Initial commit

Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
ray-project · amogkam · May 30, 2023 · Nov 22, 2022 · Nov 22, 2022 · Dec 5, 2022
commit 44696c3d555e9c917635e67928ad6a06b828dde8
diff --git a/python/ray/train/huggingface/accelerate/accelerate_trainer.py b/python/ray/train/huggingface/accelerate/accelerate_trainer.py
@@ -110,7 +110,7 @@ def train_loop_per_worker():
     It is tested with ``accelerate==0.17.1``.
 
     Example:
-        .. testcode::
+        .. code-block:: python
 
             import torch
             import torch.nn as nn
@@ -225,12 +225,6 @@ def train_loop_per_worker():
             # Assert loss is less 0.09
             assert best_checkpoint_loss <= 0.09
 
-    .. testoutput::
-        :hide:
-        :options: +ELLIPSIS
-
-        ...
-
     Args:
         train_loop_per_worker: The training function to execute.
             This can either take in no arguments or a ``config`` dict.

diff --git a/python/ray/train/lightning/lightning_predictor.py b/python/ray/train/lightning/lightning_predictor.py
@@ -15,7 +15,7 @@ class LightningPredictor(TorchPredictor):
     """A predictor for PyTorch Lightning modules.
 
     Example:
-        .. testcode:: python
+        .. testcode::
 
             import torch
             import numpy as np
@@ -55,11 +55,6 @@ def configure_optimizers(self):
 
             assert output["predictions"].shape == (batch_size, output_dim)
 
-
-        .. testoutput::
-            :hide:
-            :options: +ELLIPSIS
-
     Args:
         model: The PyTorch Lightning module to use for predictions.
         preprocessor: A preprocessor used to transform data batches prior

diff --git a/python/ray/train/lightning/lightning_trainer.py b/python/ray/train/lightning/lightning_trainer.py
@@ -238,7 +238,7 @@ class LightningTrainer(TorchTrainer):
     run ``pytorch_lightning.Trainer.fit``.
 
     Example:
-        .. testcode::
+        .. code-block:: python
 
             import torch
             import torch.nn.functional as F
@@ -326,12 +326,6 @@ def configure_optimizers(self):
             result = trainer.fit()
             result
 
-    .. testoutput::
-        :hide:
-        :options: +ELLIPSIS
-
-        ...
-
     Args:
         lightning_config: Configuration for setting up the Pytorch Lightning Trainer.
             You can setup the configurations with ``LightningConfigBuilder``, and

@@ -85,7 +85,7 @@ def train_loop_per_worker():
 
     Example:
 
-    .. testcode::
+    .. code-block:: python
 
         import tensorflow as tf
 
@@ -137,12 +137,6 @@ def train_loop_per_worker(config):
         )
         result = trainer.fit()
 
-    .. testoutput::
-        :hide:
-        :options: +ELLIPSIS
-
-        ...
-
     Args:
         train_loop_per_worker: The training function to execute.
             This can either take in no arguments or a ``config`` dict.

diff --git a/python/ray/train/torch/torch_trainer.py b/python/ray/train/torch/torch_trainer.py
@@ -129,7 +129,7 @@ def train_func():
 
     Example:
 
-        .. testcode::
+        .. code-block:: python
 
             import torch
             import torch.nn as nn
@@ -229,12 +229,6 @@ def train_loop_per_worker():
             # Assert loss is less 0.09
             assert best_checkpoint_loss <= 0.09   # doctest: +SKIP
 
-    .. testoutput::
-        :hide:
-        :options: +ELLIPSIS
-
-        ...
-
     Args:
 
         train_loop_per_worker: The training function to execute.