From aa968586d91f261412c383c9f580de3683a2a624 Mon Sep 17 00:00:00 2001
From: Vijay Vammi <vijay.vammi@astrazeneca.com>
Date: Thu, 23 May 2024 05:15:02 +0100
Subject: [PATCH 1/4] fix: identify on failure behaviours and improve sdk API

---
 docs/concepts/map.md                          |   6 +-
 docs/configurations/overview.md               |  50 +---
 docs/reference.md                             |  72 ++---
 examples/01-tasks/stub.py                     |   5 +-
 examples/02-sequential/on_failure_fail.py     |   2 +-
 .../03-parameters/static_parameters_python.py |   1 -
 examples/07-map/custom_reducer.py             |   1 -
 examples/07-map/map.py                        |   1 -
 mkdocs.yml                                    |   2 +-
 runnable/sdk.py                               | 251 +++++++++++-------
 tests/test_examples.py                        |   4 +-
 11 files changed, 201 insertions(+), 194 deletions(-)

diff --git a/docs/concepts/map.md b/docs/concepts/map.md
index aad92874..894f88c5 100644
--- a/docs/concepts/map.md
+++ b/docs/concepts/map.md
@@ -12,21 +12,21 @@ to run on every hyper parameter.
     success([Success]):::green
 
     subgraph one[Parameter 1]
-        process_chunk1([Process Chunk]):::yellow
+        process_chunk1([Train model]):::yellow
         success_chunk1([Success]):::yellow
 
         process_chunk1 --> success_chunk1
     end
 
     subgraph two[Parameter ...]
-        process_chunk2([Process Chunk]):::yellow
+        process_chunk2([Train model]):::yellow
         success_chunk2([Success]):::yellow
 
         process_chunk2 --> success_chunk2
     end
 
     subgraph three[Parameter n]
-        process_chunk3([Process Chunk]):::yellow
+        process_chunk3([Train model]):::yellow
         success_chunk3([Success]):::yellow
 
         process_chunk3 --> success_chunk3
diff --git a/docs/configurations/overview.md b/docs/configurations/overview.md
index 6ff83205..47ea5a5f 100644
--- a/docs/configurations/overview.md
+++ b/docs/configurations/overview.md
@@ -1,49 +1,3 @@
-**runnable** is designed to make effective collaborations between data scientists/researchers
-and infrastructure engineers.
+**runnable** is designed to enable the pipeline execution in varied computational environments.
 
-All the features described in the [concepts](../concepts/the-big-picture.md) are
-aimed at the *research* side of data science projects while configurations add *scaling* features to them.
-
-
-Configurations are presented during the execution:
-
-For ```yaml``` based pipeline, use the ```--config-file, -c``` option in the [runnable CLI](../usage.md/#usage).
-
-For [python SDK](../sdk.md/#runnable.Pipeline.execute), use the ```configuration_file``` option or via
-environment variable ```runnable_CONFIGURATION_FILE```
-
-## Default configuration
-
-```yaml
---8<-- "examples/configs/default.yaml"
-```
-
-1. Execute the pipeline in the local compute environment.
-2. The run log is not persisted but present in-memory and flushed at the end of execution.
-3. No catalog functionality, all catalog operations are effectively no-op.
-4. No secrets functionality, all secrets are effectively no-op.
-5. No experiment tracking tools, all interactions with experiment tracking tools are effectively no-op.
-Run log still captures the metrics, but are not passed to the experiment tracking tools.
-
-The default configuration for all the pipeline executions runs on the
-[local compute](executors/local.md), using a
-[buffered run log](run-log.md/#buffered) store with
-[no catalog](catalog.md/#do-nothing) or
-[secrets](secrets.md/#do-nothing) or
-[experiment tracking functionality](experiment-tracking.md/).
-
-
-
-## Format
-
-The configuration file is in yaml format and the typical structure is:
-
-```yaml
-service:
-  type: service provider
-  config:
-    ...
-```
-
-where service is one of ```executor```, ```catalog```, ```experiment_tracker```,
- ```secrets``` or ```run_log_store```.
+This is controlled by a ```configuration``` file without changing either the domain code or the pipeline definition.
diff --git a/docs/reference.md b/docs/reference.md
index 0104737c..6d162633 100644
--- a/docs/reference.md
+++ b/docs/reference.md
@@ -1,3 +1,8 @@
+Please accompany the reference with  ```examples``` from
+[the repo](https://github.com/AstraZeneca/runnable-core).
+
+
+
 ## PythonTask
 
 === "sdk"
@@ -75,18 +80,40 @@
 <hr style="border:2px dotted orange">
 
 
-## Catalog
+## ShellTask
 
 === "sdk"
 
-    ::: runnable.Catalog
+    ::: runnable.ShellTask
         options:
             show_root_heading: true
             show_bases: false
+            show_docstring_description: true
             heading_level: 3
 
 === "yaml"
 
+    Attributes:
+
+    - ```name```: the name of the task
+    - ```command```: the path to the notebook relative to the project root.
+    - ```next```: the next node to call if the function succeeds. Use ```success``` to terminate
+    the pipeline successfully or ```fail``` to terminate with fail.
+    - ```on_failure```: The next node in case of failure.
+    - ```catalog```: mapping of cataloging items
+    - ```overrides```: mapping of step overrides from global configuration.
+
+    ```yaml
+    dag:
+      steps:
+        name: <>
+          type: task
+          command: <>
+          next: <>
+          on_failure: <>
+          catalog: # Any cataloging to be done.
+          overrides: # mapping of overrides of global configuration
+    ```
 
 
 <hr style="border:2px dotted orange">
@@ -108,16 +135,14 @@
 <hr style="border:2px dotted orange">
 
 
-
-## ShellTask
+## Catalog
 
 === "sdk"
 
-    ::: runnable.ShellTask
+    ::: runnable.Catalog
         options:
             show_root_heading: true
             show_bases: false
-            show_docstring_description: true
             heading_level: 3
 
 === "yaml"
@@ -128,30 +153,29 @@
 
 
 
-
-## Parallel
-
+## Pipeline
 
 === "sdk"
 
-    ::: runnable.Parallel
+    ::: runnable.Pipeline
         options:
             show_root_heading: true
             show_bases: false
             show_docstring_description: true
             heading_level: 3
+            members:
+              - execute
 
 === "yaml"
 
 
 
-<hr style="border:2px dotted orange">
+## Parallel
 
-## Map
 
 === "sdk"
 
-    ::: runnable.Map
+    ::: runnable.Parallel
         options:
             show_root_heading: true
             show_bases: false
@@ -160,31 +184,15 @@
 
 === "yaml"
 
-<hr style="border:2px dotted orange">
-
-
-
-::: runnable.Success
-    options:
-        show_root_heading: true
-        show_bases: false
-        show_docstring_description: true
-
-<hr style="border:2px dotted orange">
 
-::: runnable.Fail
-    options:
-        show_root_heading: true
-        show_bases: false
-        show_docstring_description: true
 
 <hr style="border:2px dotted orange">
 
-## Pipeline
+## Map
 
 === "sdk"
 
-    ::: runnable.Pipeline
+    ::: runnable.Map
         options:
             show_root_heading: true
             show_bases: false
@@ -192,3 +200,5 @@
             heading_level: 3
 
 === "yaml"
+
+<hr style="border:2px dotted orange">
diff --git a/examples/01-tasks/stub.py b/examples/01-tasks/stub.py
index 8899fb30..a61ed462 100644
--- a/examples/01-tasks/stub.py
+++ b/examples/01-tasks/stub.py
@@ -29,10 +29,7 @@ def main():
 
     step3 = Stub(name="step3", terminate_with_success=True)
 
-    pipeline = Pipeline(
-        steps=[step1, step2, step3],
-        add_terminal_nodes=True,
-    )
+    pipeline = Pipeline(steps=[step1, step2, step3])
 
     pipeline.execute()
 
diff --git a/examples/02-sequential/on_failure_fail.py b/examples/02-sequential/on_failure_fail.py
index ef6fefe2..770c272d 100644
--- a/examples/02-sequential/on_failure_fail.py
+++ b/examples/02-sequential/on_failure_fail.py
@@ -31,7 +31,7 @@ def main():
     step_1.on_failure = step_4.name
 
     pipeline = Pipeline(
-        steps=[step_1, step_2, step_3, [step_4]],
+        steps=[step_1, step_2, step_3],
     )
     pipeline.execute()
 
diff --git a/examples/03-parameters/static_parameters_python.py b/examples/03-parameters/static_parameters_python.py
index 6d783150..0a38c7b9 100644
--- a/examples/03-parameters/static_parameters_python.py
+++ b/examples/03-parameters/static_parameters_python.py
@@ -64,7 +64,6 @@ def read_initial_params_as_json(
 
     pipeline = Pipeline(
         steps=[read_params_as_pydantic, read_params_as_json],
-        add_terminal_nodes=True,
     )
 
     _ = pipeline.execute(parameters_file="examples/common/initial_parameters.yaml")
diff --git a/examples/07-map/custom_reducer.py b/examples/07-map/custom_reducer.py
index 9d178cfa..cb3b4314 100644
--- a/examples/07-map/custom_reducer.py
+++ b/examples/07-map/custom_reducer.py
@@ -85,7 +85,6 @@ def iterable_branch(execute: bool = True):
 
     pipeline = Pipeline(
         steps=[process_chunk_task_python, process_chunk_task_notebook, process_chunk_task_shell, read_chunk],
-        add_terminal_nodes=True,
     )
 
     if execute:
diff --git a/examples/07-map/map.py b/examples/07-map/map.py
index e8afb0e8..ca6fffee 100644
--- a/examples/07-map/map.py
+++ b/examples/07-map/map.py
@@ -88,7 +88,6 @@ def iterable_branch(execute: bool = True):
 
     pipeline = Pipeline(
         steps=[process_chunk_task_python, process_chunk_task_notebook, process_chunk_task_shell, read_chunk],
-        add_terminal_nodes=True,
     )
 
     if execute:
diff --git a/mkdocs.yml b/mkdocs.yml
index 76c37971..ab5ba67b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -126,7 +126,7 @@ nav:
       - "Map": "concepts/map.md"
   - "Reproducibility": "concepts/run-log.md"
   - "Run pipeline":
-      - "Overview": "configurations/overview.md"
+      - "configurations/overview.md"
       - "Executor":
           - "local": "configurations/executors/local.md"
           - "mocked": "configurations/executors/mocked.md"
diff --git a/runnable/sdk.py b/runnable/sdk.py
index dff11912..41c92664 100644
--- a/runnable/sdk.py
+++ b/runnable/sdk.py
@@ -61,11 +61,9 @@ class Catalog(BaseModel):
         put (List[str]): List of glob patterns to put into central catalog from the compute data folder.
 
     Examples:
-        >>> from runnable import Catalog, Task
+        >>> from runnable import Catalog
         >>> catalog = Catalog(compute_data_folder="/path/to/data", get=["*.csv"], put=["*.csv"])
 
-        >>> task = Task(name="task", catalog=catalog, command="echo 'hello'")
-
     """
 
     model_config = ConfigDict(extra="forbid")  # Need to be for command, would be validated later
@@ -143,50 +141,7 @@ def create_node(self) -> TraversalNode:
 
 class BaseTask(BaseTraversal):
     """
-    An execution node of the pipeline.
-    Please refer to [concepts](concepts/task.md) for more information.
-
-    Attributes:
-        name (str): The name of the node.
-        command (str): The command to execute.
-
-            - For python functions, [dotted path](concepts/task.md/#python_functions) to the function.
-            - For shell commands: command to execute in the shell.
-            - For notebooks: path to the notebook.
-        command_type (str): The type of command to execute.
-            Can be one of "shell", "python", or "notebook".
-        catalog (Optional[Catalog]): The catalog to sync data from/to.
-            Please see Catalog about the structure of the catalog.
-        overrides (Dict[str, Any]): Any overrides to the command.
-            Individual tasks can override the global configuration config by referring to the
-            specific override.
-
-            For example,
-            ### Global configuration
-            ```yaml
-            executor:
-              type: local-container
-              config:
-                docker_image: "runnable/runnable:latest"
-                overrides:
-                  custom_docker_image:
-                    docker_image: "runnable/runnable:custom"
-            ```
-            ### Task specific configuration
-            ```python
-            task = Task(name="task", command="echo 'hello'", command_type="shell",
-                    overrides={'local-container': custom_docker_image})
-            ```
-        notebook_output_path (Optional[str]): The path to save the notebook output.
-            Only used when command_type is 'notebook', defaults to command+_out.ipynb
-        optional_ploomber_args (Optional[Dict[str, Any]]): Any optional ploomber args.
-            Only used when command_type is 'notebook', defaults to {}
-        output_cell_tag (Optional[str]): The tag of the output cell.
-            Only used when command_type is 'notebook', defaults to "runnable_output"
-        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
-        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
-        on_failure (str): The name of the node to execute if the step fails.
-
+    Base task type which has catalog, overrides, returns and secrets.
     """
 
     catalog: Optional[Catalog] = Field(default=None, alias="catalog")
@@ -220,12 +175,50 @@ def create_node(self) -> TaskNode:
 class PythonTask(BaseTask):
     """
     An execution node of the pipeline of python functions.
+    Please refer to [concepts](concepts/task.md/#python_functions) for more information.
 
     Attributes:
         name (str): The name of the node.
         function (callable): The function to execute.
-        catalog (Optional[Catalog]): The catalog to sync data from/to.
-            Please see Catalog about the structure of the catalog.
+
+        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+                Defaults to False.
+        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+                Defaults to False.
+
+        on_failure (str): The name of the node to execute if the step fails.
+
+        returns List[Union[str, TaskReturns]] : A list of the names of variables to return from the task.
+            The names should match the order of the variables returned by the function.
+
+            ```TaskReturns```: can be JSON friendly variables, objects or metrics.
+
+            By default, all variables are assumed to be JSON friendly and will be serialized to JSON.
+            Pydantic models are readily supported and will be serialized to JSON.
+
+            To return a python object, please use ```pickled(<name>)```.
+            It is advised to use ```pickled(<name>)``` for big JSON friendly variables.
+
+            For example,
+            ```python
+            from runnable import pickled
+
+            def f():
+                ...
+                x = 1
+                return x, df # A simple JSON friendly variable and a python object.
+
+            task = PythonTask(name="task", function=f, returns=["x", pickled(df)]))
+            ```
+
+            To mark any JSON friendly variable as a ```metric```, please use ```metric(x)```.
+            Metric variables should be JSON friendly and can be treated just like any other parameter.
+
+        catalog Optional[Catalog]: The files sync data from/to, refer to Catalog.
+
+        secrets List[str]: List of secrets to pass to the task. They are exposed as environment variables
+            and removed after execution.
+
         overrides (Dict[str, Any]): Any overrides to the command.
             Individual tasks can override the global configuration config by referring to the
             specific override.
@@ -246,11 +239,6 @@ class PythonTask(BaseTask):
             task = PythonTask(name="task", function="function'",
                     overrides={'local-container': custom_docker_image})
             ```
-
-        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
-        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
-        on_failure (str): The name of the node to execute if the step fails.
-
     """
 
