[Train/docs] Deduplicate Train examples & move from ray.air to ray.train

doc/source/ray-air/doc_code/tuner.py

@@ -79,7 +79,7 @@
 # __torch_start__
 from ray import tune
 from ray.tune import Tuner
-from ray.air.examples.pytorch.torch_linear_example import (
+from ray.train.examples.pytorch.torch_linear_example import (
     train_func as linear_train_func,
 )
 from ray.train.torch import TorchTrainer

doc/source/ray-overview/index.md

@@ -201,23 +201,23 @@ This example shows how you can use Ray Train with PyTorch.
 
 First, set up your dataset and model.
 
-```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/pytorch/torch_quick_start.py
 :language: python
 :start-after: __torch_setup_begin__
 :end-before: __torch_setup_end__
 ```
 
 Now define your single-worker PyTorch training function.
 
-```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/pytorch/torch_quick_start.py
 :language: python
 :start-after: __torch_single_begin__
 :end-before: __torch_single_end__
 ```
 
 This training function can be executed with:
 
-```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/pytorch/torch_quick_start.py
 :language: python
 :start-after: __torch_single_run_begin__
 :end-before: __torch_single_run_end__
@@ -231,7 +231,7 @@ easily setup your model & data for distributed training.
 This will automatically wrap your model with ``DistributedDataParallel``
 and place it on the right device, and add ``DistributedSampler`` to your DataLoaders.
 
-```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/pytorch/torch_quick_start.py
 :language: python
 :start-after: __torch_distributed_begin__
 :end-before: __torch_distributed_end__
@@ -240,7 +240,7 @@ and place it on the right device, and add ``DistributedSampler`` to your DataLoa
 Then, instantiate a ``Trainer`` that uses a ``"torch"`` backend
 with 4 workers, and use it to run the new training function!
 
-```{literalinclude} /../../python/ray/train/examples/torch_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/pytorch/torch_quick_start.py
 :language: python
 :start-after: __torch_trainer_begin__
 :end-before: __torch_trainer_end__
@@ -254,23 +254,23 @@ with Keras <https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ke
 
 First, set up your dataset and model.
 
-```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/tf/tensorflow_quick_start.py
 :language: python
 :start-after: __tf_setup_begin__
 :end-before: __tf_setup_end__
 ```
 
 Now define your single-worker TensorFlow training function.
 
-```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/tf/tensorflow_quick_start.py
 :language: python
 :start-after: __tf_single_begin__
 :end-before: __tf_single_end__
 ```
 
 This training function can be executed with:
 
-```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/tf/tensorflow_quick_start.py
 :language: python
 :start-after: __tf_single_run_begin__
 :end-before: __tf_single_run_end__
@@ -284,7 +284,7 @@ All you need to do is:
 2. Choose your TensorFlow distributed training strategy. In this example
    we use the ``MultiWorkerMirroredStrategy``.
 
-```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/tf/tensorflow_quick_start.py
 :language: python
 :start-after: __tf_distributed_begin__
 :end-before: __tf_distributed_end__
@@ -293,7 +293,7 @@ All you need to do is:
 Then, instantiate a ``Trainer`` that uses a ``"tensorflow"`` backend
 with 4 workers, and use it to run the new training function!
 
-```{literalinclude} /../../python/ray/train/examples/tensorflow_quick_start.py
+```{literalinclude} /../../python/ray/train/examples/tf/tensorflow_quick_start.py
 :language: python
 :start-after: __tf_trainer_begin__
 :end-before: __tf_trainer_end__

doc/source/train/examples.rst

@@ -15,7 +15,7 @@ General Examples
 PyTorch
 ~~~~~~~
 
-* :doc:`/train/examples/torch_fashion_mnist_example`:
+* :doc:`/train/examples/pytorch/torch_fashion_mnist_example`:
   End-to-end example for PyTorch.
 
 * :doc:`/train/examples/transformers/transformers_example`:
@@ -24,7 +24,7 @@ PyTorch
 TensorFlow
 ~~~~~~~~~~
 
-* :doc:`/train/examples/tensorflow_mnist_example`:
+* :doc:`/train/examples/tf/tensorflow_mnist_example`:
   End-to-end example for TensorFlow
 
 Horovod
@@ -43,10 +43,10 @@ Logger/Callback Examples
 Ray Tune Integration Examples
 -----------------------------
 
-* :doc:`/train/examples/tune_tensorflow_mnist_example`:
+* :doc:`/train/examples/tf/tune_tensorflow_mnist_example`:
   End-to-end example for tuning a TensorFlow model.
 
-* :doc:`/train/examples/tune_cifar_torch_pbt_example`:
+* :doc:`/train/examples/pytorch/tune_cifar_torch_pbt_example`:
   End-to-end example for tuning a PyTorch model with PBT.
 
