fix docs (#58)

* fix links

* test

* setup

docs/source/conf.py

@@ -147,3 +147,11 @@ def _package_list_from_file(pfile):
 MOCK_PACKAGES = [PACKAGE_MAPPING.get(pkg, pkg) for pkg in MOCK_PACKAGES]
 
 autodoc_mock_imports = MOCK_PACKAGES
+
+# only run doctests marked with a ".. doctest::" directive
+doctest_test_doctest_blocks = ''
+doctest_global_setup = """
+import torch
+import pytorch_lightning as pl
+import flash
+"""

docs/source/custom_task.rst

@@ -4,7 +4,7 @@ Tutorial: Creating a Custom Task
 In this tutorial we will go over the process of creating a custom task,
 along with a custom data module.
 
-.. code:: python
+.. testcode:: python
 
     import flash
 
@@ -21,7 +21,7 @@ Here we create a basic linear regression task by subclassing
 ``flash.Task``. For the majority of tasks, you will likely only need to
 override the ``__init__`` and ``forward`` methods.
 
-.. code:: python
+.. testcode::
 
     class LinearRegression(flash.Task):
         def __init__(self, num_inputs, learning_rate=0.001, metrics=None):
@@ -67,7 +67,7 @@ for the prediction of diabetes disease progression. We can create this
 ``DataModule`` below, wrapping the scikit-learn `Diabetes
 dataset <https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset>`__.
 
-.. code:: python
+.. testcode::
 
     class DiabetesPipeline(flash.core.data.TaskDataPipeline):
         def after_uncollate(self, samples):

docs/source/installation.md

@@ -5,13 +5,11 @@ Flash is tested on Python 3.6+, and PyTorch 1.6
 ## Install with pip/conda
 
 ```bash
-pip install lightning-flash
+pip install lightning-flash -U
 ```
 
 ## Install from source
 
 ``` bash
-git clone https://github.com/PyTorchLightning/flash.git
-cd flash
-pip install -e .
+pip install git+https://github.com/PyTorchLightning/lightning-flash.git
 ```

docs/source/quickstart.rst

@@ -45,7 +45,9 @@ organized PyTorch with the unecessary engineering details abstracted away.
 
 When you need more flexibility you can build your own tasks or simply use Lightning directly.
 
-.. note:: Read :doc:`here <reference/flash_to_pl>` to understand when to use Flash vs Lightning.
+.. tip::
+
+    Read :doc:`here <reference/flash_to_pl>` to understand when to use Flash vs Lightning.
 
 ----
 
@@ -56,7 +58,7 @@ You can install flash using pip or conda:
 
 .. code-block:: bash
 
-    pip install pytorch-lightning-flash -U -pre
+    pip install pytorch-lightning-flash -U
 
 ------
 

docs/source/reference/image_classification.rst

@@ -124,7 +124,9 @@ Changing the backbone
 *********************
 By default, we use a `ResNet-18 <https://arxiv.org/abs/1512.03385>`_ for image classification. You can change the model run by the task by passing in a different backbone.
 
-.. note:: When changing the backbone, make sure you pass in the same backbone to the Task and the Data object!
+.. note::
+
+    When changing the backbone, make sure you pass in the same backbone to the Task and the Data object!
 
 .. code-block:: python
 

docs/source/reference/image_embedder.rst

@@ -36,11 +36,11 @@ Or on a random image tensor
 
 .. code-block:: python
 
-  # 2. Perform inference on an image file
-  import torch
-  images = torch.rand(32, 3, 224, 224)
-  embeddings = model.predict(images)
-  print(predictions)
+    # 2. Perform inference on an image file
+    import torch
+    images = torch.rand(32, 3, 224, 224)
+    embeddings = model.predict(images)
+    print(predictions)
 
 For more advanced inference options, see :ref:`predictions`.
 

docs/source/reference/tabular_classification.rst

@@ -39,12 +39,10 @@ We can create :class:`~flash.tabular.TabularData` from csv files using the :func
 * **numerical_input**- a list of the names of columns that contain numerical continuous data (floats)
 * **target**- the name of the column we want to predict
 
-.. tip:: you can pass in val_size and test_size to partition your training data into a separate validation and test set like so:
-
 
 Next, we create the :class:`~flash.tabular.TabularClassifier` task, using the Data module we created.
 
-.. code-block::
+.. code-block:: python
 
     import flash
     from flash import download_data