# Scala User Guide
---
### **1. Try BigDL Examples**
This section will show you how to download BigDL prebuild packages and run the build-in examples.
#### **1.1 Download and config**
You can download the BigDL official releases and nightly build from the [Release Page](../release.md). After extracting the prebuild package, you need to set environment variables **BIGDL_HOME** and **SPARK_HOME** as follows:
```bash
export SPARK_HOME=folder path where you extract the Spark package
export BIGDL_HOME=folder path where you extract the BigDL package
```
#### **1.2 Use Spark interactive shell**
You can try BigDL using the Spark interactive shell as follows:
```bash
${BIGDL_HOME}/bin/spark-shell-with-bigdl.sh --master local[2]
```
You will then see a welcome message like below:
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.6
/_/
Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_112)
Type in expressions to have them evaluated.
Type :help for more information.
```
Before you try BigDL APIs, you should use `initNNcontext` to verify your environment:
```scala
scala> import com.intel.analytics.bigdl.dllib.NNContext
import com.intel.analytics.bigdl.dllib.NNContext
scala> val sc = NNContext.initNNContext("Run Example")
2021-01-26 10:19:52 WARN SparkContext:66 - Using an existing SparkContext; some configuration may not take effect.
2021-01-26 10:19:53 WARN SparkContext:66 - Using an existing SparkContext; some configuration may not take effect.
sc: org.apache.spark.SparkContext = org.apache.spark.SparkContext@487f025
```
#### **1.3 Run BigDL examples**
You can run an BigDL example, e.g., the [Lenet](https://github.com/intel-analytics/BigDL/tree/branch-2.0/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet), as a standard Spark program (running in either local mode or cluster mode) as follows:
1. You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the
files and put them in one folder(e.g. mnist).
There're four files. **train-images-idx3-ubyte** contains train images,
**train-labels-idx1-ubyte** is train label file, **t10k-images-idx3-ubyte** has validation images
and **t10k-labels-idx1-ubyte** contains validation labels. For more detail, please refer to the
download page.
After you uncompress the gzip files, these files may be renamed by some uncompress tools, e.g. **train-images-idx3-ubyte** is renamed
to **train-images.idx3-ubyte**. Please change the name back before you run the example.
2. Run the following command:
```bash
# Spark local mode
${BIGDL_HOME}/bin/spark-submit-scala-with-bigdl.sh \
--master local[2] \
--class com.intel.analytics.bigdl.dllib.models.lenet.Train \
${BIGDL_HOME}/jars/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not the same version
-f ./data/mnist \
-b 320 \
-e 20
# Spark standalone mode
${BIGDL_HOME}/bin/spark-submit-scala-with-bigdl.sh \
--master spark://... \ #add your spark master address
--executor-cores 2 \
--total-executor-cores 4 \
--class com.intel.analytics.bigdl.dllib.models.lenet.Train \
${BIGDL_HOME}/jars/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not the same version
-f ./data/mnist \
-b 320 \
-e 20
# Spark yarn client mode, please make sure the right HADOOP_CONF_DIR is set
${BIGDL_HOME}/bin/spark-submit-scala-with-bigdl.sh \
--master yarn \
--deploy-mode client \
--executor-cores 2 \
--num-executors 2 \
--class com.intel.analytics.bigdl.dllib.models.lenet.Train \
${BIGDL_HOME}/jars/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not the same version
-f ./data/mnist \
-b 320 \
-e 20
# Spark yarn cluster mode, please make sure the right HADOOP_CONF_DIR is set
${BIGDL_HOME}/bin/spark-submit-scala-with-bigdl.sh \
--master yarn \
--deploy-mode cluster \
--executor-cores 2 \
--num-executors 2 \
--class com.intel.analytics.bigdl.dllib.models.lenet.Train \
${BIGDL_HOME}/jars/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not the same version
-f ./data/mnist \
-b 320 \
-e 20
```
---
### **2. Build BigDL Applications**
This section will show you how to build your own deep learning project with BigDL.
#### **2.1 Add BigDL dependency**
##### **2.1.1 official Release**
Currently, BigDL releases are hosted on maven central; below is an example to add the BigDL dllib dependency to your own project:
```xml
com.intel.analytics.bigdl
bigdl-dllib-spark_2.4.6
2.0.0
```
You can find the other SPARK version [here](https://search.maven.org/search?q=bigdl-dllib), such as `spark_3.1.2`.
SBT developers can use
```sbt
libraryDependencies += "com.intel.analytics.bigdl" % "bigdl-dllib-spark_2.4.6" % "2.0.0"
```
##### **2.1.2 Nightly Build**
Currently, BigDL nightly build is hosted on [SonaType](https://oss.sonatype.org/content/groups/public/com/intel/analytics/bigdl/).
To link your application with the latest BigDL nightly build, you should add some dependencies like [official releases](#11-official-release), but change `2.0.0` to the snapshot version (such as 0.14.0-snapshot), and add below repository to your pom.xml.
```xml
sonatype
sonatype repository
https://oss.sonatype.org/content/groups/public/
true
true
```
SBT developers can use
```sbt
resolvers += "ossrh repository" at "https://oss.sonatype.org/content/repositories/snapshots/"
```
#### **2.2 Build a Scala project**
To enable BigDL in project, you should add BigDL to your project's dependencies using maven or sbt. Here is a [simple MLP example](https://github.com/intel-analytics/zoo-tutorials/tree/master/scala/SimpleMlp) to show you how to use BigDL to build your own deep learning project using maven or sbt, and how to run the simple example in IDEA and spark-submit.