ipex-llm/docs/readthedocs/source/doc/DLlib/Overview/dllib.md

# DLlib in 5 minutes

## Overview

DLlib is a distributed deep learning library for Apache Spark; with DLlib, users can write their deep learning applications as standard Spark programs (using either Scala or Python APIs).

It includes the functionalities of the [original BigDL](https://github.com/intel-analytics/BigDL/tree/branch-0.14) project, and provides following high-level APIs for distributed deep learning on Spark:

* [Keras-like API](keras-api.md)
* [Spark ML pipeline support](nnframes.md)


---

## Scala Example

This section show a single example of how to use dllib to build a deep learning application on Spark, using Keras APIs

#### **LeNet Model on MNIST using Keras-Style API**

This tutorial is an explanation of what is happening in the [lenet](https://github.com/intel-analytics/BigDL/tree/branch-2.0/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras) example

A bigdl-dllib program starts with initialize as follows.
````scala
val conf = Engine.createSparkConf()
  .setAppName("Train Lenet on MNIST")
  .set("spark.task.maxFailures", "1")
val sc = new SparkContext(conf)
Engine.init
````

After the initialization, we need to:

1. Load train and validation data by _**creating the [```DataSet```](https://github.com/intel-analytics/BigDL/blob/branch-2.0/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala)**_ (e.g., ````SampleToGreyImg````, ````GreyImgNormalizer```` and ````GreyImgToBatch````):
   ````scala
   val trainSet = (if (sc.isDefined) {
       DataSet.array(load(trainData, trainLabel), sc.get, param.nodeNumber)
     } else {
       DataSet.array(load(trainData, trainLabel))
     }) -> SampleToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch(
       param.batchSize)

   val validationSet = DataSet.array(load(validationData, validationLabel), sc) ->
       BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch(
       param.batchSize)
   ````

2. We then define Lenet model using Keras-style api
   ````scala
   val input = Input(inputShape = Shape(28, 28, 1))
   val reshape = Reshape(Array(1, 28, 28)).inputs(input)
   val conv1 = Convolution2D(6, 5, 5, activation = "tanh").setName("conv1_5x5").inputs(reshape)
   val pool1 = MaxPooling2D().inputs(conv1)
   val conv2 = Convolution2D(12, 5, 5, activation = "tanh").setName("conv2_5x5").inputs(pool1)
   val pool2 = MaxPooling2D().inputs(conv2)
   val flatten = Flatten().inputs(pool2)
   val fc1 = Dense(100, activation = "tanh").setName("fc1").inputs(flatten)
   val fc2 = Dense(classNum, activation = "softmax").setName("fc2").inputs(fc1)
   Model(input, fc2)
   ````

3. After that, we configure the learning process. Set the ````optimization method```` and the ````Criterion```` (which, given input and target, computes gradient per given loss function):
   ````scala
   model.compile(optimizer = optimMethod,
           loss = ClassNLLCriterion[Float](logProbAsInput = false),
           metrics = Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), new Loss[Float]))
   ````

Finally we _**train the model**_ by calling ````model.fit````:
````scala
model.fit(trainSet, nbEpoch = param.maxEpoch, validationData = validationSet)
````

---

## Python Example

#### **Initialize 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**

This tutorial describes the [Autograd](https://github.com/intel-analytics/BigDL/tree/branch-2.0/python/dllib/examples/autograd).

The example first do the initializton using `init_nncontext()`:
```python
sc = init_nncontext()
```

It then generate the input data X_, Y_

```python
data_len = 1000
X_ = np.random.uniform(0, 1, (1000, 2))
Y_ = ((2 * X_).sum(1) + 0.4).reshape([data_len, 1])
```

It then define the custom loss

```python
def mean_absolute_error(y_true, y_pred):
    result = mean(abs(y_true - y_pred), axis=1)
    return result
```

After that, the example creates the model as follows and set the criterion as the custom loss:
```python
a = Input(shape=(2,))
b = Dense(1)(a)
c = Lambda(function=add_one_func)(b)
model = Model(input=a, output=c)

model.compile(optimizer=SGD(learningrate=1e-2),
              loss=mean_absolute_error)
```
Finally the example trains the model by calling `model.fit`:

```python
model.fit(x=X_,
          y=Y_,
          batch_size=32,
          nb_epoch=int(options.nb_epoch),
          distributed=False)
```