adekunleba

val split :RDD[String] = rdd.flatMap(_.split(" "))
val trim :RDD[String] = split.map(_.trim.toLowerCase)
val stopwordsRemoved = trim.filter( x => !Set("and", "the", "is", "to", "she", "he").contains(x))
val assignOne = stopwordsRemoved.map((_, 1))
val counts = assignOne.reduceByKey(_ + _)

adekunleba

val streamingContext: StreamingContext = new StreamingContext(sparkContext, Seconds(20))
val lines: ReceiverInputDStream[String] = streamingContext.socketTextStream("localhost", 9999)

adekunleba

val spark: SparkSession = SparkSession.builder()
    .master("local[*]")
    .appName("simple-app")
    .getOrCreate()
    
val dataSet: Dataset[String] = spark.read.textFile("textfile.csv")

val df: DataFrame = dataSet.toDF()

adekunleba

val conf = new SparkConf().setMaster("local[*").setAppName("simple-app")
val sparkContext = new SparkContext(conf)


//Loading data with Spark Context returns an RDD
val rdd: RDD[String] = sparkContext.textFile("textfile.csv")


//Also you can create an RDD by parallizing an existing Data
val data: Array[Int] = Array(1, 2, 3, 4, 5, 6, 6, 7, 7)

adekunleba

 dataset = generate(tfrecordfiles)
 
IM_SIZE = 224 # image size
image_input = tf.keras.Input(shape=(IM_SIZE, IM_SIZE, 3), name='input_layer')
# Some convolutional layers
conv_1 = tf.keras.layers.Conv2D(32,
                                kernel_size=(3, 3),
                                padding='same',
                                activation='relu')(image_input)
conv_1 = tf.keras.layers.MaxPooling2D(padding='same')(conv_1)

adekunleba

scala> val examplelist = List(1, 2, 3, 4, 5)
examplelist: List[Int] = List(1, 2, 3, 4, 5)


scala> examplelist.map(x => List(x*x))
res1: List[List[Int]] = List(List(1), List(4), List(9), List(16), List(25))


scala> examplelist.flatMap(x => List(x*x))
res4: List[Int] = List(1, 4, 9, 16, 25)

adekunleba

resource_field = {"Message": fields.String,
                  "Image_Embedding": fields.List(fields.String),
                   "ID_Embedding": fields.List(fields.String),
                   "Confidence": fields.String,
                   "Round Trip Time": fields.String,
                   "Process First Image Time": fields.String,
                   "Process Second Image Time": fields.String,
                   "Generate and Compare Time": fields.String}

adekunleba

resource_field = {"Message": fields.String,
                  "Image_Embedding": fields.List(fields.String),
                   "ID_Embedding": fields.List(fields.String),
                   "Confidence": fields.String,
                   "Round Trip Time": fields.String,
                   "Process First Image Time": fields.String,
                   "Process Second Image Time": fields.String,
                   "Generate and Compare Time": fields.String}

adekunleba

docker run -p 8500:8500 --mount type=bind,source=/home/sfx/models/,target=/models/2 -e MODEL_NAME=2 -t tensorflow/serving
#please change the /home/sfx/models  absolute path of model on your server

adekunleba

docker run -p 8501:8501 --mount type=bind,source=/path/to/my_model/,target=/models/my_model \
-e MODEL_NAME=my_model -t tensorflow/serving