docs: convert downloads to links

determined-ai · Apr 17, 2020 · 13c8792 · 13c8792
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diff --git a/docs/examples.txt b/docs/examples.txt
@@ -16,13 +16,13 @@ model definition and one or more experiment configurations.
   * - Framework
     - Dataset
   * - PyTorch
-    - :download:`MNIST </examples/mnist_pytorch.tgz>`
+    - `MNIST <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_pytorch>`_
   * - PyTorch
-    - :download:`CIFAR-10 CNN </examples/cifar10_cnn_pytorch.tgz>`
+    - `CIFAR-10 <https://github.com/determined-ai/determined/tree/master/examples/officialcifar10_cnn_pytorch>`_
   * - TensorFlow (Estimator API)
-    - :download:`MNIST </examples/mnist_estimator.tgz>`
+    - `Fashion MNIST <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_estimator>`_
   * - TensorFlow (tf.keras)
-    - :download:`CIFAR-10 CNN </examples/cifar10_cnn_tf_keras.tgz>`
+    - `CIFAR-10 CNN <https://github.com/determined-ai/determined/tree/master/examples/official/cifar10_cnn_tf_keras>`_
 
 TensorBoard Integration
 -----------------------

diff --git a/docs/reference/api/pytorch.txt b/docs/reference/api/pytorch.txt
@@ -80,8 +80,8 @@ structures of the following types, which will be fed directly to the
 Examples
 --------
 
--  :download:`cifar10_cnn_pytorch </examples/cifar10_cnn_pytorch.tgz>`
+-  `cifar10_cnn_pytorch <https://github.com/determined-ai/determined/tree/master/examples/official/cifar10_cnn_pytorch>`_
    (PyTorch ``Sequential`` model)
--  :download:`mnist_pytorch </examples/mnist_pytorch.tgz>`
+-  `mnist_pytorch <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_pytorch>`_
    (two examples: PyTorch ``Sequential`` model and true multi-input
    multi-output model)
diff --git a/docs/reference/api/tensorpack.txt b/docs/reference/api/tensorpack.txt
@@ -158,4 +158,4 @@ the following abstract methods:
 Examples
 --------
 
--  :download:`mnist_tp </examples/mnist_tp.tgz>`
+-  `mnist_tp <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_tp>`_
diff --git a/docs/tutorials/pytorch-mnist-tutorial.txt b/docs/tutorials/pytorch-mnist-tutorial.txt
@@ -26,7 +26,7 @@ When training a PyTorch model, Determined provides a built-in training loop that
 
 The Determined training loop will then invoke these functions automatically. These methods should be organized into a **trial class**, which is a user-defined Python class that inherits from :class:`determined.pytorch.PyTorchTrial`. The following sections walk through how to write your first trial class and then how to run a training job with Determined.
 
-The complete code for this tutorial can be downloaded here: :download:`mnist_pytorch.tgz </examples/mnist_pytorch.tgz>`. We suggest you download that code and use it follow along as you read through this tutorial.
+The complete code for this tutorial can be downloaded here: `mnist_pytorch <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_pytorch>`_. We suggest you download that code and use it follow along as you read through this tutorial.
 
 Building a ``PyTorchTrial`` Class
 ---------------------------------

diff --git a/docs/tutorials/quick-start.txt b/docs/tutorials/quick-start.txt
@@ -24,7 +24,7 @@ In this guide, we will build an image classification model for the MNIST dataset
 
 |
 
-The code for this guide can be downloaded here: :download:`mnist_pytorch.tgz </examples/mnist_pytorch.tgz>`. A ``tf.keras`` implementation can be downloaded here: :download:`mnist_tf_keras.tgz </examples/mnist_tf_keras.tgz>`.
+The code for this guide can be downloaded here: `mnist_pytorch <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_pytorch>`_.
 
 Next, open a terminal window and ``cd`` into the extracted ``mnist_pytorch`` directory. The directory should contain the following files:
 

diff --git a/docs/tutorials/tf-mnist-tutorial.txt b/docs/tutorials/tf-mnist-tutorial.txt
@@ -25,7 +25,7 @@ When training a ``tf.keras`` model, Determined provides a built-in training loop
 
 The Determined training loop will then invoke these functions automatically. These methods should be organized into a **trial class**, which is a user-defined Python class that inherits from :class:`determined.keras.TFKerasTrial`. The following sections walk through how to write your first trial class and then how to run a training job with Determined.
 
-The complete code for this tutorial can be downloaded here: :download:`mnist_tf_keras.tgz </examples/mnist_tf_keras.tgz>`. We suggest you download that code and use it follow along as you read through this tutorial.
+The complete code for this tutorial can be downloaded here: `mnist_tf_keras <https://github.com/determined-ai/determined/tree/master/examples/official/mnist_tf_keras>`_. We suggest you download that code and use it follow along as you read through this tutorial.
 
 Building a Trial Class
 ----------------------