     function: Callable = Field(exclude=True)
@@ -269,15 +257,52 @@ def command(self) -> str:
 
 class NotebookTask(BaseTask):
     """
-    An execution node of the pipeline of type notebook.
-    Please refer to [concepts](concepts/task.md) for more information.
+    An execution node of the pipeline of notebook.
+    Please refer to [concepts](concepts/task.md/#notebooks) for more information.
+
+    We internally use [Ploomber engine](https://github.com/ploomber/ploomber-engine) to execute the notebook.
 
     Attributes:
         name (str): The name of the node.
-        notebook: The path to the notebook
-        catalog (Optional[Catalog]): The catalog to sync data from/to.
-            Please see Catalog about the structure of the catalog.
-        returns: A list of the names of variables to return from the notebook.
+        notebook (str): The path to the notebook relative the project root.
+        optional_ploomber_args (Dict[str, Any]): Any optional ploomber args, please refer to
+            [Ploomber engine](https://github.com/ploomber/ploomber-engine) for more information.
+
+        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+                Defaults to False.
+        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+                Defaults to False.
+
+        on_failure (str): The name of the node to execute if the step fails.
+
+        returns List[Union[str, TaskReturns]] : A list of the names of variables to return from the task.
+            The names should match the order of the variables returned by the function.
+
+            ```TaskReturns```: can be JSON friendly variables, objects or metrics.
+
+            By default, all variables are assumed to be JSON friendly and will be serialized to JSON.
+            Pydantic models are readily supported and will be serialized to JSON.
+
+            To return a python object, please use ```pickled(<name>)```.
+            It is advised to use ```pickled(<name>)``` for big JSON friendly variables.
+
+            For example,
+            ```python
+            from runnable import pickled
+
+            # assume, example.ipynb is the notebook with df and x as variables in some cells.
+
+            task = Notebook(name="task", notebook="example.ipynb", returns=["x", pickled(df)]))
+            ```
+
+            To mark any JSON friendly variable as a ```metric```, please use ```metric(x)```.
+            Metric variables should be JSON friendly and can be treated just like any other parameter.
+
+        catalog Optional[Catalog]: The files sync data from/to, refer to Catalog.
+
+        secrets List[str]: List of secrets to pass to the task. They are exposed as environment variables
+        and removed after execution.
+
         overrides (Dict[str, Any]): Any overrides to the command.
             Individual tasks can override the global configuration config by referring to the
             specific override.
@@ -295,18 +320,9 @@ class NotebookTask(BaseTask):
             ```
             ### Task specific configuration
             ```python
-            task = NotebookTask(name="task", notebook="evaluation.ipynb",
+            task = NotebookTask(name="task", notebook="example.ipynb",
                     overrides={'local-container': custom_docker_image})
             ```
-        notebook_output_path (Optional[str]): The path to save the notebook output.
-            Only used when command_type is 'notebook', defaults to command+_out.ipynb
-        optional_ploomber_args (Optional[Dict[str, Any]]): Any optional ploomber args.
-            Only used when command_type is 'notebook', defaults to {}
-
-        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
-        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
-        on_failure (str): The name of the node to execute if the step fails.
-
     """
 
     notebook: str = Field(serialization_alias="command")
@@ -319,15 +335,33 @@ def command_type(self) -> str:
 
 class ShellTask(BaseTask):
     """
-    An execution node of the pipeline of type shell.
-    Please refer to [concepts](concepts/task.md) for more information.
+    An execution node of the pipeline of shell script.
+    Please refer to [concepts](concepts/task.md/#shell) for more information.
+
 
     Attributes:
         name (str): The name of the node.
-        command: The shell command to execute.
-        catalog (Optional[Catalog]): The catalog to sync data from/to.
-            Please see Catalog about the structure of the catalog.
-        returns: A list of the names of variables to capture from environment variables of shell.
+        command (str): The path to the notebook relative the project root.
+        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+                Defaults to False.
+        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+                Defaults to False.
+
+        on_failure (str): The name of the node to execute if the step fails.
+
+        returns List[str] : A list of the names of environment variables to collect from the task.
+
+            The names should match the order of the variables returned by the function.
+            Shell based tasks can only return JSON friendly variables.
+
+            To mark any JSON friendly variable as a ```metric```, please use ```metric(x)```.
+            Metric variables should be JSON friendly and can be treated just like any other parameter.
+
+        catalog Optional[Catalog]: The files sync data from/to, refer to Catalog.
+
+        secrets List[str]: List of secrets to pass to the task. They are exposed as environment variables
+        and removed after execution.
+
         overrides (Dict[str, Any]): Any overrides to the command.
             Individual tasks can override the global configuration config by referring to the
             specific override.
@@ -345,14 +379,10 @@ class ShellTask(BaseTask):
             ```
             ### Task specific configuration
             ```python
-            task = ShellTask(name="task", command="exit 0",
+            task = ShellTask(name="task", command="export x=1",
                     overrides={'local-container': custom_docker_image})
             ```
 
-        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
-        terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
-        on_failure (str): The name of the node to execute if the step fails.
-
     """
 
     command: str = Field(alias="command")
@@ -364,16 +394,20 @@ def command_type(self) -> str:
 
 class Stub(BaseTraversal):
     """
-    A node that does nothing.
+    A node that passes through the pipeline with no action. Just like ```pass``` in Python.
+    Please refer to [concepts](concepts/task.md/#stub) for more information.
 
     A stub node can tak arbitrary number of arguments.
-    Please refer to [concepts](concepts/stub.md) for more information.
 
     Attributes:
         name (str): The name of the node.
-        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+        command (str): The path to the notebook relative the project root.
         terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+                Defaults to False.
+        terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+                Defaults to False.
 
+        on_failure (str): The name of the node to execute if the step fails.
     """
 
     model_config = ConfigDict(extra="ignore")
@@ -422,12 +456,13 @@ class Map(BaseTraversal):
     Please refer to [concepts](concepts/map.md) for more information.
 
     Attributes:
-        branch: The pipeline to execute for each item.
+        branch (Pipeline): The pipeline to execute for each item.
 
-        iterate_on: The name of the parameter to iterate over.
+        iterate_on (str): The name of the parameter to iterate over.
             The parameter should be defined either by previous steps or statically at the start of execution.
 
-        iterate_as: The name of the iterable to be passed to functions.
+        iterate_as (str): The name of the iterable to be passed to functions.
+        reducer (Callable): The function to reduce the results of the branches.
 
 
         overrides (Dict[str, Any]): Any overrides to the command.
@@ -510,29 +545,44 @@ def create_node(self) -> FailNode:
 
 class Pipeline(BaseModel):
     """
-    A Pipeline is a directed acyclic graph of Steps that define a workflow.
+    A Pipeline is a sequence of Steps.
 
     Attributes:
-        steps (List[Stub | PythonTask | NotebookTask | ShellTask | Parallel | Map | Success | Fail]):
+        steps (List[Stub | PythonTask | NotebookTask | ShellTask | Parallel | Map]]):
             A list of Steps that make up the Pipeline.
-        start_at (Stub | Task | Parallel | Map): The name of the first Step in the Pipeline.
+
+            The order of steps is important as it determines the order of execution.
+            Any on failure behavior should the first step in ```on_failure``` pipelines.
+
+
+
+        on_failure (List[List[Pipeline], optional): A list of Pipelines to execute in case of failure.
+
+            For example, for the below pipeline:
+                step1 >> step2
+                and step1 to reach step3 in case of failure.
+
+                failure_pipeline = Pipeline(steps=[step1, step3])
+
+                pipeline = Pipeline(steps=[step1, step2, on_failure=[failure_pipeline])
+
         name (str, optional): The name of the Pipeline. Defaults to "".
         description (str, optional): A description of the Pipeline. Defaults to "".
-        add_terminal_nodes (bool, optional): Whether to add terminal nodes to the Pipeline. Defaults to True.
 
-    The default behavior is to add "success" and "fail" nodes to the Pipeline.
-    To add custom success and fail nodes, set add_terminal_nodes=False and create success
-    and fail nodes manually.
+    The pipeline implicitly add success and fail nodes.
 
     """
 
-    steps: List[Union[StepType, List[StepType]]]
+    steps: List[Union[StepType, List["Pipeline"]]]
     name: str = ""
     description: str = ""
-    add_terminal_nodes: bool = True  # Adds "success" and "fail" nodes
 
     internal_branch_name: str = ""
 
+    @property
+    def add_terminal_nodes(self) -> bool:
+        return True
+
     _dag: graph.Graph = PrivateAttr()
     model_config = ConfigDict(extra="forbid")
 
@@ -590,6 +640,7 @@ def model_post_init(self, __context: Any) -> None:
                 Any definition of pipeline should have one node that terminates with success.
         """
         # TODO: Bug with repeat names
+        # TODO: https://github.com/AstraZeneca/runnable/issues/156
 
         success_path: List[StepType] = []
         on_failure_paths: List[List[StepType]] = []
@@ -598,7 +649,7 @@ def model_post_init(self, __context: Any) -> None:
             if isinstance(step, (Stub, PythonTask, NotebookTask, ShellTask, Parallel, Map)):
                 success_path.append(step)
                 continue
-            on_failure_paths.append(step)
+            # on_failure_paths.append(step)
 
         if not success_path:
             raise Exception("There should be some success path")
@@ -654,21 +705,19 @@ def execute(
 
         Traverse and execute all the steps of the pipeline, eg. [local execution](configurations/executors/local.md).
 
-        Or create the ```yaml``` representation of the pipeline for other executors.
+        Or create the representation of the pipeline for other executors.
 
         Please refer to [concepts](concepts/executor.md) for more information.
 
         Args:
             configuration_file (str, optional): The path to the configuration file. Defaults to "".
-                The configuration file can be overridden by the environment variable runnable_CONFIGURATION_FILE.
+                The configuration file can be overridden by the environment variable RUNNABLE_CONFIGURATION_FILE.
 
             run_id (str, optional): The ID of the run. Defaults to "".
             tag (str, optional): The tag of the run. Defaults to "".
                 Use to group multiple runs.
 
             parameters_file (str, optional): The path to the parameters file. Defaults to "".
-            use_cached (str, optional): Whether to use cached results. Defaults to "".
-                Provide the run_id of the older execution to recover.
 
             log_level (str, optional): The log level. Defaults to defaults.LOG_LEVEL.
         """
diff --git a/tests/test_examples.py b/tests/test_examples.py
index 974a4263..4e338a45 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -73,8 +73,8 @@ def argo_context():
     ("01-tasks/scripts", False, []),
     ("01-tasks/stub", False, []),
     ("02-sequential/default_fail", True, []),
-    ("02-sequential/on_failure_fail", True, []),
-    ("02-sequential/on_failure_succeed", False, []),
+    # ("02-sequential/on_failure_fail", True, []), # need https://github.com/AstraZeneca/runnable/issues/156
+    # ("02-sequential/on_failure_succeed", False, []), # https://github.com/AstraZeneca/runnable/issues/156
     ("02-sequential/traversal", False, []),
     ("03-parameters/passing_parameters_notebook", False, []),
     ("03-parameters/passing_parameters_python", False, []),

From 5d12a09cd948648326fc59e8b4562841ad3af8a2 Mon Sep 17 00:00:00 2001
From: Vijay Vammi <vijay.vammi@astrazeneca.com>
Date: Tue, 4 Jun 2024 21:42:51 +0100
Subject: [PATCH 2/4] docs: comparison with metaflow

---
 docs/configurations/overview.md               |    5 +-
 examples/comparisions/README.md               |    1 -
 examples/comparisons/README.md                |   66 +
 examples/comparisons/metaflow/main.py         |  127 +
 examples/comparisons/metaflow/parameters.json |   46 +
 examples/comparisons/runnable/main.py         |  283 ++
 .../comparisons/runnable/runnable_params.yaml |   41 +
 examples/comparisons/source.py                |  148 +
 poetry.lock                                   | 2405 ++++++++++-------
 pyproject.toml                                |   66 +-
 10 files changed, 2247 insertions(+), 941 deletions(-)
 delete mode 100644 examples/comparisions/README.md
 create mode 100644 examples/comparisons/README.md
 create mode 100644 examples/comparisons/metaflow/main.py
 create mode 100644 examples/comparisons/metaflow/parameters.json
 create mode 100644 examples/comparisons/runnable/main.py
 create mode 100644 examples/comparisons/runnable/runnable_params.yaml
 create mode 100644 examples/comparisons/source.py

diff --git a/docs/configurations/overview.md b/docs/configurations/overview.md
index 47ea5a5f..fed9ee7f 100644
--- a/docs/configurations/overview.md
+++ b/docs/configurations/overview.md
@@ -1,3 +1,2 @@
-**runnable** is designed to enable the pipeline execution in varied computational environments.
-
-This is controlled by a ```configuration``` file without changing either the domain code or the pipeline definition.
+**runnable** is designed to enable the pipeline execution in varied computational environments without changing the
+infrastructure patterns.
diff --git a/examples/comparisions/README.md b/examples/comparisions/README.md
deleted file mode 100644
index 769790f9..00000000
--- a/examples/comparisions/README.md
+++ /dev/null
@@ -1 +0,0 @@
-To avoid any subjective bias in comparisons, we take the same example and implement it in alternate frameworks.
diff --git a/examples/comparisons/README.md b/examples/comparisons/README.md
new file mode 100644
index 00000000..d0295f48
--- /dev/null
+++ b/examples/comparisons/README.md
@@ -0,0 +1,66 @@
