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integrate dllib.md with scala.md (#3388)

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* integrate dllib.md with scala.md

* add how to run in cluster for python dllib
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180
docs/readthedocs/source/doc/DLlib/Overview/dllib.md
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@ -10,167 +10,12 @@ It includes the functionalities of the [original BigDL](https://github.com/intel
* [Spark ML pipeline support](nnframes.md)
## 2. Scala user guide
### 2.1 Install
#### 2.1.1 **Download a pre-built library**
You can download the bigdl-dllib build from the [Release Page](../release.md).
#### 2.1.2 **Link with a release version**
Currently, dllib releases are hosted on maven central; here's an example to add the dllib dependency to your own project:
```xml
<dependency>
<groupId>com.intel.analytics.bigdl</groupId>
<artifactId>bigdl-dllib-[spark_2.4.6|spark_3.1.2]</artifactId>
<version>${BIGD_DLLIB_VERSION}</version>
</dependency>
```
Please choose the suffix according to your Spark platform.
SBT developers can use
```sbt
libraryDependencies += "com.intel.analytics.bigdl" % "dllib-[spark_2.4.6|spark_3.1.2]" % "${BIGDL_DLLIB_VERSION}"
```
### 2.2 Run
#### 2.2.1 **Set Environment Variables**
Set **BIGDL_HOME** and **SPARK_HOME**:
* If you download bigdl-dllib from the [Release Page](../release-download.md)
```bash
export SPARK_HOME=folder path where you extract the spark package
export BIGDL_HOME=folder path where you extract the bigdl package
```
---
#### 2.2.2 **Use Interactive Spark Shell**
You can try bigdl-dllib easily using the Spark interactive shell. Run below command to start spark shell with bigdl-dllib support:
```bash
${BIGDL_HOME}/bin/spark-shell-with-dllib.sh
```
You will see a welcome message looking like below:
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.3
/_/
Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181)
Spark context available as sc.
scala>
```
To use BigDL, you should first initialize the environment as below.
```scala
scala> import com.intel.analytics.bigdl.dllib.NNContext
import com.intel.analytics.bigdl.dllib.NNContext
scala> NNContext.initNNContext()
2021-10-25 10:12:36 WARN SparkContext:66 - Using an existing SparkContext; some configuration may not take effect.
2021-10-25 10:12:36 WARN SparkContext:66 - Using an existing SparkContext; some configuration may not take effect.
res0: org.apache.spark.SparkContext = org.apache.spark.SparkContext@525c0f74
```
Once the environment is successfully initiated, you'll be able to play with dllib API's.
For instance, to experiment with the ````dllib.keras```` APIs in dllib, you may try below code:
```scala
scala> import com.intel.analytics.bigdl.dllib.keras.layers._
scala> import com.intel.analytics.bigdl.numeric.NumericFloat
scala> import com.intel.analytics.bigdl.dllib.utils.Shape
scala> val seq = Sequential()
val layer = ConvLSTM2D(32, 4, returnSequences = true, borderMode = "same",
inputShape = Shape(8, 40, 40, 32))
seq.add(layer)
```
### 2.1 Install and Run
Please refer [scala guide](../../UserGuide/scala.md) for details.
---
#### 2.2.3 **Run as a Spark Program**
You can run a bigdl-dllib program, e.g., the [Image Inference](https://github.com/intel-analytics/BigDL/blob/branch-2.0/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/nnframes/imageInference), as a standard Spark program (running on either a local machine or a distributed cluster) as follows:
1. Download the pretrained caffe model and prepare the images
2. Run the following command:
```bash
# Spark local mode
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master local[2] \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
# Spark standalone mode
## ${SPARK_HOME}/sbin/start-master.sh
## check master URL from http://localhost:8080
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master spark://... \
--executor-cores cores_per_executor \
--total-executor-cores total_cores_for_the_job \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
# Spark yarn client mode
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master yarn \
--deploy-mode client \
--executor-cores cores_per_executor \
--num-executors executors_number \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
# Spark yarn cluster mode
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master yarn \
--deploy-mode cluster \
--executor-cores cores_per_executor \
--num-executors executors_number \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
```
The parameters used in the above command are:
* -f: The path where you put your PTB data.
* -b: The mini-batch size. The mini-batch size is expected to be a multiple of *total cores* used in the job. In this example, the mini-batch size is suggested to be set to *total cores * 4*
* --learningRate: learning rate for adagrad
* --learningRateDecay: learning rate decay for adagrad
* --hidden: hiddensize for lstm
* --vocabSize: vocabulary size, default 10000
* --numLayers: numbers of lstm cell, default 2 lstm cells
* --numSteps: number of words per record in LM
* --keepProb: the probability to do dropout
If you are to run your own program, do remember to do the initialize before call other bigdl-dllib API's, as shown below.
```scala
// Scala code example
import com.intel.analytics.bigdl.dllib.NNContext
NNContext.initNNContext()
```
---
### 2.3 Get started
### 2.2 Get started
---
This section show a single example of how to use dllib to build a deep learning application on Spark, using Keras APIs
@ -288,7 +133,24 @@ python script.py
```
---
### 3.3 Get started
---
#### **NN Context**
`NNContext` is the main entry for provisioning the dllib program on the underlying cluster (such as K8s or Hadoop cluster), or just on a single laptop.
An dlllib program usually starts with the initialization of `NNContext` as follows:
```python
from bigdl.dllib.nncontext import *
init_nncontext()
```
In `init_nncontext`, the user may specify cluster mode for the dllib program:
- *Cluster mode=*: "local", "yarn-client", "yarn-cluster", "k8s-client", "standalone" and "spark-submit". Default to be "local".
The dllib program simply runs `init_nncontext` on the local machine, which will automatically provision the runtime Python environment and distributed execution engine on the underlying computing environment (such as a single laptop, a large K8s or Hadoop cluster, etc.).
#### **Autograd Examples using bigdl-dllb keras Python API**

