adding advanced usage docs for custom handlers

Author: isabelizimm

docs/source/advancedusage/custom_handler.md

@@ -0,0 +1,41 @@
+# Custom Handlers
+
+There are two different ways that vetiver supports flexible handling for models that do not work automatically with the vetiver framework. The first way is with [new model types,](#new-model-type) where there is no current implementation for the type of model you would like to deploy. The second way is when you would like to implement a current handler, but [in a different way](#different-model-implementation). In either case, you *must* make a custom handler from the base `VetiverHandler`. A minimal custom handler could look like the following:
+
+```python
+from vetiver.handlers.base import VetiverHandler
+
+class SampleCustomHandler(VetiverHandler):
+    def __init__(model, ptype_data):
+        super().__init__(model, ptype_data)
+    
+    def handler_predict(self, input_data, check_ptype):
+        """
+        handler_predict defines how to make predictions from your model
+        """
+        # your code here
+```
+
+## New model type
+If your model type is not supported by vetiver, you should create and then register the handler using [single dispatch](https://docs.python.org/3/library/functools.html#functools.singledispatch). Once the new type is registered, you are able to use `VetiverModel()` as normal. Here is a template for such a function: 
+
+```python    
+from vetiver.handlers._interface import create_handler
+
+@create_handler.register
+def _(model: {_model_type}, ptype_data):
+    return SampleCustomHandler(model, ptype_data)
+
+VetiverModel(your_model, "your_model")
+```
+
+If your datatype is a common type, please consider submitting a pull request.
+
+## Different model implementation
+If your model's prediction function is different than vetiver's, you should create a custom handler with a `handler_predict` method to make predictions. Then, initialize your handler with your model, and pass the object into `VetiverModel`.
+
+```python    
+new_model = SampleCustomHandler(your_model, your_ptype_data)
+
+VetiverModel(new_model, "your_model")
+```

docs/source/conf.py

@@ -10,9 +10,10 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
+import os
+import sys
+sys.path.insert(0, os.path.abspath('.'))
+from vetiver import __version__
 
 
 # -- Project information -----------------------------------------------------
@@ -22,7 +23,7 @@
 author = "Isabel Zimmerman"
 
 # The full version, including alpha/beta/rc tags
-release = "0.1.3"
+release = __version__
 
 
 # -- General configuration ---------------------------------------------------
@@ -64,6 +65,13 @@
 
 }
 
+source_suffix = {
+    '.rst': 'restructuredtext',
+    '.txt': 'markdown',
+    '.md': 'markdown',
+}
+myst_heading_anchors = 2
+
 html_logo = "../figures/logo.png"
 html_favicon = "../figures/logo.png"
 

docs/source/index.rst

@@ -42,8 +42,6 @@ Version
 Deploy
 ==================
 
-.. currentmodule:: vetiver
-
 .. autosummary::
    :toctree: reference/
    :caption: Deploy
@@ -56,3 +54,10 @@ Deploy
    ~load_pkgs
    ~vetiver_write_app
    ~vetiver_write_docker
+
+Advanced Usage
+==================
+.. toctree::
+   advancedusage/custom_handler.md
+   :caption: Advanced Usage
+   

vetiver/vetiver_model.py

@@ -18,10 +18,10 @@ def __init__(
 class VetiverModel:
     """Create VetiverModel class for serving.
 
-    Attributes
+    Parameters
     ----------
     model :
-        A trained model, such as an sklearn or spacy model
+        A trained model, such as an sklearn or torch model
     name : string
         Model name or ID
     ptype_data : pd.DataFrame, np.array
@@ -32,6 +32,19 @@ class VetiverModel:
         A detailed description of the model. if omitted, a brief description will be generated
     metadata : dict
         Other details to be saved and accessed for serving
+
+    Attributes
+    ----------    
+    ptype : pydantic.main.BaseModel
+        Data prototype
+    handler_predict:
+        Method to make predictions from a trained model
+    
+    Notes
+    -----
+    VetiverModel can also take an initialized custom VetiverHandler
+    as a model, for advanced use cases or non-supported model types.
+    
     """
 
     def __init__(