diff --git a/docs/readthedocs/source/_toc.yml b/docs/readthedocs/source/_toc.yml index de5a3db9..6002adbd 100644 --- a/docs/readthedocs/source/_toc.yml +++ b/docs/readthedocs/source/_toc.yml @@ -39,15 +39,22 @@ subtrees: - file: doc/Orca/Overview/distributed-tuning - file: doc/Orca/Overview/ray - file: doc/Orca/QuickStart/index - title: "Quick Examples" + title: "Quickstarts" subtrees: - entries: - - file: doc/UseCase/spark-dataframe - - file: doc/UseCase/xshards-pandas - - file: doc/Orca/QuickStart/ray-quickstart - - file: doc/Orca/QuickStart/orca-pytorch-distributed-quickstart - - file: doc/Orca/QuickStart/orca-autoestimator-pytorch-quickstart - - file: doc/Orca/QuickStart/orca-autoxgboost-quickstart + - file: doc/Orca/Quickstart/orca-tf-quickstart + - file: doc/Orca/Quickstart/orca-tf2keras-quickstart + - file: doc/Orca/Quickstart/orca-keras-quickstart + - file: doc/Orca/Quickstart/orca-pytorch-quickstart + - file: doc/Orca/Quickstart/ray-quickstart + - file: doc/Orca/Howto/index + title: "How-to Guides" + subtrees: + - entries: + - file: doc/Orca/Howto/spark-dataframe + - file: doc/Orca/Howto/xshards-pandas + - file: doc/Orca/Howto/orca-autoestimator-pytorch-quickstart + - file: doc/Orca/Howto/orca-autoxgboost-quickstart - file: doc/Orca/Tutorial/index title: "Tutorials" subtrees: diff --git a/docs/readthedocs/source/doc/Orca/Howto/index.rst b/docs/readthedocs/source/doc/Orca/Howto/index.rst new file mode 100644 index 00000000..df387ce3 --- /dev/null +++ b/docs/readthedocs/source/doc/Orca/Howto/index.rst @@ -0,0 +1,7 @@ +Orca How-to Guides +========================= + +* `Use Spark DataFrames for Deep Learning `__ +* `Use Distributed Pandas for Deep Learning `__ +* `Enable AutoML for PyTorch `__ +* `Use AutoXGBoost to auto-tune XGBoost parameters `__ diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-autoestimator-pytorch-quickstart.md b/docs/readthedocs/source/doc/Orca/Howto/orca-autoestimator-pytorch-quickstart.md similarity index 100% rename from docs/readthedocs/source/doc/Orca/QuickStart/orca-autoestimator-pytorch-quickstart.md rename to docs/readthedocs/source/doc/Orca/Howto/orca-autoestimator-pytorch-quickstart.md diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-autoxgboost-quickstart.md b/docs/readthedocs/source/doc/Orca/Howto/orca-autoxgboost-quickstart.md similarity index 100% rename from docs/readthedocs/source/doc/Orca/QuickStart/orca-autoxgboost-quickstart.md rename to docs/readthedocs/source/doc/Orca/Howto/orca-autoxgboost-quickstart.md diff --git a/docs/readthedocs/source/doc/UseCase/spark-dataframe.md b/docs/readthedocs/source/doc/Orca/Howto/spark-dataframe.md similarity index 98% rename from docs/readthedocs/source/doc/UseCase/spark-dataframe.md rename to docs/readthedocs/source/doc/Orca/Howto/spark-dataframe.md index 8c5b1fba..e4d87f44 100644 --- a/docs/readthedocs/source/doc/UseCase/spark-dataframe.md +++ b/docs/readthedocs/source/doc/Orca/Howto/spark-dataframe.md @@ -6,7 +6,7 @@ --- -**In this guide we will describe how to use Apache Spark Dataframes to scale-out data processing for distribtued deep learning.** +**In this guide we will describe how to use Apache Spark Dataframes to scale-out data processing for distributed deep learning.** The dataset used in this guide is [movielens-1M](https://grouplens.org/datasets/movielens/1m/), which contains 1 million ratings of 5 levels from 6000 users on 4000 movies. We will read the data into Spark Dataframe and directly use the Spark Dataframe as the input to the distributed training. diff --git a/docs/readthedocs/source/doc/UseCase/xshards-pandas.md b/docs/readthedocs/source/doc/Orca/Howto/xshards-pandas.md similarity index 99% rename from docs/readthedocs/source/doc/UseCase/xshards-pandas.md rename to docs/readthedocs/source/doc/Orca/Howto/xshards-pandas.md index cbb47ccd..26469474 100644 --- a/docs/readthedocs/source/doc/UseCase/xshards-pandas.md +++ b/docs/readthedocs/source/doc/Orca/Howto/xshards-pandas.md @@ -6,7 +6,7 @@ --- -**In this guide we will describe how to use [XShards](../Orca/Overview/data-parallel-processing.md) to scale-out Pandas data processing for distribtued deep learning.** +**In this guide we will describe how to use [XShards](../Orca/Overview/data-parallel-processing.md) to scale-out Pandas data processing for distributed deep learning.** ### 1. Read input data into XShards of Pandas DataFrame