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+# OpenML Local Development Environment Setup
+
+This guide outlines the standard procedures for setting up a local development environment for the OpenML ecosystem. It covers the configuration of the backend servers (API v1 and API v2) and the Python Client SDK.
+
+OpenML currently has two backend architecture:
+
+* **API v1**: The PHP-based server currently serving production traffic.
+* **API v2**: The Python-based server (FastAPI) currently under active development.
+
+> Note on Migration: API v1 is projected to remain operational through at least 2026. API v2 is the target architecture for future development.
+
+## 1. API v1 Setup (PHP Backend)
+
+This section details the deployment of the legacy PHP backend.
+
+### Prerequisites
+
+* **Docker**: Docker Desktop (Ensure the daemon is running).
+* **Version Control**: Git.
+
+### Installation Steps
+
+#### 1. Clone the Repository
+
+Retrieve the OpenML services source code:
+
+```bash
+git clone https://github.com/openml/services
+cd services
+```
+
+#### 2. Configure File Permissions
+
+To ensure the containerized PHP service can write to the local filesystem, initialize the data directory permissions.
+
+From the repository root:
+
+```bash
+chown -R www-data:www-data data/php
+```
+
+If the `www-data` user does not exist on the host system, grant full permissions as a fallback:
+
+```bash
+chmod -R 777 data/php
+```
+
+#### 3. Launch Services
+
+Initialize the container stack:
+
+```bash
+docker compose --profile all up -d
+```
+
+#### Warning: Container Conflicts
+
+If API v2 (Python backend) containers are present on the system, name conflicts may occur. To resolve this, stop and remove existing containers before launching API v1:
+
+```bash
+docker compose --profile all down
+docker compose --profile all up -d
+```
+
+#### 4. Verification
+
+Validate the deployment by accessing the flow endpoint. A successful response will return structured JSON data.
+
+* **Endpoint**: http://localhost:8080/api/v1/json/flow/181
+
+### Client Configuration
+
+To direct the `openml-python` client to the local API v1 instance, modify the configuration as shown below. The API key corresponds to the default key located in `services/config/php/.env`.
+
+```python
+import openml
+from openml_sklearn.extension import SklearnExtension
+from sklearn.neighbors import KNeighborsClassifier
+
+# Configure client to use local Docker instance
+openml.config.server = "http://localhost:8080/api/v1/xml"
+openml.config.apikey = "AD000000000000000000000000000000"
+
+# Test flow publication
+clf = KNeighborsClassifier(n_neighbors=3)
+extension = SklearnExtension()
+knn_flow = extension.model_to_flow(clf)
+
+knn_flow.publish()
+```
+
+## 2. API v2 Setup (Python Backend)
+
+This section details the deployment of the FastAPI backend.
+
+### Prerequisites
+
+* **Docker**: Docker Desktop (Ensure the daemon is running).
+* **Version Control**: Git.
+
+### Installation Steps
+
+#### 1. Clone the Repository
+
+Retrieve the API v2 source code:
+
+```bash
+git clone https://github.com/openml/server-api
+cd server-api
+```
+
+#### 2. Launch Services
+
+Build and start the container stack:
+
+```bash
+docker compose --profile all up
+```
+
+#### 3. Verification
+
+Validate the deployment using the following endpoints:
+
+* **Task Endpoint**: http://localhost:8001/tasks/31
+* **Swagger UI (Documentation)**: http://localhost:8001/docs
+
+## 3. Python SDK (`openml-python`) Setup
+
+This section outlines the environment setup for contributing to the OpenML Python client.
+
+### Installation Steps
+
+#### 1. Clone the Repository
+
+```bash
+git clone https://github.com/openml/openml-python
+cd openml-python
+```
+
+#### 2. Environment Initialization
+
+Create an isolated virtual environment (example using Conda):
+
+```bash
+conda create -n openml-python-dev python=3.12
+conda activate openml-python-dev
+```
+
+#### 3. Install Dependencies
+
+Install the package in editable mode, including development and documentation dependencies:
+
+```bash
+python -m pip install -e ".[dev,docs]"
+```
+
+#### 4. Configure Quality Gates
+
+Install pre-commit hooks to enforce coding standards:
+
+```bash
+pre-commit install
+pre-commit run --all-files
+```
+
+## 4. Testing Guidelines
+
+The OpenML Python SDK utilizes `pytest` markers to categorize tests based on dependencies and execution context.
+
+| Marker            | Description                                                                 |
+|-------------------|-----------------------------------------------------------------------------|
+| `sklearn`          | Tests requiring `scikit-learn`. Skipped if the library is missing.          |
+| `production`      | Tests that interact with the live OpenML server (real API calls).         |
+| `uses_test_server`  | Tests requiring the OpenML test server environment.                       |
+
+### Execution Examples
+
+Run the full test suite:
+
+```bash
+pytest
+```
+
+Run a specific subset (e.g., `scikit-learn` tests):
+
+```bash
+pytest -m sklearn
+```
+
+Exclude production tests (local only):
+
+```bash
+pytest -m "not production"
+```
+
+### Admin Privilege Tests
+
+Certain tests require administrative privileges on the test server. These are skipped automatically unless an admin API key is provided via environment variables.
+
+#### Windows (PowerShell):
+
+```shell
+$env:OPENML_TEST_SERVER_ADMIN_KEY = "admin-key"
+```
+
+#### Linux/macOS:
+
+```bash
+export OPENML_TEST_SERVER_ADMIN_KEY="admin-key"
+```