+In this section, we take the familiar MNIST problem and implement it in different orchestration frameworks.
+
+The [original source code](https://github.com/pytorch/examples/blob/main/mnist/main.py) is shown in [source.py](source.py)
+
+The individual directories are orchestration specific implementations.
+
+## Notes
+
+For the purpose of comparisons, consider the following function:
+
+```python
+def func(x: int, y:pd.DataFrame):
+    # do something with the inputs.
+    # Write a file called data.csv for downstream steps.
+    # return an output.
+    return z
+```
+
+It takes *inputs* x (integer) and y (a pandas dataframe or any other object), does some processing and writes a file to local disk and returns z (a simple datatype or object)
+
+The function in wrapped in runnable as:
+
+```python
+from somewhere import func
+from runnable import PythonTask, pickled, Catalog
+
+# instruction to move the file data.csv from local disk to a blob store
+catalog = Catalog(put=["data.csv"])
+# Call the function, func and expect it to return "z" while moving the files
+# It is expected that "x" and "y" are parameters set by some upstream step.
+# If the return parameter is an object, use pickled("z")
+func_task = PythonTask(name="function", function=func, returns=["z"], catalog=catalog)
+```
+
+
+### metaflow
+
+The function in metaflow's step would rougly be:
+
+```python
+from metaflow import step, conda, FlowSpec
+
+class Flow(FlowSpec)
+
+    @conda(libraries={...})
+    @step
+    def func_step(self):
+        from somewhere import func
+        self.z = func(self.x, self.y)
+
+        # Use metaflow.S3 to move files
+        # Move to next step.
+        ...
+```
+
+- The API between ```runnable``` and ```metaflow``` are comparable.
+- There is a mechanism for functions to accept/return parameters.
+
+The differences between both:
+
+- dependency management in ```runnable``` is expected to be user driven and "pythonic" (venv, poetry etc) while metaflow packages provides a per-step (or flow level) libraries.
+- data flow between steps is by an instruction in runnable to ```glob``` files in
+local disk and present them in the same structure to downstream steps. ```metaflow``` needs this to be via code.
+- runnable allows ```notebooks``` to be a task, allowing simple data types to be
+parameters while collecting objects from the notebook execution.
+- metaflow has a *platform* side to it and comes up with prescribed infrastructure while runnable is, in many ways, a transpiler to your chosen infrastructure.
diff --git a/examples/comparisons/metaflow/main.py b/examples/comparisons/metaflow/main.py
new file mode 100644
index 00000000..68724bd7
--- /dev/null
+++ b/examples/comparisons/metaflow/main.py
@@ -0,0 +1,127 @@
+# Code as seen here: https://outerbounds.com/docs/use-pytorch-with-metaflow/
+
+# ruff: noqa
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from metaflow import FlowSpec, Parameter, conda_base, step
+from torch_utilities import Net, get_data_loaders, test, train
+from torchvision import datasets, transforms
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        output = F.log_softmax(x, dim=1)
+        return output
+
+
+def train(model, train_loader, optimizer, epoch):
+    model.train()
+    for idx, (data, target) in enumerate(train_loader):
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if idx * len(data) % 10000 == 0:
+            out = "Train Epoch: " + "{} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                epoch, idx * len(data), len(train_loader.dataset), 100.0 * idx / len(train_loader), loss.item()
+            )
+            print(out)
+
+
+def test(model, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            output = model(data)
+            test_loss += F.nll_loss(output, target, reduction="sum").item()
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+    return correct / len(test_loader.dataset)
+
+
+def get_data_loaders(name="train"):
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    if name == "train":
+        dataset = datasets.MNIST("../data", train=True, download=True, transform=transform)
+        train_args = {"batch_size": 32}
+        return dataset, train_args
+    elif name == "test":
+        dataset = datasets.MNIST("../data", train=False, transform=transform)
+        test_args = {"batch_size": 32}
+        return dataset, test_args
+
+
+@conda_base(libraries={"pytorch": "1.11.0", "torchvision": "0.12.0"}, python="3.8")
+class TorchFlow(FlowSpec):
+    lr = Parameter("lr", default=0.01)
+    epochs = Parameter("epochs", default=1)
+
+    # It might be better to user a parameters.json?
+    # There is an example json in this directory.
+
+    @step
+    def start(self):
+        self.next(self.get_data)
+
+    @step
+    def get_data(self):
+        import torch
+
+        train_dataset, train_args = get_data_loaders()
+        test_dataset, test_args = get_data_loaders("test")
+        self.train_loader = torch.utils.data.DataLoader(train_dataset, **train_args)
+        self.test_loader = torch.utils.data.DataLoader(test_dataset, **test_args)
+        self.next(self.fit_model)
+
+    @step
+    def fit_model(self):
+        import torch.optim as optim
+        from torch.optim.lr_scheduler import StepLR
+
+        self.model = Net()
+        optimizer = optim.Adadelta(self.model.parameters(), lr=self.lr)
+        scheduler = StepLR(optimizer, step_size=1)
+        for epoch in range(1, self.epochs + 1):
+            train(self.model, self.train_loader, optimizer, epoch)
+            _ = test(self.model, self.test_loader)
+            scheduler.step()
+        self.next(self.evaluate_model)
+
+    @step
+    def evaluate_model(self):
+        self.test_score = test(self.model, self.test_loader)
+        print(f"Model scored {100*self.test_score}%")
+        self.next(self.end)
+
+    @step
+    def end(self):
+        pass
+
+
+if __name__ == "__main__":
+    TorchFlow()
diff --git a/examples/comparisons/metaflow/parameters.json b/examples/comparisons/metaflow/parameters.json
new file mode 100644
index 00000000..73ac2078
--- /dev/null
+++ b/examples/comparisons/metaflow/parameters.json
@@ -0,0 +1,46 @@
+{
+  "cuda_args": {
+    "num_workers": 1,
+    "pin_memory": true,
+    "shuffle": true
+  },
+  "train_batch_size": {
+    "num_workers": 1,
+    "pin_memory": true,
+    "shuffle": true,
+    "batch_size": 64
+  },
+  "test_batch_size": {
+    "num_workers": 1,
+    "pin_memory": true,
+    "shuffle": true,
+    "batch_size": 1000
+  },
+  "num_epochs": 14,
+  "lr": 1,
+  "gamma": 0.7,
+  "seed": 1,
+  "log_interval": 10,
+  "conv1_params": {
+    "in_channels": 1,
+    "out_channels": 32,
+    "kernel_size": 3,
+    "stride": 1
+  },
+  "conv2_params": {
+    "in_channels": 32,
+    "out_channels": 64,
+    "kernel_size": 3,
+    "stride": 1
+  },
+  "dropout1": 0.25,
+  "dropout2": 0.5,
+  "fc1": [
+    9216,
+    128
+  ],
+  "fc2": [
+    128,
+    10
+  ]
+}
diff --git a/examples/comparisons/runnable/main.py b/examples/comparisons/runnable/main.py
new file mode 100644
index 00000000..3b9d9c36
--- /dev/null
+++ b/examples/comparisons/runnable/main.py
@@ -0,0 +1,283 @@
+# https://github.com/pytorch/examples/blob/main/mnist/main.py
+# The main function is modularized into many smaller functions.
+
+# ruff: noqa
+
+"""
+The captured data/logs can be found in the.catalog folder.
+
+tree .catalog/pounded-swanson-0626
+├── data
+│   └── MNIST
+│       └── raw
+│           ├── t10k-images-idx3-ubyte
+│           ├── t10k-images-idx3-ubyte.gz
+│           ├── t10k-labels-idx1-ubyte
+│           ├── t10k-labels-idx1-ubyte.gz
+│           ├── train-images-idx3-ubyte
+│           ├── train-images-idx3-ubyte.gz
+│           ├── train-labels-idx1-ubyte
+│           └── train-labels-idx1-ubyte.gz
+├── dataloadersJYZ.execution.log
+├── device.dill
+├── devicetaskJ6P.execution.log
+├── mnist_cnn.pt_epoch_1.pt
+├── mnist_cnn.pt_epoch_10.pt
+├── mnist_cnn.pt_epoch_11.pt
+├── mnist_cnn.pt_epoch_12.pt
+├── mnist_cnn.pt_epoch_13.pt
+├── mnist_cnn.pt_epoch_14.pt
+├── mnist_cnn.pt_epoch_2.pt
+├── mnist_cnn.pt_epoch_3.pt
+├── mnist_cnn.pt_epoch_4.pt
+├── mnist_cnn.pt_epoch_5.pt
+├── mnist_cnn.pt_epoch_6.pt
+├── mnist_cnn.pt_epoch_7.pt
+├── mnist_cnn.pt_epoch_8.pt
+├── mnist_cnn.pt_epoch_9.pt
+├── test_loader.dill
+├── train_loader.dill
+└── trainingloop0TF.execution.log
+
+4 directories, 28 files
+"""
+
+
+from __future__ import print_function
+
+# import argparse
+from typing import Any, Dict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from pydantic import BaseModel
+from torch.optim.lr_scheduler import StepLR
+from torchvision import datasets, transforms
+
+from runnable import Catalog, Pipeline, PythonTask, pickled
+
+
+# pydantic model for the convolutional layer parameters.
+# This is not essential but it makes the code more readable.
+class ConvParams(BaseModel):
+    in_channels: int
+    out_channels: int
+    kernel_size: int
+    stride: int
+
+
+class Net(nn.Module):
+    # A parameterized version of the Net class would be
+    # def __init__(
+    #     self,
+    #     conv1_params: ConvParams,
+    #     conv2_params: ConvParams,
+    #     dropout1: float,
+    #     dropout2: float,
+    #     fc1: List[int],
+    #     fc2: List[int],
+    # ):
+    #     super(Net, self).__init__()
+    #     self.conv1 = nn.Conv2d(**conv1_params.model_dump())
+    #     self.conv2 = nn.Conv2d(**conv2_params.model_dump())
+    #     self.dropout1 = nn.Dropout(dropout1)
+    #     self.dropout2 = nn.Dropout(dropout2)
+    #     self.fc1 = nn.Linear(*fc1)
+    #     self.fc2 = nn.Linear(*fc2)
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        output = F.log_softmax(x, dim=1)
+        return output
+
+
+# same as the source but with explicit arguments
+def train(
+    model,
+    device,
+    train_loader,
+    optimizer,
+    epoch,
+    dry_run: bool = False,
+    log_interval: int = 10,
+):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % log_interval == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(train_loader.dataset),
+                    100.0 * batch_idx / len(train_loader),
+                    loss.item(),
+                )
+            )
+            if dry_run:
+                break
+
+
+def test(model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(output, target, reduction="sum").item()  # sum up batch loss
+            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    print(
+        "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+            test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
+        )
+    )
+
+
+# A function to prepare the dataset.
+def prepare_dataset(train_batch_size: Dict[str, Any], test_batch_size: Dict[str, Any]):
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    dataset1 = datasets.MNIST("data", train=True, download=True, transform=transform)
+    dataset2 = datasets.MNIST("data", train=False, transform=transform)
+    train_loader = torch.utils.data.DataLoader(dataset1, **train_batch_size)
+    test_loader = torch.utils.data.DataLoader(dataset2, **test_batch_size)
+
+    return train_loader, test_loader
+
+
+# A function to identify the device to use.
+def get_device(use_cuda: bool = False, use_mps: bool = False, seed: int = 1):
+    use_cuda = not use_cuda and torch.cuda.is_available()
+    use_mps = not use_mps and torch.backends.mps.is_available()
+
+    torch.manual_seed(seed)
+
+    if use_cuda:
+        device = torch.device("cuda")
+    elif use_mps:
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
+
+    return device
+
+
+def fit_model(
+    device,
+    train_loader,
+    test_loader,
+    num_epochs: int,
+    gamma: float = 0.7,
+    dry_run: bool = False,
+    lr: float = 1.0,
+):
+    model = Net().to(device)
+    optimizer = optim.Adadelta(model.parameters(), lr=lr)
+
+    scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
+    for epoch in range(1, num_epochs + 1):
+        print(f"Epoch {epoch}")
+        train(model, device, train_loader, optimizer, epoch, dry_run=dry_run)
+        test(model, device, test_loader)
+        scheduler.step()
+
+        torch.save(model.state_dict(), f"mnist_cnn.pt_epoch_{epoch}.pt")
+
+
+def main():
+    # device = get_device(args.use_cuda, args.use_mps)
+    device_task = PythonTask(
+        name="device_task",
+        function=get_device,
+        returns=[pickled("device")],
+    )
+
+    data_catalog = Catalog(put=["data/**/*"])
+
+    # train_loader, test_loader = prepare_dataset()
+    data_loaders = PythonTask(
+        name="data_loaders",
+        function=prepare_dataset,
+        returns=[pickled("train_loader"), pickled("test_loader")],
+        catalog=data_catalog,
+    )
+
+    train_catalog = Catalog(put=["mnist_cnn.pt_epoch_*"], get=["data/**/*"])
+
+    # fit_model(
+    #     device,
+    #     train_loader,
+    #     test_loader,
+    #     args.epochs,
+    #     gamma=args.gamma,
+    #     dry_run=args.dry_run,
+    #     lr=args.lr,
+    # )
+    fit_model_task = PythonTask(
+        name="training_loop",
+        function=fit_model,
+        catalog=train_catalog,
+        terminate_with_success=True,
+    )
+
+    pipeline = Pipeline(steps=[device_task, data_loaders, fit_model_task])
+    pipeline.execute(parameters_file="examples/comparisons/runnable_params.yaml")
+
+
+if __name__ == "__main__":
+    # parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
+    # parser.add_argument(
+    #     "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
+    # )
+    # parser.add_argument(
+    #     "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
+    # )
+    # parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
+    # parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
+    # parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
+    # parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
+    # parser.add_argument("--no-mps", action="store_true", default=False, help="disables macOS GPU training")
+    # parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
+    # parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
+    # parser.add_argument(
+    #     "--log-interval",
+    #     type=int,
+    #     default=10,
+    #     metavar="N",
+    #     help="how many batches to wait before logging training status",
+    # )
+    # parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
+    # args = parser.parse_args()
+    # main(args)
+    main()
diff --git a/examples/comparisons/runnable/runnable_params.yaml b/examples/comparisons/runnable/runnable_params.yaml
new file mode 100644
index 00000000..ae736318
--- /dev/null
+++ b/examples/comparisons/runnable/runnable_params.yaml
@@ -0,0 +1,41 @@
+cuda_args: &cuda_args
+  num_workers: 1
+  pin_memory: True
+  shuffle: True
+
+train_batch_size:
+  <<: *cuda_args # yaml syntax to merge keys
+  # The above essentially does this.
+  # num_workers: 1
+  # pin_memory: True
+  # shuffle: True
+  batch_size: 64
+
+test_batch_size:
+  <<: *cuda_args
+  batch_size: 1000
+
+num_epochs: 14
+lr: 1.0
+gamma: 0.7
+seed: 1
+log_interval: 10
+
+## NN params
+# not used by code, this is just to show the extent of parameterization
+conv1_params:
+  in_channels: 1
+  out_channels: 32
+  kernel_size: 3
+  stride: 1
+
+conv2_params:
+  in_channels: 32
+  out_channels: 64
+  kernel_size: 3
+  stride: 1
+
+dropout1: 0.25
+dropout2: 0.5
+fc1: [9216, 128]
+fc2: [128, 10]
diff --git a/examples/comparisons/source.py b/examples/comparisons/source.py
new file mode 100644
index 00000000..10c7daed
--- /dev/null
+++ b/examples/comparisons/source.py
@@ -0,0 +1,148 @@
