AstraZeneca
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@@ -0,0 +1,102 @@
+# Tutorial development
+
+You are helping with tutorial of the Runnable framework.
+
+# Working examples
+
+There are plenty of examples in examples folder with the following structure.
+They are layered as per increasing complexity. Focus only on .py files as yaml is being deprecated.
+
+You can run any example as : ```uv run <python_file_name>```.
+The resulting run is named by ```run_id```.
+
+Any execution results in:
+
+- a run log captured in .run_log_store against that run id
+- a catalog folder against the run_id which has the files moved between tasks. It also captures the output from the
+  function or script execution. In case of the notebook, the output notebook is stored.
+
+├── 01-tasks - tells you how to run python functions, notebooks or scripts as pipelines
+│   ├── notebook.py
+│   ├── notebook.yaml
+│   ├── python_task_as_pipeline.py
+│   ├── python_tasks.py
+│   ├── python_tasks.yaml
+│   ├── scripts.py
+│   ├── scripts.yaml
+│   ├── stub.py
+│   └── stub.yaml
+├── 02-sequential - tell you how to stitch tasks into pipelines.
+│   ├── conditional.py
+│   ├── default_fail.py
+│   ├── default_fail.yaml
+│   ├── on_failure_fail.py
+│   ├── on_failure_fail.yaml
+│   ├── on_failure_succeed.py
+│   ├── on_failure_succeed.yaml
+│   ├── traversal.py
+│   └── traversal.yaml
+├── 03-parameters - shows the parameter flow between tasks and setting initial parameters.
+                    Focus on how parameters are accessed and returned back. They are by names or argspace or kwargs.
+│   ├── passing_parameters_notebook.py
+│   ├── passing_parameters_notebook.yaml
+│   ├── passing_parameters_python.py
+│   ├── passing_parameters_python.yaml
+│   ├── passing_parameters_shell.py
+│   ├── passing_parameters_shell.yaml
+│   ├── static_parameters_fail.py
+│   ├── static_parameters_fail.yaml
+│   ├── static_parameters_non_python.py
+│   ├── static_parameters_non_python.yaml
+│   ├── static_parameters_python.py
+│   └── static_parameters_python.yaml
+├── 04-catalog - Shows how to flow files between tasks. Focus on how get/put works and also how the user can chose not
+                to store a copy in case if the file is too big.
+│   ├── catalog_no_copy.py
+│   ├── catalog_on_fail.py
+│   ├── catalog_on_fail.yaml
+│   ├── catalog_python.py
+│   ├── catalog_python.yaml
+│   └── catalog.py
+├── 06-parallel - shows how to run parallel branches
+│   ├── nesting.py
+│   ├── nesting.yaml
+│   ├── parallel_branch_fail.py
+│   ├── parallel_branch_fail.yaml
+│   ├── parallel.py
+│   └── parallel.yaml
+├── 07-map - shows how to run a branch looped over an iterable.
+│   ├── custom_reducer.py
+│   ├── custom_reducer.yaml
+│   ├── map_fail.py
+│   ├── map_fail.yaml
+│   ├── map.py
+│   └── map.yaml
+├── 08-mocking - Useful for mocking/testing parts of the workflow.
+│   ├── default.yaml
+│   ├── mocked_map_parameters.yaml
+│   ├── mocked-config-debug.yaml
+│   ├── mocked-config-simple.yaml
+│   ├── mocked-config-unittest.yaml
+│   ├── mocked-config.yaml
+│   └── patching.yaml
+├── 11-jobs - shows how to run jobs.
+│   ├── catalog_no_copy.py
+│   ├── catalog.py
+│   ├── emulate.yaml
+│   ├── k8s-job.yaml
+│   ├── local-container.yaml
+│   ├── mini-k8s-job.yaml
+│   ├── notebooks.py
+│   ├── passing_parameters_python.py
+│   ├── python_tasks.py
+│   └── scripts.py
+
+# Your role
+
+Your role is to understand the current show case of capabilities and come up with missing examples.
+
+You also need to help me with writing tutorials based on common ML workflows. There are some examples given in
+examples/tutorials but it can be improved.
+
+The same applies to examples provided in torch folder. They should be improved to make it easier to understand.
@@ -163,3 +163,6 @@ minikube/
 *_timeline.html
 *_dashboard.html
 *_diagram.svg
+
+# Test Dockerfile
+Dockerfile.test
@@ -179,7 +179,7 @@ The docs explain the contextual example first and then show a detailed working e
 
