Merge branch 'master' into train-examples

Signed-off-by: angelinalg <[email protected]>
ray-project · Sep 13, 2023 · 6368840 · 6368840
2 parents 55f348f + b00d029
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diff --git a/doc/source/train/examples.rst b/doc/source/train/examples.rst
@@ -45,8 +45,8 @@ Intermediate
  - :ref:`Fine-tune of Stable Diffusion with DreamBooth and Ray Train <torch_finetune_dreambooth_ex>`
  * - Lightning
  - :ref:`Train with PyTorch Lightning and Ray Data <lightning_advanced_example>`
- * - Accelerate
- - :ref:`Fine-tune a text classifier on GLUE Benchmark with Hugging Face Accelerate <train_transformers_accelerate_example>`
+ * - Transformers
+ - :ref:`Fine-tune a Text Classifier on GLUE Benchmark with Hugging Face Accelerate <train_transformers_glue_example>`
 
 
 Advanced

diff --git a/doc/source/train/examples/transformers/huggingface_text_classification.ipynb b/doc/source/train/examples/transformers/huggingface_text_classification.ipynb
@@ -4,7 +4,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "(train_transformers_accelerate_example)=\n",
+ "(train_transformers_glue_example)=\n",
  "\n",
  "# Fine-tune a Hugging Face Transformers Model"
  ]

diff --git a/doc/source/tune/api/suggestion.rst b/doc/source/tune/api/suggestion.rst
@@ -163,7 +163,7 @@ See the `BOHB paper <https://arxiv.org/abs/1807.01774>`_ for more details.
 BlendSearch (tune.search.flaml.BlendSearch)
 -------------------------------------------
 
-BlendSearch is an economical hyperparameter optimization algorithm that combines combines local search with global search.
+BlendSearch is an economical hyperparameter optimization algorithm that combines local search with global search.
 It is backed by the `FLAML library <https://github.com/microsoft/FLAML>`_.
 It allows the users to specify a low-cost initial point as input if such point exists.
 

diff --git a/python/ray/train/examples/accelerate/accelerate_torch_trainer.py b/python/ray/train/examples/accelerate/accelerate_torch_trainer.py
@@ -19,6 +19,7 @@
  set_seed,
 )
 
+import ray
 import ray.train
 from ray.train import DataConfig, ScalingConfig, Checkpoint
 from ray.train.torch import TorchTrainer