+# Script as taken from https://github.com/pytorch/examples/blob/main/mnist/main.py
+from __future__ import print_function
+
+import argparse
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.optim.lr_scheduler import StepLR
+from torchvision import datasets, transforms
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        output = F.log_softmax(x, dim=1)
+        return output
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(train_loader.dataset),
+                    100.0 * batch_idx / len(train_loader),
+                    loss.item(),
+                )
+            )
+            if args.dry_run:
+                break
+
+
+def test(model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(output, target, reduction="sum").item()  # sum up batch loss
+            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    print(
+        "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+            test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
+        )
+    )
+
+
+def main():
+    # Training settings
+    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
+    parser.add_argument(
+        "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
+    )
+    parser.add_argument(
+        "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
+    )
+    parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
+    parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
+    parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
+    parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
+    parser.add_argument("--no-mps", action="store_true", default=False, help="disables macOS GPU training")
+    parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
+    parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
+    parser.add_argument(
+        "--log-interval",
+        type=int,
+        default=10,
+        metavar="N",
+        help="how many batches to wait before logging training status",
+    )
+    parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
+    args = parser.parse_args()
+    use_cuda = not args.no_cuda and torch.cuda.is_available()
+    use_mps = not args.no_mps and torch.backends.mps.is_available()
+
+    torch.manual_seed(args.seed)
+
+    if use_cuda:
+        device = torch.device("cuda")
+    elif use_mps:
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
+
+    train_kwargs = {"batch_size": args.batch_size}
+    test_kwargs = {"batch_size": args.test_batch_size}
+    if use_cuda:
+        cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
+        train_kwargs.update(cuda_kwargs)
+        test_kwargs.update(cuda_kwargs)
+
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
+    dataset2 = datasets.MNIST("../data", train=False, transform=transform)
+    train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
+    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
+
+    model = Net().to(device)
+    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
+
+    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
+    for epoch in range(1, args.epochs + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test(model, device, test_loader)
+        scheduler.step()
+
+    if args.save_model:
+        torch.save(model.state_dict(), "mnist_cnn.pt")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/poetry.lock b/poetry.lock
index da08314b..b158f414 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,13 +24,13 @@ files = [
 
 [[package]]
 name = "annotated-types"
-version = "0.6.0"
+version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
-    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
 [[package]]
@@ -101,13 +101,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p
 
 [[package]]
 name = "babel"
-version = "2.14.0"
+version = "2.15.0"
 description = "Internationalization utilities"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"},
-    {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
+    {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"},
+    {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
 ]
 
 [package.extras]
@@ -255,6 +255,17 @@ files = [
 [package.dependencies]
 numpy = {version = ">=1.19.0", markers = "python_version >= \"3.9\""}
 
+[[package]]
+name = "cachetools"
+version = "5.3.3"
+description = "Extensible memoizing collections and decorators"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
+]
+
 [[package]]
 name = "catalogue"
 version = "2.0.10"
@@ -556,13 +567,13 @@ files = [
 
 [[package]]
 name = "confection"
-version = "0.1.4"
+version = "0.1.5"
 description = "The sweetest config system for Python"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "confection-0.1.4-py3-none-any.whl", hash = "sha256:a658818d004939069c3e2b3db74a2cb9d956a5e61a1c9ad61788e0ee09a7090f"},
-    {file = "confection-0.1.4.tar.gz", hash = "sha256:e80f22fd008b5231a2e8852fac6de9e28f2276a04031d0536cff74fe4a990c8f"},
+    {file = "confection-0.1.5-py3-none-any.whl", hash = "sha256:e29d3c3f8eac06b3f77eb9dfb4bf2fc6bcc9622a98ca00a698e3d019c6430b14"},
+    {file = "confection-0.1.5.tar.gz", hash = "sha256:8e72dd3ca6bd4f48913cd220f10b8275978e740411654b6e8ca6d7008c590f0e"},
 ]
 
 [package.dependencies]
@@ -634,63 +645,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.4.4"
+version = "7.5.3"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -796,6 +807,23 @@ files = [
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 ]
 
+[[package]]
+name = "deprecated"
+version = "1.2.14"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = true
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
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+]
+
+[package.dependencies]
+wrapt = ">=1.10,<2"
+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
+
 [[package]]
 name = "dill"
 version = "0.3.8"
@@ -824,22 +852,23 @@ files = [
 
 [[package]]
 name = "docker"
-version = "7.0.0"
+version = "7.1.0"
 description = "A Python library for the Docker Engine API."
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-packaging = ">=14.0"
 pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
 requests = ">=2.26.0"
 urllib3 = ">=1.26.0"
 
 [package.extras]
+dev = ["coverage (==7.2.7)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.1.0)", "ruff (==0.1.8)"]
+docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
 ssh = ["paramiko (>=2.4.3)"]
 websockets = ["websocket-client (>=1.3.0)"]
 
@@ -884,13 +913,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -926,13 +955,13 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.13.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -953,53 +982,53 @@ files = [
 
 [[package]]
 name = "fonttools"
-version = "4.51.0"
+version = "4.53.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1016,6 +1045,44 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "fsspec"
+version = "2024.5.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
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+tqdm = ["tqdm"]
+
 [[package]]
 name = "gast"
 version = "0.5.4"
@@ -1206,13 +1273,13 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "0.42.2"
+version = "0.45.2"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -1220,69 +1287,61 @@ colorama = ">=0.4"
 
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 description = "HTTP/2-based RPC framework"
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 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.62.1)"]
+protobuf = ["grpcio-tools (>=1.64.0)"]
 
 [[package]]
 name = "h5py"
@@ -1319,13 +1378,13 @@ numpy = ">=1.17.3"
 
 [[package]]
 name = "identify"
-version = "2.5.35"
+version = "2.5.36"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1390,6 +1449,20 @@ files = [
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 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "ipython"
 version = "8.18.1"
@@ -1448,13 +1521,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1465,24 +1538,24 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.4.0"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "jsonschema"
-version = "4.21.1"
+version = "4.22.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1511,13 +1584,13 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.1"
+version = "8.6.2"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1530,7 +1603,7 @@ traitlets = ">=5.3"
 
 [package.extras]
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+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
@@ -1565,13 +1638,13 @@ files = [
 
 [[package]]
 name = "keras"
-version = "3.2.1"
+version = "3.3.3"
 description = "Multi-backend Keras."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -1698,17 +1771,39 @@ files = [
 
 [[package]]
 name = "langcodes"
-version = "3.3.0"
+version = "3.4.0"
 description = "Tools for labeling human languages with IETF language tags"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
+[package.dependencies]
+language-data = ">=1.2"
+
 [package.extras]
-data = ["language-data (>=1.1,<2.0)"]
+build = ["build", "twine"]
+test = ["pytest", "pytest-cov"]
+
+[[package]]
+name = "language-data"
+version = "1.2.0"
+description = "Supplementary data about languages used by the langcodes module"
+optional = false
+python-versions = "*"
+files = [
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+[package.dependencies]
+marisa-trie = ">=0.7.7"
+
+[package.extras]
+build = ["build", "twine"]
+test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "libclang"
@@ -1742,6 +1837,109 @@ files = [
 [package.dependencies]
 altgraph = ">=0.17"
 
+[[package]]
+name = "marisa-trie"
+version = "1.1.1"
+description = "Static memory-efficient and fast Trie-like structures for Python."
+optional = false
+python-versions = ">=3.7"
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+    {file = "matplotlib-3.9.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:290d304e59be2b33ef5c2d768d0237f5bd132986bdcc66f80bc9bcc300066a03"},
+    {file = "matplotlib-3.9.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ff2e239c26be4f24bfa45860c20ffccd118d270c5b5d081fa4ea409b5469fcd"},
+    {file = "matplotlib-3.9.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:af4001b7cae70f7eaacfb063db605280058246de590fa7874f00f62259f2df7e"},
+    {file = "matplotlib-3.9.0.tar.gz", hash = "sha256:e6d29ea6c19e34b30fb7d88b7081f869a03014f66fe06d62cc77d5a6ea88ed7a"},
 ]
 
 [package.dependencies]
@@ -1896,12 +2095,15 @@ cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21"
+numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[package.extras]
+dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -1951,34 +2153,34 @@ files = [
 
 [[package]]
 name = "mkdocs"
-version = "1.5.3"
+version = "1.6.0"
 description = "Project documentation with Markdown."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "mkdocs-1.5.3-py3-none-any.whl", hash = "sha256:3b3a78e736b31158d64dbb2f8ba29bd46a379d0c6e324c2246c3bc3d2189cfc1"},
-    {file = "mkdocs-1.5.3.tar.gz", hash = "sha256:eb7c99214dcb945313ba30426c2451b735992c73c2e10838f76d09e39ff4d0e2"},
+    {file = "mkdocs-1.6.0-py3-none-any.whl", hash = "sha256:1eb5cb7676b7d89323e62b56235010216319217d4af5ddc543a91beb8d125ea7"},
+    {file = "mkdocs-1.6.0.tar.gz", hash = "sha256:a73f735824ef83a4f3bcb7a231dcab23f5a838f88b7efc54a0eef5fbdbc3c512"},
 ]
 
 [package.dependencies]
 click = ">=7.0"
 colorama = {version = ">=0.4", markers = "platform_system == \"Windows\""}
 ghp-import = ">=1.0"
-importlib-metadata = {version = ">=4.3", markers = "python_version < \"3.10\""}
+importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 jinja2 = ">=2.11.1"
-markdown = ">=3.2.1"
+markdown = ">=3.3.6"
 markupsafe = ">=2.0.1"
 mergedeep = ">=1.3.4"
+mkdocs-get-deps = ">=0.2.0"
 packaging = ">=20.5"
 pathspec = ">=0.11.1"
-platformdirs = ">=2.2.0"
 pyyaml = ">=5.1"
 pyyaml-env-tag = ">=0.1"
 watchdog = ">=2.0"
 
 [package.extras]
 i18n = ["babel (>=2.9.0)"]
-min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-import (==1.0)", "importlib-metadata (==4.3)", "jinja2 (==2.11.1)", "markdown (==3.2.1)", "markupsafe (==2.0.1)", "mergedeep (==1.3.4)", "packaging (==20.5)", "pathspec (==0.11.1)", "platformdirs (==2.2.0)", "pyyaml (==5.1)", "pyyaml-env-tag (==0.1)", "typing-extensions (==3.10)", "watchdog (==2.0)"]
+min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-import (==1.0)", "importlib-metadata (==4.4)", "jinja2 (==2.11.1)", "markdown (==3.3.6)", "markupsafe (==2.0.1)", "mergedeep (==1.3.4)", "mkdocs-get-deps (==0.2.0)", "packaging (==20.5)", "pathspec (==0.11.1)", "pyyaml (==5.1)", "pyyaml-env-tag (==0.1)", "watchdog (==2.0)"]
 
 [[package]]
 name = "mkdocs-autorefs"
@@ -2011,15 +2213,32 @@ files = [
 click = ">=8.1"
 markdown = ">=3.3"
 
+[[package]]
+name = "mkdocs-get-deps"
+version = "0.2.0"
+description = "MkDocs extension that lists all dependencies according to a mkdocs.yml file"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mkdocs_get_deps-0.2.0-py3-none-any.whl", hash = "sha256:2bf11d0b133e77a0dd036abeeb06dec8775e46efa526dc70667d8863eefc6134"},
+    {file = "mkdocs_get_deps-0.2.0.tar.gz", hash = "sha256:162b3d129c7fad9b19abfdcb9c1458a651628e4b1dea628ac68790fb3061c60c"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.3", markers = "python_version < \"3.10\""}
+mergedeep = ">=1.3.4"
+platformdirs = ">=2.2.0"
+pyyaml = ">=5.1"
+
 [[package]]
 name = "mkdocs-material"
-version = "9.5.18"
+version = "9.5.25"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.18-py3-none-any.whl", hash = "sha256:1e0e27fc9fe239f9064318acf548771a4629d5fd5dfd45444fd80a953fe21eb4"},
-    {file = "mkdocs_material-9.5.18.tar.gz", hash = "sha256:a43f470947053fa2405c33995f282d24992c752a50114f23f30da9d8d0c57e62"},
+    {file = "mkdocs_material-9.5.25-py3-none-any.whl", hash = "sha256:68fdab047a0b9bfbefe79ce267e8a7daaf5128bcf7867065fcd201ee335fece1"},
+    {file = "mkdocs_material-9.5.25.tar.gz", hash = "sha256:d0662561efb725b712207e0ee01f035ca15633f29a64628e24f01ec99d7078f4"},
 ]
 
 [package.dependencies]
@@ -2027,7 +2246,7 @@ babel = ">=2.10,<3.0"
 colorama = ">=0.4,<1.0"
 jinja2 = ">=3.0,<4.0"
 markdown = ">=3.2,<4.0"
-mkdocs = ">=1.5.3,<1.6.0"
+mkdocs = ">=1.6,<2.0"
 mkdocs-material-extensions = ">=1.3,<2.0"
 paginate = ">=0.5,<1.0"
 pygments = ">=2.16,<3.0"
@@ -2053,13 +2272,13 @@ files = [
 
 [[package]]
 name = "mkdocs-section-index"
-version = "0.3.8"
+version = "0.3.9"
 description = "MkDocs plugin to allow clickable sections that lead to an index page"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_section_index-0.3.8-py3-none-any.whl", hash = "sha256:823d298d78bc1e73e23678ff60889f3c369c2167b03dba73fea88bd0e268a60d"},
-    {file = "mkdocs_section_index-0.3.8.tar.gz", hash = "sha256:bbd209f0da79441baf136ef3a9c40665bb9681d1fb62c73ca2f116fd1388a404"},
+    {file = "mkdocs_section_index-0.3.9-py3-none-any.whl", hash = "sha256:5e5eb288e8d7984d36c11ead5533f376fdf23498f44e903929d72845b24dfe34"},
+    {file = "mkdocs_section_index-0.3.9.tar.gz", hash = "sha256:b66128d19108beceb08b226ee1ba0981840d14baf8a652b6c59e650f3f92e4f8"},
 ]
 
 [package.dependencies]
@@ -2096,19 +2315,37 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.9.2"
+version = "1.10.0"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings_python-1.9.2-py3-none-any.whl", hash = "sha256:96d82f6424e08db6245e4a15ca95619f4ecd0ddd254c0aa590d4181814e16ee5"},
-    {file = "mkdocstrings_python-1.9.2.tar.gz", hash = "sha256:8546a103c9b22e1778c72c887696acc39a6635fedde3c912ce00f967518a8847"},
+    {file = "mkdocstrings_python-1.10.0-py3-none-any.whl", hash = "sha256:ba833fbd9d178a4b9d5cb2553a4df06e51dc1f51e41559a4d2398c16a6f69ecc"},
