From b622d600487caa7d2ab686bdae7037b76465ac13 Mon Sep 17 00:00:00 2001
From: Yu Shan <shan.yu@intel.com>
Date: Thu, 28 Oct 2021 14:45:16 +0800
Subject: [PATCH] Add installation guide in Orca AutoML Doc (#3304)

* add install guide in orca.automl doc

* typo

* update

* add sklearn and tensorboard
---
 .../doc/Orca/Overview/distributed-tuning.md   | 21 ++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md b/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
index d7869ab6..173f61f0 100644
--- a/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
+++ b/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
@@ -4,6 +4,25 @@
 
 **Orca `AutoEstimator` provides similar APIs as Orca `Estimator` for distributed hyper-parameter tuning.** 
 
+### **Install**
+We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the Python environment.
+```bash
+conda create -n bigdl-orca-automl python=3.7  # "bigdl-orca-automl" is conda environment name, you can use any name you like.
+conda activate bigdl-orca-automl
+pip install bigdl-orca[automl]
+````
+You can install the latest release version of BigDL Orca as follows:
+```bash
+pip install --pre --upgrade bigdl-orca[automl]
+```
+_Note that with extra key of [automl], `pip` will automatically install the additional dependencies for distributed hyper-parameter tuning,
+including `ray[tune]==1.2.0`, `psutil`, `aiohttp==3.7.0`, `aioredis==1.1.0`, `setproctitle`, `hiredis==1.1.0`, `async-timeout==3.0.1`, `scikit-learn`, `tensorboard`, `xgboost`._
+
+To use [Pytorch Estimator](#pytorch-autoestimator), you need to install Pytorch with `pip install torch==1.8.1`.
+
+To use [TensorFlow/Keras AutoEstimator](#tensorflow-keras-autoestimator), you need to install Tensorflow with `pip install tensorflow==1.15.0`.
+
+
 ### **1. AutoEstimator**
 
 To perform distributed hyper-parameter tuning, user can first create an Orca `AutoEstimator` from standard TensorFlow Keras or PyTorch model, and then call `AutoEstimator.fit`.
@@ -180,7 +199,7 @@ auto_estimator.fit(
 ```
 See [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
 
-### **4. Scheduler**
+### **5. Scheduler**
 *Scheduler* can stop/pause/tweak the hyper-parameters of running trials, making the hyper-parameter tuning process much efficient.
 
 We support all *Schedulers* in [Ray Tune](https://docs.ray.io/en/master/index.html). See [Ray Tune Schedulers](https://docs.ray.io/en/master/tune/api_docs/schedulers.html#schedulers-ref) for more details.