 ..
@@ -66,6 +66,6 @@ Ray Tune Integration Examples
 Benchmarks
 ----------
 
-* :doc:`/train/examples/torch_data_prefetch_benchmark/benchmark_example`:
+* :doc:`/train/examples/pytorch/torch_data_prefetch_benchmark/benchmark_example`:
   Benchmark example for the PyTorch data transfer auto pipeline.
 

doc/source/train/examples/torch_data_prefetch_benchmark/benchmark_example.rst → doc/source/train/examples/pytorch/torch_data_prefetch_benchmark/benchmark_example.rst

@@ -49,4 +49,4 @@ The auto pipeline functionality offers more speed improvement when the model siz
 ========== =================== ======================== ========================
 
 
-.. literalinclude:: /../../python/ray/train/examples/torch_data_prefetch_benchmark/auto_pipeline_for_host_to_device_data_transfer.py
+.. literalinclude:: /../../python/ray/train/examples/pytorch/torch_data_prefetch_benchmark/auto_pipeline_for_host_to_device_data_transfer.py

doc/source/train/examples/pytorch/torch_fashion_mnist_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+torch_fashion_mnist_example
+===========================
+
+.. literalinclude:: /../../python/ray/train/examples/pytorch/torch_fashion_mnist_example.py

doc/source/train/examples/pytorch/torch_regression_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+torch_regression_example
+========================
+
+.. literalinclude:: /../../python/ray/train/examples/pytorch/torch_regression_example.py

doc/source/train/examples/pytorch/tune_cifar_torch_pbt_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+tune_cifar_torch_pbt_example
+============================
+
+.. literalinclude:: /../../python/ray/train/examples/pytorch/tune_cifar_torch_pbt_example.py

doc/source/train/examples/pytorch/tune_torch_regression_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+tune_torch_regression_example
+=============================
+
+.. literalinclude:: /../../python/ray/train/examples/pytorch/tune_torch_regression_example.py

doc/source/train/examples/tf/tensorflow_mnist_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+tensorflow_mnist_example
+========================
+
+.. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_mnist_example.py

doc/source/train/examples/tf/tensorflow_regression_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+tensorflow_regression_example
+=============================
+
+.. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_regression_example.py

doc/source/train/examples/tf/tune_tensorflow_mnist_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+tune_tensorflow_mnist_example
+=============================
+
+.. literalinclude:: /../../python/ray/train/examples/tf/tune_tensorflow_mnist_example.py

doc/source/train/getting-started.rst

@@ -88,22 +88,22 @@ Here are examples for some of the commonly used trainers:
 
     First, set up your dataset and model.
 
-    .. literalinclude:: /../../python/ray/train/examples/torch_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/pytorch/torch_quick_start.py
         :language: python
         :start-after: __torch_setup_begin__
         :end-before: __torch_setup_end__
 
 
     Now define your single-worker PyTorch training function.
 
-    .. literalinclude:: /../../python/ray/train/examples/torch_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/pytorch/torch_quick_start.py
         :language: python
         :start-after: __torch_single_begin__
         :end-before: __torch_single_end__
 
     This training function can be executed with:
 
-    .. literalinclude:: /../../python/ray/train/examples/torch_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/pytorch/torch_quick_start.py
         :language: python
         :start-after: __torch_single_run_begin__
         :end-before: __torch_single_run_end__
@@ -116,15 +116,15 @@ Here are examples for some of the commonly used trainers:
     This will automatically wrap your model with ``DistributedDataParallel``
     and place it on the right device, and add ``DistributedSampler`` to your DataLoaders.
 
-    .. literalinclude:: /../../python/ray/train/examples/torch_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/pytorch/torch_quick_start.py
         :language: python
         :start-after: __torch_distributed_begin__
         :end-before: __torch_distributed_end__
 
     Then, instantiate a ``TorchTrainer``
     with 4 workers, and use it to run the new training function!
 
-    .. literalinclude:: /../../python/ray/train/examples/torch_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/pytorch/torch_quick_start.py
         :language: python
         :start-after: __torch_trainer_begin__
         :end-before: __torch_trainer_end__
@@ -138,21 +138,21 @@ Here are examples for some of the commonly used trainers:
 
     First, set up your dataset and model.
 
-    .. literalinclude:: /../../python/ray/train/examples/tensorflow_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_quick_start.py
         :language: python
         :start-after: __tf_setup_begin__
         :end-before: __tf_setup_end__
 
     Now define your single-worker TensorFlow training function.
 
-    .. literalinclude:: /../../python/ray/train/examples/tensorflow_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_quick_start.py
         :language: python
         :start-after: __tf_single_begin__
         :end-before: __tf_single_end__
 
     This training function can be executed with:
 
-    .. literalinclude:: /../../python/ray/train/examples/tensorflow_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_quick_start.py
         :language: python
         :start-after: __tf_single_run_begin__
         :end-before: __tf_single_run_end__
@@ -165,15 +165,15 @@ Here are examples for some of the commonly used trainers:
     2. Choose your TensorFlow distributed training strategy. In this example
        we use the ``MultiWorkerMirroredStrategy``.
 
-    .. literalinclude:: /../../python/ray/train/examples/tensorflow_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_quick_start.py
         :language: python
         :start-after: __tf_distributed_begin__
         :end-before: __tf_distributed_end__
 
     Then, instantiate a ``TensorflowTrainer`` with 4 workers,
     and use it to run the new training function!
 
-    .. literalinclude:: /../../python/ray/train/examples/tensorflow_quick_start.py
+    .. literalinclude:: /../../python/ray/train/examples/tf/tensorflow_quick_start.py
         :language: python
         :start-after: __tf_trainer_begin__
         :end-before: __tf_trainer_end__