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202 lines
6.2 KiB
Markdown
202 lines
6.2 KiB
Markdown
# Image Specifics
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This page provides details about features specific to one or more images.
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## Apache Spark
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**Specific Docker Image Options**
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* `-p 4040:4040` - The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images open [SparkUI (Spark Monitoring and Instrumentation UI)](http://spark.apache.org/docs/latest/monitoring.html) at default port `4040`, this option map `4040` port inside docker container to `4040` port on host machine . Note every new spark context that is created is put onto an incrementing port (ie. 4040, 4041, 4042, etc.), and it might be necessary to open multiple ports. For example: `docker run -d -p 8888:8888 -p 4040:4040 -p 4041:4041 jupyter/pyspark-notebook`
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**Usage Examples**
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The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images support the use of [Apache Spark](https://spark.apache.org/) in Python, R, and Scala notebooks. The following sections provide some examples of how to get started using them.
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### Using Spark Local Mode
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Spark local mode is useful for experimentation on small data when you do not have a Spark cluster available.
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#### In a Python Notebook
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```python
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from pyspark.sql import SparkSession
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spark = SparkSession.builder.appName("SimpleApp").getOrCreate()
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# do something to prove it works
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spark.sql('SELECT "Test" as c1').show()
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```
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#### In a R Notebook
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```r
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library(SparkR)
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as <- sparkR.session("local[*]")
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# do something to prove it works
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df <- as.DataFrame(iris)
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head(filter(df, df$Petal_Width > 0.2))
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```
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#### In a Spylon Kernel Scala Notebook
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Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
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options in a `%%init_spark` magic cell.
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```python
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%%init_spark
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# Configure Spark to use a local master
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launcher.master = "local[*]"
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```
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```scala
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// Now run Scala code that uses the initialized SparkContext in sc
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val rdd = sc.parallelize(0 to 999)
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rdd.takeSample(false, 5)
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```
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#### In an Apache Toree Scala Notebook
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Apache Toree instantiates a local `SparkContext` for you in variable `sc` when the kernel starts.
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```scala
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val rdd = sc.parallelize(0 to 999)
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rdd.takeSample(false, 5)
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```
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### Connecting to a Spark Cluster in Standalone Mode
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Connection to Spark Cluster on Standalone Mode requires the following set of steps:
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0. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being
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deployed, run the same version of Spark.
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1. [Deploy Spark in Standalone Mode](http://spark.apache.org/docs/latest/spark-standalone.html).
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2. Run the Docker container with `--net=host` in a location that is network addressable by all of
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your Spark workers. (This is a [Spark networking
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requirement](http://spark.apache.org/docs/latest/cluster-overview.html#components).)
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* NOTE: When using `--net=host`, you must also use the flags `--pid=host -e
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TINI_SUBREAPER=true`. See https://github.com/jupyter/docker-stacks/issues/64 for details.
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#### In a Python Notebook
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```python
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import os
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# make sure pyspark tells workers to use python3 not 2 if both are installed
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os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
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import pyspark
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conf = pyspark.SparkConf()
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# Point to spark master
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conf.setMaster("spark://10.10.10.10:7070")
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# point to spark binary package in HDFS or on local filesystem on all slave
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# nodes (e.g., file:///opt/spark/spark-2.2.0-bin-hadoop2.7.tgz)
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conf.set("spark.executor.uri", "hdfs://10.10.10.10/spark/spark-2.2.0-bin-hadoop2.7.tgz")
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# set other options as desired
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conf.set("spark.executor.memory", "8g")
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conf.set("spark.core.connection.ack.wait.timeout", "1200")
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# create the context
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sc = pyspark.SparkContext(conf=conf)
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# do something to prove it works
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rdd = sc.parallelize(range(100000000))
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rdd.sumApprox(3)
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```
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#### In a R Notebook
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```r
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library(SparkR)
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# Point to spark master
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# Point to spark binary package in HDFS or on local filesystem on all worker
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# nodes (e.g., file:///opt/spark/spark-2.2.0-bin-hadoop2.7.tgz) in sparkEnvir
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# Set other options in sparkEnvir
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sc <- sparkR.session("spark://10.10.10.10:7070", sparkEnvir=list(
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spark.executor.uri="hdfs://10.10.10.10/spark/spark-2.4.3-bin-hadoop2.7.tgz",
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spark.executor.memory="8g"
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)
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)
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# do something to prove it works
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data(iris)
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df <- as.DataFrame(iris)
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head(filter(df, df$Petal_Width > 0.2))
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```
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#### In a Spylon Kernel Scala Notebook
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```python
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%%init_spark
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# Point to spark master
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launcher.master = "spark://10.10.10.10:7070"
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launcher.conf.spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.4.3-bin-hadoop2.7.tgz
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```
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```scala
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// Now run Scala code that uses the initialized SparkContext in sc
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val rdd = sc.parallelize(0 to 999)
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rdd.takeSample(false, 5)
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```
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#### In an Apache Toree Scala Notebook
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The Apache Toree kernel automatically creates a `SparkContext` when it starts based on configuration
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information from its command line arguments and environment variables. You can pass information
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about your cluster via the `SPARK_OPTS` environment variable when you spawn a container.
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For instance, to pass information about a standalone Spark master, Spark binary location in HDFS,
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and an executor options, you could start the container like so:
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```
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docker run -d -p 8888:8888 -e SPARK_OPTS='--master=spark://10.10.10.10:7070 \
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--spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.4.3-bin-hadoop2.7.tgz \
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--spark.executor.memory=8g' jupyter/all-spark-notebook
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```
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Note that this is the same information expressed in a notebook in the Python case above. Once the
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kernel spec has your cluster information, you can test your cluster in an Apache Toree notebook like
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so:
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```scala
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// should print the value of --master in the kernel spec
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println(sc.master)
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// do something to prove it works
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val rdd = sc.parallelize(0 to 99999999)
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rdd.sum()
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```
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## Tensorflow
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The `jupyter/tensorflow-notebook` image supports the use of
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[Tensorflow](https://www.tensorflow.org/) in single machine or distributed mode.
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### Single Machine Mode
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```python
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import tensorflow as tf
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hello = tf.Variable('Hello World!')
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sess = tf.Session()
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init = tf.global_variables_initializer()
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sess.run(init)
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sess.run(hello)
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```
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### Distributed Mode
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```python
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import tensorflow as tf
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hello = tf.Variable('Hello Distributed World!')
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server = tf.train.Server.create_local_server()
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sess = tf.Session(server.target)
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init = tf.global_variables_initializer()
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sess.run(init)
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sess.run(hello)
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```
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