Update docs [ci skip]

website/docs/usage/layers-architectures.md

@@ -646,7 +646,9 @@ get_candidates = model.attrs["get_candidates"]
 
 To use our new relation extraction model as part of a custom
 [trainable component](/usage/processing-pipelines#trainable-components), we
-create a subclass of [`Pipe`](/api/pipe) that holds the model:
+create a subclass of [`Pipe`](/api/pipe) that holds the model.
+
+![Illustration of Pipe methods](../images/trainable_component.svg)
 
 ```python
 ### Pipeline component skeleton
@@ -826,7 +828,7 @@ def __call__(self, Doc doc):
 
 Once our `Pipe` subclass is fully implemented, we can
 [register](/usage/processing-pipelines#custom-components-factories) the
-component with the [`@Language.factory`](/api/lnguage#factory) decorator. This
+component with the [`@Language.factory`](/api/language#factory) decorator. This
 assigns it a name and lets you create the component with
 [`nlp.add_pipe`](/api/language#add_pipe) and via the
 [config](/usage/training#config).

website/docs/usage/processing-pipelines.md

@@ -1172,13 +1172,15 @@ doc = nlp("This is a text...")
 spaCy's [`Pipe`](/api/pipe) class helps you implement your own trainable
 components that have their own model instance, make predictions over `Doc`
 objects and can be updated using [`spacy train`](/api/cli#train). This lets you
-plug fully custom machine learning components into your pipeline. You'll need
+plug fully custom machine learning components into your pipeline.
-the following:
+
+![Illustration of Pipe methods](../images/trainable_component.svg)
+
+You'll need the following:
 
 1. **Model:** A Thinc [`Model`](https://thinc.ai/docs/api-model) instance. This
-   can be a model implemented in
+   can be a model implemented in [Thinc](/usage/layers-architectures#thinc), or
-   [Thinc](/usage/layers-architectures#thinc), or a
+   a [wrapped model](/usage/layers-architectures#frameworks) implemented in
-   [wrapped model](/usage/layers-architectures#frameworks) implemented in
    PyTorch, TensorFlow, MXNet or a fully custom solution. The model must take a
    list of [`Doc`](/api/doc) objects as input and can have any type of output.
 2. **Pipe subclass:** A subclass of [`Pipe`](/api/pipe) that implements at least
@@ -1283,7 +1285,7 @@ loss is calculated and to add evaluation scores to the training output.
 For more details on how to implement your own trainable components and model
 architectures, and plug existing models implemented in PyTorch or TensorFlow
 into your spaCy pipeline, see the usage guide on
-[layers and model architectures](/usage/layers-architectures).
+[layers and model architectures](/usage/layers-architectures#components).
 
 </Infobox>
 

website/docs/usage/spacy-101.md

@@ -404,8 +404,73 @@ import Training101 from 'usage/101/\_training.md'
 <Infobox title="Training pipelines and models" emoji="📖">
 
 To learn more about **training and updating** pipelines, how to create training
-data and how to improve spaCy's named entity recognition models, see the usage
+data and how to improve spaCy's named models, see the usage guides on
-guides on [training](/usage/training).
+[training](/usage/training).
+
+</Infobox>
+
+### Training config and lifecycle {#training-config}
+
+Training config files include all **settings and hyperparameters** for training
+your pipeline. Instead of providing lots of arguments on the command line, you
+only need to pass your `config.cfg` file to [`spacy train`](/api/cli#train).
+This also makes it easy to integrate custom models and architectures, written in
+your framework of choice. A pipeline's `config.cfg` is considered the "single
+source of truth", both at **training** and **runtime**.
+
+> ```ini
+> ### config.cfg (excerpt)
+> [training]
+> accumulate_gradient = 3
+>
+> [training.optimizer]
+> @optimizers = "Adam.v1"
+>
+> [training.optimizer.learn_rate]
+> @schedules = "warmup_linear.v1"
+> warmup_steps = 250
+> total_steps = 20000
+> initial_rate = 0.01
+> ```
+
+![Illustration of pipeline lifecycle](../images/lifecycle.svg)
+
+<Infobox title="Training configuration system" emoji="📖">
+
+For more details on spaCy's **configuration system** and how to use it to
+customize your pipeline components, component models, training settings and
+hyperparameters, see the [training config](/usage/training#config) usage guide.
+
+</Infobox>
+
+### Trainable components {#training-components}
+
+spaCy's [`Pipe`](/api/pipe) class helps you implement your own trainable
+components that have their own model instance, make predictions over `Doc`
+objects and can be updated using [`spacy train`](/api/cli#train). This lets you
+plug fully custom machine learning components into your pipeline that can be
+configured via a single training config.
+
+> #### config.cfg (excerpt)
+>
+> ```ini
+> [components.my_component]
+> factory = "my_component"
+>
+> [components.my_component.model]
+> @architectures = "my_model.v1"
+> width = 128
+> ```
+
+![Illustration of Pipe methods](../images/trainable_component.svg)
+
+<Infobox title="Custom trainable components" emoji="📖">
+
+To learn more about how to implement your own **model architectures** and use
+them to power custom **trainable components**, see the usage guides on the
+[trainable component API](/usage/processing-pipelines#trainable-components) and
+implementing [layers and architectures](/usage/layers-architectures#components)
+for trainable components.
 
 </Infobox>