+    {file = "mkdocstrings_python-1.10.0.tar.gz", hash = "sha256:71678fac657d4d2bb301eed4e4d2d91499c095fd1f8a90fa76422a87a5693828"},
 ]
 
 [package.dependencies]
-griffe = ">=0.37"
+griffe = ">=0.44"
 mkdocstrings = ">=0.24.2"
 
+[[package]]
+name = "mkl"
+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"},
+    {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"},
+    {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"},
+]
+
+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
 [[package]]
 name = "ml-dtypes"
 version = "0.3.2"
@@ -2148,23 +2385,26 @@ dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
 
 [[package]]
 name = "mlflow-skinny"
-version = "2.12.1"
+version = "2.13.1"
 description = "MLflow is an open source platform for the complete machine learning lifecycle"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "mlflow_skinny-2.12.1-py3-none-any.whl", hash = "sha256:51539de93a7f8b74b2d6b4307f204235469f6fadd19078599abd12a4bcf6b5b0"},
-    {file = "mlflow_skinny-2.12.1.tar.gz", hash = "sha256:a5c3bb2f111867db988d4cdd782b6224fca4fc3d86706e9e587c379973ce7353"},
+    {file = "mlflow_skinny-2.13.1-py3-none-any.whl", hash = "sha256:0c792050a64f2e6b774b83b5093a78b9f4c1e6b1d3ae004b563fcf43f9344699"},
+    {file = "mlflow_skinny-2.13.1.tar.gz", hash = "sha256:c08a734d92e27092a2b407e808db7a817d8f0b0b13883fcefb3460903bdb8de5"},
 ]
 
 [package.dependencies]
+cachetools = ">=5.0.0,<6"
 click = ">=7.0,<9"
 cloudpickle = "<4"
 entrypoints = "<1"
 gitpython = ">=3.1.9,<4"
 importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<8"
+opentelemetry-api = ">=1.0.0,<3"
+opentelemetry-sdk = ">=1.0.0,<3"
 packaging = "<25"
-protobuf = ">=3.12.0,<6"
+protobuf = ">=3.12.0,<5"
 pytz = "<2025"
 pyyaml = ">=5.1,<7"
 requests = ">=2.17.3,<3"
@@ -2176,6 +2416,8 @@ databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "botocore", "go
 extras = ["azureml-core (>=1.2.0)", "boto3", "botocore", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1,<1.4.0)", "mlserver-mlflow (>=1.2.0,!=1.3.1,<1.4.0)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
 gateway = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "pydantic (>=1.0,<3)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
 genai = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "pydantic (>=1.0,<3)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
+jfrog = ["mlflow-jfrog-plugin"]
+langchain = ["langchain (>=0.1.4,<=0.2.1)"]
 sqlserver = ["mlflow-dbstore"]
 xethub = ["mlflow-xethub"]
 
@@ -2190,6 +2432,23 @@ files = [
     {file = "monotonic-1.6.tar.gz", hash = "sha256:3a55207bcfed53ddd5c5bae174524062935efed17792e9de2ad0205ce9ad63f7"},
 ]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "murmurhash"
 version = "1.0.10"
@@ -2234,38 +2493,38 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.9.0"
+version = "1.10.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
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@@ -2325,13 +2584,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.3"
+version = "7.16.4"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2353,9 +2612,9 @@ tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
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-qtpdf = ["nbconvert[qtpng]"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
 test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
@@ -2382,20 +2641,35 @@ traitlets = ">=5.1"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
 test = ["pep440", "pre-commit", "pytest", "testpath"]
 
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+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
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+version = "1.9.0"
 description = "Node.js virtual environment builder"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
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 ]
 
-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -2441,6 +2715,193 @@ files = [
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 ]
 
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+optional = false
+python-versions = ">=3"
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+python-versions = ">=3"
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+[[package]]
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+
+[[package]]
+name = "opentelemetry-sdk"
+version = "1.25.0"
+description = "OpenTelemetry Python SDK"
+optional = true
+python-versions = ">=3.8"
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+typing-extensions = ">=3.7.4"
+
+[[package]]
+name = "opentelemetry-semantic-conventions"
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+description = "OpenTelemetry Semantic Conventions"
+optional = true
+python-versions = ">=3.8"
+files = [
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 version = "3.3.0"
@@ -2772,18 +3233,19 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
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+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
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 python-versions = ">=3.8"
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 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "ploomber-core"
@@ -2829,13 +3291,13 @@ dev = ["flake8", "invoke", "jupytext", "matplotlib", "numpy", "pandas", "pkgmt",
 
 [[package]]
 name = "pluggy"
-version = "1.4.0"
+version = "1.5.0"
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 ]
 
 [package.extras]
@@ -2867,13 +3329,13 @@ test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint"
 
 [[package]]
 name = "pre-commit"
-version = "3.7.0"
+version = "3.7.1"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
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@@ -2931,13 +3393,13 @@ murmurhash = ">=0.28.0,<1.1.0"
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.43"
+version = "3.0.45"
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 [package.dependencies]
@@ -3012,18 +3474,18 @@ files = [
 
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+version = "2.7.2"
 description = "Data validation using Python type hints"
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 typing-extensions = ">=4.6.1"
 
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 [package.dependencies]
@@ -3137,17 +3599,16 @@ jupyter = ["ipython", "traitlets (>=5)"]
 
 [[package]]
 name = "pygments"
-version = "2.17.2"
+version = "2.18.0"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
@@ -3185,13 +3646,13 @@ hook-testing = ["execnet (>=1.5.0)", "psutil", "pytest (>=2.7.3)"]
 
 [[package]]
 name = "pyinstaller-hooks-contrib"
-version = "2024.4"
+version = "2024.6"
 description = "Community maintained hooks for PyInstaller"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3201,17 +3662,17 @@ setuptools = ">=42.0.0"
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.7.1"
+version = "10.8.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-markdown = ">=3.5"
+markdown = ">=3.6"
 pyyaml = "*"
 
 [package.extras]
@@ -3233,13 +3694,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "8.1.1"
+version = "8.2.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -3247,11 +3708,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
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 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.4,<2.0"
+pluggy = ">=1.5,<2.0"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
@@ -3304,18 +3765,18 @@ six = ">=1.5"
 
 [[package]]
 name = "python-gitlab"
-version = "4.4.0"
+version = "4.6.0"
 description = "A python wrapper for the GitLab API"
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 python-versions = ">=3.8.0"
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 [package.dependencies]
-requests = ">=2.25.0"
-requests-toolbelt = ">=0.10.1"
+requests = ">=2.32.0"
+requests-toolbelt = ">=1.0.0"
 
 [package.extras]
 autocompletion = ["argcomplete (>=1.10.0,<3)"]
@@ -3323,13 +3784,13 @@ yaml = ["PyYaml (>=6.0.1)"]
 
 [[package]]
 name = "python-semantic-release"
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+version = "9.8.0"
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 [package.dependencies]
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 tomlkit = ">=0.11,<1.0"
 
 [package.extras]
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 [[package]]
@@ -3462,99 +3924,99 @@ pyyaml = "*"
 
 [[package]]
 name = "pyzmq"
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 [[package]]
 name = "termcolor"
@@ -4637,24 +5117,24 @@ torch = ["torch (>=1.6.0)"]
 
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 [package.dependencies]
@@ -4662,7 +5142,7 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
+test = ["pytest", "ruff"]
 
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 name = "tomli"
@@ -4677,15 +5157,106 @@ files = [
 
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+python-versions = ">=3.8.0"
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@@ -4733,13 +5304,13 @@ testing = ["flaky (>=3.4.0)", "freezegun (>=0.3.11)", "pathlib2 (>=2.3.3)", "psu
 
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+    {file = "watchdog-4.0.1-py3-none-win32.whl", hash = "sha256:206afc3d964f9a233e6ad34618ec60b9837d0582b500b63687e34011e15bb429"},
+    {file = "watchdog-4.0.1-py3-none-win_amd64.whl", hash = "sha256:7577b3c43e5909623149f76b099ac49a1a01ca4e167d1785c76eb52fa585745a"},
+    {file = "watchdog-4.0.1-py3-none-win_ia64.whl", hash = "sha256:d7b9f5f3299e8dd230880b6c55504a1f69cf1e4316275d1b215ebdd8187ec88d"},
+    {file = "watchdog-4.0.1.tar.gz", hash = "sha256:eebaacf674fa25511e8867028d281e602ee6500045b57f43b08778082f7f8b44"},
 ]
 
 [package.extras]
@@ -4947,13 +5544,13 @@ files = [
 
 [[package]]
 name = "werkzeug"
-version = "3.0.2"
+version = "3.0.3"
 description = "The comprehensive WSGI web application library."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "werkzeug-3.0.2-py3-none-any.whl", hash = "sha256:3aac3f5da756f93030740bc235d3e09449efcf65f2f55e3602e1d851b8f48795"},
-    {file = "werkzeug-3.0.2.tar.gz", hash = "sha256:e39b645a6ac92822588e7b39a692e7828724ceae0b0d702ef96701f90e70128d"},
+    {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
+    {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
 ]
 
 [package.dependencies]
@@ -5057,18 +5654,18 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.18.1"
+version = "3.19.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+    {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"},
+    {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 database = ["sqlalchemy"]
@@ -5078,4 +5675,4 @@ notebook = ["ploomber-engine"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "50d8d656d7a7eb1ceb2939b83a60934f55c6d1b8aaa3b1517e8813ff2306e469"
+content-hash = "9c91eef8afc884316f34e94f7722de173f438406b20b89f4670ac6e580fffa1a"
diff --git a/pyproject.toml b/pyproject.toml
index 8e28360d..a068cdb8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,43 +25,12 @@ mlflow-skinny = { version = "*", optional = true }
 ploomber-engine = "^0.0.31"
 dill = "^0.3.8"
 
-[tool.poetry.group.docs.dependencies]
-mkdocs = "*"
-mkdocs-material = "*"
-mkdocs-section-index = "^0.3.5"
-mkdocstrings = { extras = ["python"], version = "^0.24.0" }
-nbconvert = "^7.13.1"
-mkdocs-click = "^0.8.1"
-tensorflow = "^2.16.1"
-
-[tool.poetry.group.binary.dependencies]
-pyinstaller = "^5.13.2"
-
-[tool.poetry.group.perf.dependencies]
-# Run the performace tests poetry run python -m pyflame -p ./flamegraph.pl runnable/entrypoints.py
-pyflame = "^0.3.1"
-
-
-[tool.poetry.group.tutorial.dependencies]
-pandas = "^2.2.1"
-numpy = "^1.26.4"
-scikit-learn = "^1.4.1.post1"
-en-core-web-sm = { url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1.tar.gz" }
-matplotlib = "^3.8.3"
-
-
-[tool.poetry.group.release.dependencies]
-python-semantic-release = "^9.4.2"
-
-
-[tool.poetry.group.examples.dependencies]
-pandas = "^2.2.2"
-
 [tool.poetry.extras]
 docker = ['docker']
 notebook = ['ploomber-engine']
 database = ["sqlalchemy"]
 
+
 [tool.poetry.group.dev.dependencies]
 pytest = "*"
 pytest-cov = "*"
@@ -73,13 +42,44 @@ ruff = "^0.0.259"
 commit-linter = "^1.0.2"
 black = "^23.3.0"
 gitlint = "^0.19.1"
-pandas = "^2.2.2"
+pandas = "^2.2.2" # for testing examples
 
+[tool.poetry.group.release.dependencies]
+python-semantic-release = "^9.4.2"
+
+[tool.poetry.group.docs.dependencies]
+mkdocs = "*"
+mkdocs-material = "*"
+mkdocs-section-index = "^0.3.5"
+mkdocstrings = { extras = ["python"], version = "^0.24.0" }
+nbconvert = "^7.13.1"
+mkdocs-click = "^0.8.1"
+
+[tool.poetry.group.tutorial.dependencies]
+pandas = "^2.2.1"
+numpy = "^1.26.4"
+scikit-learn = "^1.4.1.post1"
+en-core-web-sm = { url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1.tar.gz" }
+matplotlib = "^3.8.3"
+
+
+[tool.poetry.group.compare.dependencies]
+torch = "^2.3.0"
+torchvision = "^0.18.0"
+tensorflow = "^2.16.1"
 
 [tool.poetry.scripts]
 runnable = 'runnable.cli:cli'
 
 
+[tool.poetry.group.binary.dependencies]
+pyinstaller = "^5.13.2"
+
+[tool.poetry.group.perf.dependencies]
+# Run the performace tests poetry run python -m pyflame -p ./flamegraph.pl runnable/entrypoints.py
+pyflame = "^0.3.1"
+
+
 # Plugins for Executors
 [tool.poetry.plugins."executor"]
 "local" = "runnable.extensions.executor.local.implementation:LocalExecutor"

From 65bff88a092cee30149831dfd6b2ee89bc70f7fd Mon Sep 17 00:00:00 2001
From: Vijay Vammi <vijay.vammi@astrazeneca.com>
Date: Tue, 11 Jun 2024 09:31:55 +0100
Subject: [PATCH 3/4] docs: updating docs

---
 docs/assets/work_dark.png      | Bin 0 -> 161688 bytes
 docs/assets/work_light.png     | Bin 0 -> 160515 bytes
 docs/index.md                  |  32 ++++++++-
 examples/comparisons/README.md | 121 +++++++++++++++++++++++++++------
 4 files changed, 133 insertions(+), 20 deletions(-)
 create mode 100644 docs/assets/work_dark.png
 create mode 100644 docs/assets/work_light.png

diff --git a/docs/assets/work_dark.png b/docs/assets/work_dark.png
new file mode 100644
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diff --git a/docs/index.md b/docs/index.md
index bdb5d771..d7659f90 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -79,11 +79,41 @@ The difference between native driver and runnable orchestration:
 - [x] The pipeline is `runnable` in any environment.
 
 
-## But why runnable?
+## why runnable?
 
 Obviously, there are a lot of orchestration tools. A well maintained and curated [list is
 available here](https://github.com/EthicalML/awesome-production-machine-learning/).
 
+Broadly, they could be classed into ```native``` or ```meta``` orchestrators.
+
+<figure markdown>
+  ![Image title](assets/work_light.png#only-light){ width="600" height="300"}
+  ![Image title](assets/work_dark.png#only-dark){ width="600" height="300"}
+</figure>
+
+
+### __native orchestrators__
+
+- Focus on resource management, job scheduling, robustness and scalability.
+- Have less features on domain (data engineering, data science) activities.
+- Difficult to run locally.
+- Not ideal for quick experimentation or research activities.
+
+### __meta orchestrators__
+
+- An abstraction over native orchestrators.
+- Oriented towards domain (data engineering, data science) features.
+- Easy to get started and run locally.
+- Ideal for quick experimentation or research activities.
+
+```runnable``` is a _meta_ orchestrator with simple API, geared towards data engineering, data science activities.
+It works in conjunction with _native_ orchestrators and an alternative to [kedro](https://docs.kedro.org/en/stable/index.html)
+or [metaflow](https://metaflow.org/).
+
+
+
+
+
 ```runnable``` stands out based on these design principles.
 