136
docs/readthedocs/source/doc/UserGuide/scala.md
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@ -17,7 +17,7 @@ export BIGDL_HOME=folder path where you extract the BigDL package
You can try BigDL using the Spark interactive shell as follows:
```bash
${BIGDL_HOME}/bin/spark-shell-with-bigdl.sh --master local[2]
${BIGDL_HOME}/bin/spark-shell-with-dllib.sh
```
You will then see a welcome message like below:
@ -46,69 +46,100 @@ scala> val sc = NNContext.initNNContext("Run Example")
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
```
Once the environment is successfully initiated, you'll be able to play with dllib API's.
For instance, to experiment with the ````dllib.keras```` APIs in dllib, you may try below code:
```scala
scala> import com.intel.analytics.bigdl.dllib.keras.layers._
scala> import com.intel.analytics.bigdl.numeric.NumericFloat
scala> import com.intel.analytics.bigdl.dllib.utils.Shape
scala> val seq = Sequential()
val layer = ConvLSTM2D(32, 4, returnSequences = true, borderMode = "same",
inputShape = Shape(8, 40, 40, 32))
seq.add(layer)
```
#### **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:
You can run a bigdl-dllib program, e.g., the [Language Model](https://github.com/intel-analytics/BigDL/tree/branch-2.0/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel), as a standard Spark program (running on either a local machine or a distributed cluster) 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.
1. Prepare the dataset, please refer [Prepare PTB Data](https://github.com/intel-analytics/BigDL/tree/branch-2.0/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel) for details
2. Run the following command:
```bash
# Spark local mode
${BIGDL_HOME}/bin/spark-submit-scala-with-bigdl.sh \
${BIGDL_HOME}/bin/spark-submit-with-dllib.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
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
# 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_HOME}/sbin/start-master.sh
## check master URL from http://localhost:8080
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master spark://... \
--executor-cores cores_per_executor \
--total-executor-cores total_cores_for_the_job \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
# 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 client mode
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master yarn \
--deploy-mode client \
--executor-cores cores_per_executor \
--num-executors executors_number \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
# 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
# Spark yarn cluster mode
${BIGDL_HOME}/bin/spark-submit-with-dllib.sh \
--master yarn \
--deploy-mode cluster \
--executor-cores cores_per_executor \
--num-executors executors_number \
--class com.intel.analytics.bigdl.dllib.example.languagemodel.PTBWordLM \
${BIGDL_HOME}/jars/bigdl-dllib-0.14.0-SNAPSHOT-jar-with-dependencies.jar \ #change to your jar file if your download is not spark_2.4.3-0.14.0
-f DATA_PATH \
-b 4 \
--numLayers 2 --vocab 100 --hidden 6 \
--numSteps 3 --learningRate 0.005 -e 1 \
--learningRateDecay 0.001 --keepProb 0.5
```
The parameters used in the above command are:
* -f: The path where you put your PTB data.
* -b: The mini-batch size. The mini-batch size is expected to be a multiple of *total cores* used in the job. In this example, the mini-batch size is suggested to be set to *total cores * 4*
* --learningRate: learning rate for adagrad
* --learningRateDecay: learning rate decay for adagrad
* --hidden: hiddensize for lstm
* --vocabSize: vocabulary size, default 10000
* --numLayers: numbers of lstm cell, default 2 lstm cells
* --numSteps: number of words per record in LM
* --keepProb: the probability to do dropout
If you are to run your own program, do remember to do the initialize before call other bigdl-dllib API's, as shown below.
```scala
// Scala code example
import com.intel.analytics.bigdl.dllib.NNContext
NNContext.initNNContext()
```
---
### **2. Build BigDL Applications**
@ -119,12 +150,11 @@ This section will show you how to build your own deep learning project with BigD
##### **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
<dependency>
<groupId>com.intel.analytics.bigdl</groupId>
<artifactId>bigdl-dllib-spark_2.4.6</artifactId>
<version>2.0.0</version>
<version>0.14.0</version>
</dependency>
```
@ -133,7 +163,7 @@ You can find the other SPARK version [here](https://search.maven.org/search?q=bi
SBT developers can use
```sbt
libraryDependencies += "com.intel.analytics.bigdl" % "bigdl-dllib-spark_2.4.6" % "2.0.0"
libraryDependencies += "com.intel.analytics.bigdl" % "bigdl-dllib-spark_2.4.6" % "0.14.0"
```
##### **2.1.2 Nightly Build**
@ -164,5 +194,5 @@ resolvers += "ossrh repository" at "https://oss.sonatype.org/content/repositorie
#### **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.
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/BigDL/tree/branch-2.0/apps/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.