add contributing guide

docs/guides/contributing.md

@@ -0,0 +1,54 @@
+# Contributing
+This is a shot guide on how to start contibuting to Elegy along with some best practices for the project.
+
+## Setup
+We use `poetry` so the easiest way to setup a development environment is run
+
+```bash
+poetry install
+```
+
+## Creating Losses and Metrics
+For this you can follow these guidelines:
+
+* Each loss / metric should be defined in its own file.
+* Inherit from either `elegy.losses.loss.Loss` or `elegy.metrics.metric.Metric` or an existing class that inherits from them.
+* Try to use an existing metric or loss as a template
+* You must provide documentation for the following:
+  * The class definition.
+  * The `__init__` method.
+  * The `call` method.
+* Try to port the documentation + signature from its Keras counter part.
+  * If so you must give credits to the original source file.
+* You must include tests.
+  * If you there exists an equivalent loss/metric in Keras you must test numerical equivalence between both.
+
+## Testing
+To execute all the tests just run
+```bash
+pytest
+```
+
+## Documentation
+We use `mkdocs`. If you create a new object that requires documentation please do the following:
+
+* Add a markdown file inside `/docs/api` in the appropriate location according to the project's structure. This file must:
+  * Contain the path of function / class as header
+  * Use `mkdocstring` to render the API information.
+  * Example:
+```markdown
+# elegy.losses.BinaryCrossentropy
+
+::: elegy.losses.BinaryCrossentropy
+    selection:
+        inherited_members: true
+        members:
+            - call
+            - __init__
+```
+* Add and entry to `mkdocs.yml` inside `nav` pointing to this file. Checkout `mkdocs.yml`.
+
+To build and visualize the documentation locally run
+```bash
+mkdocs serve
+```

docs/index.md

@@ -24,6 +24,8 @@ Install Elegy using pip:
 pip install elegy
 ```
 
+For Windows users we recomend the Windows subsystem for linux 2 [WSL2](https://docs.microsoft.com/es-es/windows/wsl/install-win10?redirectedfrom=MSDN) since [jax](https://github.com/google/jax/issues/438) does not have support for it yet.
+
 ## Quick Start
 Elegy greatly simplifies the training of Deep Learning models compared to pure Jax / Haiku where, due to Jax functional nature, users have to do a lot of book keeping around the state of the model. In Elegy just you just have to follow 3 basic steps:
 
@@ -39,7 +41,7 @@ class MLP(elegy.Module):
         ])
         return mlp(image)
 ```
-**2.** Create a `Model` from this module and specify additional things like losses, metrics, optimizers, and callbacks:
+**2.** Create a `Model` from this module and specify additional things like losses, metrics, and optimizers:
 ```python
 model = elegy.Model(
     module=MLP.defer(),
@@ -61,6 +63,7 @@ model.fit(
     batch_size=64,
     validation_data=(X_test, y_test),
     shuffle=True,
+    callbacks=[elegy.callbacks.TensorBoard("summaries")]
 )
 ```
 

mkdocs.yml

@@ -15,6 +15,7 @@ nav:
   - Getting Started: getting-started.ipynb
   - Guides:
       Modules, Losses, and Metrics: guides/modules-losses-metrics.md
+      Contibuting: guides/contributing.md
   - API Reference:
       Model: api/Model.md
       # Module: api/Module.md