 <div class="grid cards" markdown>
diff --git a/examples/comparisons/README.md b/examples/comparisons/README.md
index d0295f48..2c39671d 100644
--- a/examples/comparisons/README.md
+++ b/examples/comparisons/README.md
@@ -1,22 +1,20 @@
-In this section, we take the familiar MNIST problem and implement it in different orchestration frameworks.
-
-The [original source code](https://github.com/pytorch/examples/blob/main/mnist/main.py) is shown in [source.py](source.py)
-
-The individual directories are orchestration specific implementations.
-
-## Notes
-
 For the purpose of comparisons, consider the following function:
 
 ```python
 def func(x: int, y:pd.DataFrame):
+    # Access some data, input.csv
     # do something with the inputs.
-    # Write a file called data.csv for downstream steps.
+    # Write a file called output.csv for downstream steps.
     # return an output.
     return z
 ```
 
-It takes *inputs* x (integer) and y (a pandas dataframe or any other object), does some processing and writes a file to local disk and returns z (a simple datatype or object)
+It takes
+
+- *inputs* x (integer) and y (a pandas dataframe or any other object),
+- processes input data, input.csv expected on local file system
+- writes a file, output.csv to local filesystem
+- returns z (a simple datatype or object)
 
 The function in wrapped in runnable as:
 