 When writing docs always use code from examples directory and always use code snippets to avoid duplication
 
-Remember that when writing lists in md, there should be an empty line between the list - and the preceding line
+Remember that when writing lists in md, there should be an empty line between the list and the preceding line. This applies to all lists, including those following headings, text, or other elements
 
 
 I prefer to give prompts in a visual editor and I have my prompts in a file called prompt.md.
 
@@ -0,0 +1,31 @@
+# Test Dockerfile with all runtime dependencies
+# Apple M1 compatible multi-platform image
+
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+USER root
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    curl \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv for fast dependency management
+RUN pip install uv
+
+# Copy project files
+COPY pyproject.toml uv.lock README.md ./
+RUN uv sync --all-extras --frozen --all-groups
+
+COPY runnable/ ./runnable/
+COPY extensions/ ./extensions/
+COPY examples/ ./examples/
+
+# Set environment variables
+ENV PYTHONPATH=/app
+ENV PATH="/app/.venv/bin:$PATH"
@@ -5,7 +5,7 @@
 **Transform any Python function into a portable, trackable pipeline in seconds.**
 
 <p align="center">
-<a href="https://pypi.org/project/runnable/"><img alt="python:" src="https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue.svg"></a>
+<a href="https://pypi.org/project/runnable/"><img alt="python:" src="https://img.shields.io/badge/python-3.10+-blue.svg"></a>
 <a href="https://pypi.org/project/runnable/"><img alt="Pypi" src="https://badge.fury.io/py/runnable.svg"></a>
 <a href="https://github.com/AstraZeneca/runnable/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg"></a>
 <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
@@ -26,11 +26,16 @@ def analyze_sales():
     return total_revenue, best_product
 ```
 
-**Make it runnable everywhere (2 lines):**
+**Make it runnable everywhere:**
 
 ```python
 from runnable import PythonJob
-PythonJob(function=analyze_sales).execute()
+
+def main():
+    PythonJob(function=analyze_sales).execute()
+
+if __name__ == "__main__":
+    main()
 ```
 
 **🎉 Success!** Your function now runs the same on laptop, containers, and Kubernetes with automatic tracking and reproducibility.
@@ -46,10 +51,15 @@ def analyze_segments(customer_data):  # Name matches = automatic connection
 
 # What Runnable needs (same logic, no glue):
 from runnable import Pipeline, PythonTask
-Pipeline(steps=[
-    PythonTask(function=load_customer_data, returns=["customer_data"]),
-    PythonTask(function=analyze_segments, returns=["analysis"])
-]).execute()
+
+def main():
+    Pipeline(steps=[
+        PythonTask(function=load_customer_data, returns=["customer_data"]),
+        PythonTask(function=analyze_segments, returns=["analysis"])
+    ]).execute()
+
+if __name__ == "__main__":
+    main()
 ```
 
 **Same pipeline runs unchanged on laptop, containers, and Kubernetes.**
@@ -60,6 +70,16 @@ Pipeline(steps=[
 pip install runnable
 ```
 
+**For development:**
+```bash
+uv sync --all-extras --dev
+```
+
+**Run examples:**
+```bash
+uv run examples/01-tasks/python_tasks.py
+```
+
 ## 📊 Why Choose Runnable?
 
 - **🎯 Easy to adopt**: Your code remains as-is, no decorators or imposed structure
 
@@ -17,28 +17,33 @@ flowchart TD
 ```python
 from runnable import Conditional, Pipeline, PythonTask, Stub
 
-# Step 1: Make a decision
-toss_task = PythonTask(
-    function=toss_function,    # Returns "heads" or "tails"
-    returns=["toss"],          # Named return for conditional to use
-    name="toss_task"
-)
-
-# Step 2: Branch based on decision
-conditional = Conditional(
-    parameter="toss",          # Use the "toss" value from above
-    branches={
-        "heads": heads_pipeline,    # Run this if toss="heads"
-        "tails": tails_pipeline     # Run this if toss="tails"
-    },
-    name="conditional"
-)
-
-# Step 3: Continue after branching
-continue_step = Stub(name="continue_processing")
-
-pipeline = Pipeline(steps=[toss_task, conditional, continue_step])
-pipeline.execute()
+def main():
+    # Step 1: Make a decision
+    toss_task = PythonTask(
+        function=toss_function,    # Returns "heads" or "tails"
+        returns=["toss"],          # Named return for conditional to use
+        name="toss_task"
+    )
+
+    # Step 2: Branch based on decision
+    conditional = Conditional(
+        parameter="toss",          # Use the "toss" value from above
+        branches={
+            "heads": create_heads_pipeline(),    # Run this if toss="heads"
+            "tails": create_tails_pipeline()     # Run this if toss="tails"
+        },
+        name="conditional"
+    )
+
+    # Step 3: Continue after branching
+    continue_step = Stub(name="continue_processing")
+
+    pipeline = Pipeline(steps=[toss_task, conditional, continue_step])
+    pipeline.execute()
+    return pipeline
+
+if __name__ == "__main__":
+    main()
 ```
 
 ??? example "See complete runnable code"
@@ -60,6 +65,7 @@ pipeline.execute()
 
 ## The decision function
 
+**Helper function (makes the decision):**
 ```python
 import random
 
@@ -74,6 +80,7 @@ Returns `"heads"` or `"tails"` - the conditional uses this to pick a branch.
 
 ## Branch pipelines
 
+**Helper functions (create the branch pipelines):**
 ```python
 def create_heads_pipeline():
     return PythonTask(
@@ -106,35 +113,41 @@ flowchart TD
 
 **Data validation:**
 ```python
-# Check data quality, route accordingly
-parameter="data_quality"  # returns "good", "needs_cleaning", "invalid"
-branches={
-    "good": analysis_pipeline,
-    "needs_cleaning": cleanup_then_analysis_pipeline,
-    "invalid": error_handling_pipeline
-}
+# Example conditional configuration (partial code)
+conditional = Conditional(
+    parameter="data_quality",  # returns "good", "needs_cleaning", "invalid"
+    branches={
+        "good": analysis_pipeline,
+        "needs_cleaning": cleanup_then_analysis_pipeline,
+        "invalid": error_handling_pipeline
+    }
+)
 ```
 
 **Model selection:**
 ```python
-# Choose model based on data size
-parameter="dataset_size"  # returns "small", "medium", "large"
-branches={
-    "small": simple_model_pipeline,
-    "medium": ensemble_pipeline,
-    "large": distributed_training_pipeline
-}
+# Example conditional configuration (partial code)
+conditional = Conditional(
+    parameter="dataset_size",  # returns "small", "medium", "large"
+    branches={
+        "small": simple_model_pipeline,
+        "medium": ensemble_pipeline,
+        "large": distributed_training_pipeline
+    }
+)
 ```
 
 **Environment routing:**
 ```python
-# Different behavior per environment
-parameter="environment"  # returns "dev", "staging", "prod"
-branches={
-    "dev": fast_testing_pipeline,
-    "staging": full_validation_pipeline,
-    "prod": production_pipeline
-}
+# Example conditional configuration (partial code)
+conditional = Conditional(
+    parameter="environment",  # returns "dev", "staging", "prod"
+    branches={
+        "dev": fast_testing_pipeline,
+        "staging": full_validation_pipeline,
+        "prod": production_pipeline
+    }
+)
 ```
 
 !!! tip "Conditional tips"