Python's module cache and answer a different question.

python -X importtime -c "import odipie"

Keep the interpreter, environment, dependency versions, hardware, and command constant. Report the command and multiple runs rather than a universal percentage.

Common problems

An optional module is missing

Install the corresponding extra, then repeat the failing access. Do not install every framework unless the project uses all of them.

The first feature call is slow

That can be the deferred import cost. For latency-sensitive services, explicitly warm required modules before accepting traffic.

The CLI uses the wrong Python environment

Run python -m pip --version and python -c "import sys; print(sys.executable)". Both should point to the intended virtual environment.

Docker cannot find project files

Run Compose from the repository root and check that the Docker build context has not changed.

Next steps

• Use the Prompt and Context Guide to specify an AI-assisted task.
• Read AI Terminology and FAQ before connecting tools or agents.
• Open an issue with the exact command, environment, and traceback when reporting a defect.