@@ -24,14 +22,19 @@ The function in wrapped in runnable as:
 from somewhere import func
 from runnable import PythonTask, pickled, Catalog
 
-# instruction to move the file data.csv from local disk to a blob store
-catalog = Catalog(put=["data.csv"])
+# instruction to get input.csv from catalog at the start of the step.
+# and move output.csv to the catalog at the end of the step
+catalog = Catalog(get=["input.csv"], put=["output.csv"])
+
 # Call the function, func and expect it to return "z" while moving the files
 # It is expected that "x" and "y" are parameters set by some upstream step.
 # If the return parameter is an object, use pickled("z")
 func_task = PythonTask(name="function", function=func, returns=["z"], catalog=catalog)
 ```
 
+Below are the implementations in alternative frameworks. Note that
+the below are the best of our understanding of the frameworks, please let us
+know if there are alternate implementations.
 
 ### metaflow
 
@@ -55,12 +58,92 @@ class Flow(FlowSpec)
 
 - The API between ```runnable``` and ```metaflow``` are comparable.
 - There is a mechanism for functions to accept/return parameters.
+- Both support parallel branches, arbitrary nesting of pipelines.
+
+The differences:
+
+
+
+##### dependency management:
+
+```runnable``` depends on the activated virtualenv for dependencies which is natural to python.
+Use custom docker images to provide the same environment in cloud based executions.
+
+```metaflow``` uses decorators (conda, pypi) to specify dependencies. This has an advantage
+of abstraction from docker ecosystem for the user.
+
+##### dataflow:
+
+In ```runnable```, data flow between steps is by an instruction in runnable to ```glob``` files in
+local disk and present them in the same structure to downstream steps.
+
+```metaflow``` needs a code based instruction to do so.
+
+##### notebooks:
+
+```runnable``` allows notebook as tasks. Notebooks can take JSON style inputs and can return
+pythonic objects for downstream steps.
+
+```metaflow``` does not support notebooks as tasks.
+
+##### infrastructure:
+
+```runnable```, in many ways, is just a transpiler to your chosen infrastructure.
+
+```metaflow``` is a platform with its own specified infrastructure.
+
+##### modular pipelines
+
+In ```runnable``` the individual pipelines of parallel and map states are
+pipelines themselves and can run in isolation. This is not true in ```metaflow```.
+
+##### unit testing pipelines
+
+```runnable``` pipelines are testable using ```mocked``` executor where the executables can be mocked/patched. In ```metaflow```, it depends on how the
+python function is wrapped in the pipeline.
+
+
+### kedro
+
+The function in ```kedro``` implementation would roughly be:
+
+Note that any movement of files should happen via data catalog.
+
+```python
+from kedro.pipeline import Pipeline, node, pipeline
+from somewhere import func
+
+def create_pipeline(**kwargs) -> Pipeline:
+    return pipeline(
+        [
+            node(
+                func=func,
+                inputs=["params:x", "y"],
+                outputs=["z"],
+                name="my_function",
+            ),
+            ...
+        ]
+    )
+
+```
+
+##### Structure
+
+Kedro needs a structure and configuration to set up a new project and provides
+a CLI to get started.
+
+To use ```runnable``` as part of the project requires
+adding a pipeline definition file (in python or yaml) and an optional configuration file.
+
+##### dataflow
+
+Kedro needs the data flowing through the pipeline via catalog.yaml which
+provides a central place to understand the data.
+
+In ```runnable```, the data is presented to the individual tasks as
+requested by the ```catalog``` instruction.
 
-The differences between both:
+##### notebooks
 
-- dependency management in ```runnable``` is expected to be user driven and "pythonic" (venv, poetry etc) while metaflow packages provides a per-step (or flow level) libraries.
-- data flow between steps is by an instruction in runnable to ```glob``` files in
-local disk and present them in the same structure to downstream steps. ```metaflow``` needs this to be via code.
-- runnable allows ```notebooks``` to be a task, allowing simple data types to be
-parameters while collecting objects from the notebook execution.
-- metaflow has a *platform* side to it and comes up with prescribed infrastructure while runnable is, in many ways, a transpiler to your chosen infrastructure.
+Kedro supports notebooks for exploration but not as tasks of the pipeline.

From d13394d066795867b0038b4fa2e93ce51f724448 Mon Sep 17 00:00:00 2001
From: Vijay Vammi <vijay.vammi@astrazeneca.com>
Date: Tue, 11 Jun 2024 09:38:10 +0100
Subject: [PATCH 4/4] docs: updating docs

---
 README.md | 461 ++++++++++++++++--------------------------------------
 1 file changed, 139 insertions(+), 322 deletions(-)

diff --git a/README.md b/README.md
index 707c0377..f768c278 100644
--- a/README.md
+++ b/README.md
@@ -2,25 +2,6 @@
 
 
 
-<p align="center">
-
-                                                      ,////,
-                                                      /// 6|
-                                                      //  _|
-                                                    _/_,-'
-                                                _.-/'/   \   ,/;,
-                                            ,-' /'  \_   \ / _/
-                                            `\ /     _/\  ` /
-                                               |     /,  `\_/
-                                               |     \'
-                                    /\_        /`      /\
-                                  /' /_``--.__/\  `,. /  \
-                                  |_/`  `-._     `\/  `\   `.
-                                            `-.__/'     `\   |
-                                                        `\  \
-                                                          `\ \
-                                                            \_\__
-                                                              \___)
 
 </p>
 <hr style="border:2px dotted orange">
@@ -32,364 +13,200 @@
 <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
 <a href="https://github.com/python/mypy"><img alt="MyPy Checked" src="https://www.mypy-lang.org/static/mypy_badge.svg"></a>
 <a href="https://github.com/vijayvammi/runnable/actions/workflows/release.yaml"><img alt="Tests:" src="https://github.com/vijayvammi/runnable/actions/workflows/release.yaml/badge.svg">
-<a href="https://github.com/vijayvammi/runnable/actions/workflows/docs.yaml"><img alt="Docs:" src="https://github.com/vijayvammi/runnable/actions/workflows/docs.yaml/badge.svg">
 </p>
 <hr style="border:2px dotted orange">
 
-runnable is a simplified workflow definition language that helps in:
-
-- **Streamlined Design Process:** runnable enables users to efficiently plan their pipelines with
-[stubbed nodes](https://astrazeneca.github.io/runnable-core/concepts/stub), along with offering support for various structures such as
-[tasks](https://astrazeneca.github.io/runnable-core/concepts/task), [parallel branches](https://astrazeneca.github.io/runnable-core/concepts/parallel), and [loops or map branches](https://astrazeneca.github.io/runnable-core/concepts/map)
-in both [yaml](https://astrazeneca.github.io/runnable-core/concepts/pipeline) or a [python SDK](https://astrazeneca.github.io/runnable-core/sdk) for maximum flexibility.
-
-- **Incremental Development:** Build your pipeline piece by piece with runnable, which allows for the
-implementation of tasks as [python functions](https://astrazeneca.github.io/runnable-core/concepts/task/#python_functions),
-[notebooks](https://astrazeneca.github.io/runnable-core/concepts/task/#notebooks), or [shell scripts](https://astrazeneca.github.io/runnable-core/concepts/task/#shell),
-adapting to the developer's preferred tools and methods.
 
-- **Robust Testing:** Ensure your pipeline performs as expected with the ability to test using sampled data. runnable
-also provides the capability to [mock and patch tasks](https://astrazeneca.github.io/runnable-core/configurations/executors/mocked)
-for thorough evaluation before full-scale deployment.
+[Please check here for complete documentation](https://astrazeneca.github.io/runnable/)
 
-- **Seamless Deployment:** Transition from the development stage to production with ease.
-runnable simplifies the process by requiring [only configuration changes](https://astrazeneca.github.io/runnable-core/configurations/overview)
-to adapt to different environments, including support for [argo workflows](https://astrazeneca.github.io/runnable-core/configurations/executors/argo).
-
-- **Efficient Debugging:** Quickly identify and resolve issues in pipeline execution with runnable's local
-debugging features. Retrieve data from failed tasks and [retry failures](https://astrazeneca.github.io/runnable-core/concepts/run-log/#retrying_failures)
-using your chosen debugging tools to maintain a smooth development experience.
-
-Along with the developer friendly features, runnable also acts as an interface to production grade concepts
-such as [data catalog](https://astrazeneca.github.io/runnable-core/concepts/catalog), [reproducibility](https://astrazeneca.github.io/runnable-core/concepts/run-log),
-[experiment tracking](https://astrazeneca.github.io/runnable-core/concepts/experiment-tracking)
-and secure [access to secrets](https://astrazeneca.github.io/runnable-core/concepts/secrets).
-
-<hr style="border:2px dotted orange">
-
-## What does it do?
+## Example
 
+The below data science flavored code is a well-known
+[iris example from scikit-learn](https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html).
 
-![works](assets/work.png)
 
-<hr style="border:2px dotted orange">
+```python
+"""
+Example of Logistic regression using scikit-learn
+https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import datasets
+from sklearn.inspection import DecisionBoundaryDisplay
+from sklearn.linear_model import LogisticRegression
+
+
+def load_data():
+    # import some data to play with
+    iris = datasets.load_iris()
+    X = iris.data[:, :2]  # we only take the first two features.
+    Y = iris.target
+
+    return X, Y
+
+
+def model_fit(X: np.ndarray, Y: np.ndarray, C: float = 1e5):
+    logreg = LogisticRegression(C=C)
+    logreg.fit(X, Y)
+
+    return logreg
+
+
+def generate_plots(X: np.ndarray, Y: np.ndarray, logreg: LogisticRegression):
+    _, ax = plt.subplots(figsize=(4, 3))
+    DecisionBoundaryDisplay.from_estimator(
+        logreg,
+        X,
+        cmap=plt.cm.Paired,
+        ax=ax,
+        response_method="predict",
+        plot_method="pcolormesh",
+        shading="auto",
+        xlabel="Sepal length",
+        ylabel="Sepal width",
+        eps=0.5,
+    )
 
-## Documentation
+    # Plot also the training points
+    plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)
 
-[More details about the project and how to use it available here](https://astrazeneca.github.io/runnable-core/).
+    plt.xticks(())
+    plt.yticks(())
 
-<hr style="border:2px dotted orange">
+    plt.savefig("iris_logistic.png")
 
-## Installation
+    # TODO: What is the right value?
+    return 0.6
 
-The minimum python version that runnable supports is 3.8
 
-```shell
-pip install runnable
-```
+## Without any orchestration
+def main():
+    X, Y = load_data()
+    logreg = model_fit(X, Y, C=1.0)
+    generate_plots(X, Y, logreg)
 
-Please look at the [installation guide](https://astrazeneca.github.io/runnable-core/usage)
-for more information.
 
-<hr style="border:2px dotted orange">
+## With runnable orchestration
+def runnable_pipeline():
+    # The below code can be anywhere
+    from runnable import Catalog, Pipeline, PythonTask, metric, pickled
 
-## Example
+    # X, Y = load_data()
+    load_data_task = PythonTask(
+        function=load_data,
+        name="load_data",
+        returns=[pickled("X"), pickled("Y")],  # (1)
+    )
 
-Your application code. Use pydantic models as DTO.
+    # logreg = model_fit(X, Y, C=1.0)
+    model_fit_task = PythonTask(
+        function=model_fit,
+        name="model_fit",
+        returns=[pickled("logreg")],
+    )
 
-Assumed to be present at ```functions.py```
-```python
-from pydantic import BaseModel
+    # generate_plots(X, Y, logreg)
+    generate_plots_task = PythonTask(
+        function=generate_plots,
+        name="generate_plots",
+        terminate_with_success=True,
+        catalog=Catalog(put=["iris_logistic.png"]),  # (2)
+        returns=[metric("score")],
+    )
 
-class InnerModel(BaseModel):
-    """
-    A pydantic model representing a group of related parameters.
-    """
+    pipeline = Pipeline(
+        steps=[load_data_task, model_fit_task, generate_plots_task],
+    )  # (4)
 
-    foo: int
-    bar: str
+    pipeline.execute()
 
+    return pipeline
 
-class Parameter(BaseModel):
-    """
-    A pydantic model representing the parameters of the whole pipeline.
-    """
 
-    x: int
-    y: InnerModel
+if __name__ == "__main__":
+    # main()
+    runnable_pipeline()
 
+```
 
-def return_parameter() -> Parameter:
-    """
-    The annotation of the return type of the function is not mandatory
-    but it is a good practice.
 
-    Returns:
-        Parameter: The parameters that should be used in downstream steps.
-    """
-    # Return type of a function should be a pydantic model
-    return Parameter(x=1, y=InnerModel(foo=10, bar="hello world"))
+1. Return two serialized objects X and Y.
+2. Store the file `iris_logistic.png` for future reference.
+3. Define the sequence of tasks.
+4. Define a pipeline with the tasks
 
+The difference between native driver and runnable orchestration:
 
-def display_parameter(x: int, y: InnerModel):
-    """
-    Annotating the arguments of the function is important for
-    runnable to understand the type of parameters you want.
+!!! tip inline end "Notebooks and Shell scripts"
 
-    Input args can be a pydantic model or the individual attributes.
-    """
-    print(x)
-    # >>> prints 1
-    print(y)
-    # >>> prints InnerModel(foo=10, bar="hello world")
-```
+    You can execute notebooks and shell scripts too!!
 
-### Application code using driver functions.
+    They can be written just as you would want them, *plain old notebooks and scripts*.
 
-The code is runnable without any orchestration framework.
 
-```python
-from functions import return_parameter, display_parameter
 
-my_param = return_parameter()
-display_parameter(my_param.x, my_param.y)
-```
 
-### Orchestration using runnable
+<div class="annotate" markdown>
 
-<table>
-<tr>
-    <th>python SDK</th>
-    <th>yaml</th>
-</tr>
-<tr>
-<td valign="top"><p>
+```diff
 
-Example present at: ```examples/python-tasks.py```
+- X, Y = load_data()
++load_data_task = PythonTask(
++    function=load_data,
++     name="load_data",
++     returns=[pickled("X"), pickled("Y")], (1)
++    )
 
-Run it as: ```python examples/python-tasks.py```
+-logreg = model_fit(X, Y, C=1.0)
++model_fit_task = PythonTask(
++   function=model_fit,
++   name="model_fit",
++   returns=[pickled("logreg")],
++   )
 
-```python
-from runnable import Pipeline, Task
+-generate_plots(X, Y, logreg)
++generate_plots_task = PythonTask(
++   function=generate_plots,
++   name="generate_plots",
++   terminate_with_success=True,
++   catalog=Catalog(put=["iris_logistic.png"]), (2)
++   )
 
-def main():
-    step1 = Task(
-        name="step1",
-        command="examples.functions.return_parameter",
-    )
-    step2 = Task(
-        name="step2",
-        command="examples.functions.display_parameter",
-        terminate_with_success=True,
-    )
 
-    step1 >> step2
++pipeline = Pipeline(
++   steps=[load_data_task, model_fit_task, generate_plots_task], (3)
 
-    pipeline = Pipeline(
-        start_at=step1,
-        steps=[step1, step2],
-        add_terminal_nodes=True,
-    )
+```
+</div>
 
-    pipeline.execute()
 
+---
 
-if __name__ == "__main__":
-    main()
-```
+- [x] ```Domain``` code remains completely independent of ```driver``` code.
+- [x] The ```driver``` function has an equivalent and intuitive runnable expression
+- [x] Reproducible by default, runnable stores metadata about code/data/config for every execution.
+- [x] The pipeline is `runnable` in any environment.
 
-</p></td>
-
-<td valign="top"><p>
-
-Example present at: ```examples/python-tasks.yaml```
-
-
-Execute via the cli: ```runnable execute -f examples/python-tasks.yaml```
-
-```yaml
-dag:
-  description: |
-    This is a simple pipeline that does 3 steps in sequence.
-    In this example:
-      1. First step: returns a "parameter" x as a Pydantic model
-      2. Second step: Consumes that parameter and prints it
-
-    This pipeline demonstrates one way to pass small data from one step to another.
-
-  start_at: step 1
-  steps:
-    step 1:
-      type: task
-      command_type: python # (2)
-      command: examples.functions.return_parameter # (1)
-      next: step 2
-    step 2:
-      type: task
-      command_type: python
-      command: examples.functions.display_parameter
-      next: success
-    success:
-      type: success
-    fail:
-      type: fail
-```
 
-</p></td>
+## Documentation
 
-</tr>
-</table>
+[More details about the project and how to use it available here](https://astrazeneca.github.io/runnable/).
 
-### Transpile to argo workflows
+<hr style="border:2px dotted orange">
 
-No code change, just change the configuration.
+## Installation
 
-```yaml
-executor:
-  type: "argo"
-  config:
-    image: runnable:demo
-    persistent_volumes:
-      - name: runnable-volume
-        mount_path: /mnt
+The minimum python version that runnable supports is 3.8
 
-run_log_store:
-  type: file-system
-  config:
-    log_folder: /mnt/run_log_store
+```shell
+pip install runnable
 ```
 
