# Installation

## Install Java
You need to download and install JDK in the environment, and properly set the environment variable `JAVA_HOME`. JDK8 is highly recommended.

```bash
# For Ubuntu
sudo apt-get install openjdk-8-jre
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/

# For CentOS
su -c "yum install java-1.8.0-openjdk"
export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.282.b08-1.el7_9.x86_64/jre

export PATH=$PATH:$JAVA_HOME/bin
java -version  # Verify the version of JDK.
```

## Install Anaconda
We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the Python environment.

You can follow the steps below to install conda:
```bash
# Download Anaconda installation script 
wget -P /tmp https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh

# Execute the script to install conda
bash /tmp/Anaconda3-2020.02-Linux-x86_64.sh

# Run this command in your terminal to activate conda
source ~/.bashrc
``` 

Then create a Python environment for BigDL Orca:
```bash
conda create -n py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
conda activate py37
```

## To use basic Orca features
You can install Orca in your created conda environment for distributed data processing, training and inference with the following command:
```bash
pip install bigdl-orca  # For the official release version
```

or for the nightly build version, use:
```bash
pip install --pre --upgrade bigdl-orca  # For the latest nightly build version
```

## To additionally use RayOnSpark

If you wish to run [RayOnSpark](ray.md) or [sklearn-style Estimator APIs in Orca](distributed-training-inference.md) with "ray" backend, use the extra key `[ray]` during the installation above:

```bash
pip install bigdl-orca[ray]  # For the official release version
```

or for the nightly build version, use:
```bash
pip install --pre --upgrade bigdl-orca[ray]  # For the latest nightly build version
```

Note that with the extra key of [ray], `pip` will automatically install the additional dependencies for RayOnSpark,
including `ray[default]==1.9.2`, `aiohttp==3.8.1`, `async-timeout==4.0.1`, `aioredis==1.3.1`, `hiredis==2.0.0`, `prometheus-client==0.11.0`, `psutil`,  `setproctitle`.

## To additionally use AutoML

If you wish to run AutoML, use the extra key `[automl]` during the installation above:

```bash
pip install bigdl-orca[automl]  # For the official release version
````

or for the nightly build version, use:
```bash
pip install --pre --upgrade bigdl-orca[automl]  # For the latest nightly build version
```

Note that with the extra key of [automl], `pip` will automatically install the additional dependencies for distributed hyper-parameter tuning,
including `ray[tune]==1.9.2`, `scikit-learn`, `tensorboard`, `xgboost` together with the dependencies given by the extra key [ray].

- To use [Pytorch AutoEstimator](distributed-tuning.md#pytorch-autoestimator), you need to install Pytorch with `pip install torch==1.8.1`.

- To use [TensorFlow/Keras AutoEstimator](distributed-tuning.md#tensorflow-keras-autoestimator), you need to install TensorFlow with `pip install tensorflow==1.15.0`.