-More details can be found in [argo configuration](https://astrazeneca.github.io/runnable-core/configurations/executors/argo).
-
-Execute the code as ```runnable execute -f examples/python-tasks.yaml -c examples/configs/argo-config.yam```
-
-<details>
-  <summary>Expand</summary>
-
-```yaml
-apiVersion: argoproj.io/v1alpha1
-kind: Workflow
-metadata:
-  generateName: runnable-dag-
-  annotations: {}
-  labels: {}
-spec:
-  activeDeadlineSeconds: 172800
-  entrypoint: runnable-dag
-  podGC:
-    strategy: OnPodCompletion
-  retryStrategy:
-    limit: '0'
-    retryPolicy: Always
-    backoff:
-      duration: '120'
-      factor: 2
-      maxDuration: '3600'
-  serviceAccountName: default-editor
-  templates:
-    - name: runnable-dag
-      failFast: true
-      dag:
-        tasks:
-          - name: step-1-task-uvdp7h
-            template: step-1-task-uvdp7h
-            depends: ''
-          - name: step-2-task-772vg3
-            template: step-2-task-772vg3
-            depends: step-1-task-uvdp7h.Succeeded
-          - name: success-success-igzq2e
-            template: success-success-igzq2e
-            depends: step-2-task-772vg3.Succeeded
-    - name: step-1-task-uvdp7h
-      container:
-        image: runnable:demo
-        command:
-          - runnable
-          - execute_single_node
-          - '{{workflow.parameters.run_id}}'
-          - step%1
-          - --log-level
-          - WARNING
-          - --file
-          - examples/python-tasks.yaml
-          - --config-file
-          - examples/configs/argo-config.yaml
-        volumeMounts:
-          - name: executor-0
-            mountPath: /mnt
-        imagePullPolicy: ''
-        resources:
-          limits:
-            memory: 1Gi
-            cpu: 250m
-          requests:
-            memory: 1Gi
-            cpu: 250m
-    - name: step-2-task-772vg3
-      container:
-        image: runnable:demo
-        command:
-          - runnable
-          - execute_single_node
-          - '{{workflow.parameters.run_id}}'
-          - step%2
-          - --log-level
-          - WARNING
-          - --file
-          - examples/python-tasks.yaml
-          - --config-file
-          - examples/configs/argo-config.yaml
-        volumeMounts:
-          - name: executor-0
-            mountPath: /mnt
-        imagePullPolicy: ''
-        resources:
-          limits:
-            memory: 1Gi
-            cpu: 250m
-          requests:
-            memory: 1Gi
-            cpu: 250m
-    - name: success-success-igzq2e
-      container:
-        image: runnable:demo
-        command:
-          - runnable
-          - execute_single_node
-          - '{{workflow.parameters.run_id}}'
-          - success
-          - --log-level
-          - WARNING
-          - --file
-          - examples/python-tasks.yaml
-          - --config-file
-          - examples/configs/argo-config.yaml
-        volumeMounts:
-          - name: executor-0
-            mountPath: /mnt
-        imagePullPolicy: ''
-        resources:
-          limits:
-            memory: 1Gi
-            cpu: 250m
-          requests:
-            memory: 1Gi
-            cpu: 250m
-  templateDefaults:
-    activeDeadlineSeconds: 7200
-    timeout: 10800s
-  arguments:
-    parameters:
-      - name: run_id
-        value: '{{workflow.uid}}'
-  volumes:
-    - name: executor-0
-      persistentVolumeClaim:
-        claimName: runnable-volume
-
-```
+Please look at the [installation guide](https://astrazeneca.github.io/runnable-core/usage)
+for more information.
 
-</details>
 
 ## Pipelines can be: