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Fast Data: Data Analytics with JupyterLab, Spark and TensorFlow

In this demo we will assume the role of a Data Scienctist and use JupyterLab to preprocess and analyze datasets with Spark and TensorFlow. The technologies used in the demo are as follows:

JupyterLab
HDFS
Apache Spark
TensorFlow using the Yahoo TensorFlowOnSpark distribution
Marathon-LB to expose JupyterLab externally
Estimated time for completion:
Manual install: 20min
Target audience: Anyone interested in Data Analytics.

Table of Contents:

Prerequisites
Install
Use the demo

Prerequisites

A running DC/OS 1.11 or higher cluster with at least 6 private agents and 1 public agent. Each agent should have 2 CPUs and 5 GB of RAM available. The DC/OS CLI also needs to be installed. If you plan to use GPU support we recommend to use the dcos-terraform project to provision DC/OS. Please refer to the the GPU Cluster Provisioning section in the README for more details.

Install

HDFS

You can install the HDFS service from the DC/OS UI or directly from the CLI:

$ dcos package install hdfs

Note that if you want to learn more about HDFS Service or advanced installation options, you can check out the HDFS Service Docs.

Marathon-LB

In order to expose JupyterLab externally we install Marathon-LB using

$ dcos package install marathon-lb

JupyterLab

We can install Jupyterlab also from the UI or CLI. In both cases we need to change two parameter;

First the VHOST for exposing the service on a public agent externally.

This means changing the networking.external_access.external_public_agent_hostname to the externally reachable VHOST (e.g., the Public Agent ELB in an AWS environment). We can check that using the UI:

Or using a jupyterlab_options.json file where we need to configure the following setting;

    "external_access": {
      "enabled": true,
      "external_public_agent_hostname": "<ADD YOUR VHOST NAME HERE *WITHOUT* trailing / NOR http://>"
    }

Secondly, as we want to access datasets in HDFS during this demo, we need to configure access to HDFS by exposing the necessary config files from the previously installed HDFS.

Again this can be done with the UI install:

Or using the jupyterlab_options.json with the following setting:

    "jupyter_conf_urls": "http://api.hdfs.marathon.l4lb.thisdcos.directory/v1/endpoints",

Furthermore for better monitoring and debugging, we can enable the following two parameter either via the UI or in jupyterlab_options.json:

    "start_spark_history_server": true,
    "start_tensorboard": true,

After configuring those settings we can install JupyterLab either with by clicking Run Service in the UI:

Or with the CLI and dcos package install jupyterlab --options=jupyterlab_options.json.

For more options of installing Jupyterlab please refer to the installation section in the README.

Demo

Login

The first step is to login into JupyterLab. If we have used the default name and VHOST setting above it should be reachable via <VHOST>/jupyterlab-notebook. .

The default password witht he above settings is jupyter.

Once logged in you should be able to see the JupyterLab Launcher: .

SparkPi Job

As a first test let us run the SparkPi example job.

For this we simply launch a Terminal from inside the notebook and then use the following command:

eval \
  spark-submit \
  ${SPARK_OPTS} \
  --verbose \
  --class org.apache.spark.examples.SparkPi \
  /opt/spark/examples/jars/spark-examples_2.11-2.2.1.jar 100

You should then see Spark spinning up tasks and computing PI. If you want, you can check the Mesos UI via <cluster>/mesos and see the Spark tasks being spawned there.

Once the Spark job has finished you should be able to see output similar to Pi is roughly 3.1416119141611913 (followed by the Spark teardown log messages).

SparkPi with Apache Toree

Let us run the SparkPi example as well directly from a Apache Toree notebook. So launch a new notebook with an Apache Toree Scala Kernel and use the below Scala code to compute Pi once more:

val NUM_SAMPLES = 10000000

val count2 = spark.sparkContext.parallelize(1 to NUM_SAMPLES).map{i =>
  val x = Math.random()
  val y = Math.random()
  if (x*x + y*y < 1) 1 else 0
}.reduce(_ + _)

println("Pi is roughly " + 4.0 * count2 / NUM_SAMPLES)

[Optional for GPU enabled JupyterLab] Check available GPUs

Launch a new notebook with Python 3 Kernel and use the following python code to show the available GPUs.

from tensorflow.python.client import device_lib

def get_available_devices():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos]

print(get_available_devices())

MNIST TensorFlowOnSpark

Next let us use TensorFlowOnSpark and the MNIST database to train a network recognizing handwritten digits.

Clone the Yahoo TensorFlowOnSpark Github Repo using the notebook's Terminal:

git clone https://github.com/yahoo/TensorFlowOnSpark

Retrieve and extract raw MNIST Dataset using the notebook's Terminal:

cd $MESOS_SANDBOX
curl -fsSL -O https://s3.amazonaws.com/vishnu-mohan/tensorflow/mnist/mnist.zip
unzip mnist.zip

Check HDFS

Let us briefly check HDFS is working as expected and the mnist directory does not exist yet from the notebook's Terminal:

hdfs dfs -ls  mnist/
ls: `mnist/': No such file or directory

Prepare MNIST Dataset in CSV format and store on HDFS from the notebook's Terminal

eval \
  spark-submit \
  ${SPARK_OPTS} \
  --verbose \
  $(pwd)/TensorFlowOnSpark/examples/mnist/mnist_data_setup.py \
    --output mnist/csv \
    --format csv

Check for mnist directory in HDFS from notebook's Terminal

hdfs dfs -ls -R  mnist
/drwxr-xr-x   - nobody supergroup          0 2018-08-08 01:33 mnist/csv
drwxr-xr-x   - nobody supergroup          0 2018-08-08 01:33 mnist/csv/test
drwxr-xr-x   - nobody supergroup          0 2018-08-08 01:33 mnist/csv/test/images
-rw-r--r--   3 nobody supergroup          0 2018-08-08 01:33 mnist/csv/test/images/_SUCCESS
-rw-r--r--   3 nobody supergroup    1810248 2018-08-08 01:33 mnist/csv/test/images/part-00000
-rw-r--r--   3 nobody supergroup    1806102 2018-08-08 01:33 mnist/csv/test/images/part-00001
-rw-r--r--   3 nobody supergroup    1811128 2018-08-08 01:33 mnist/csv/test/images/part-00002
-rw-r--r--   3 nobody supergroup    1812952 2018-08-08 01:33 mnist/csv/test/images/part-00003
-rw-r--r--   3 nobody supergroup    1810946 2018-08-08 01:33 mnist/csv/test/images/part-00004
-rw-r--r--   3 nobody supergroup    1835497 2018-08-08 01:33 mnist/csv/test/images/part-00005
...

Train MNIST model with CPUs from the notebook's Terminal

eval \
  spark-submit \
  ${SPARK_OPTS} \
  --verbose \
  --conf spark.mesos.executor.docker.image=dcoslabs/dcos-jupyterlab:1.2.0-0.33.7 \
  --py-files $(pwd)/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
  $(pwd)/TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
  --cluster_size 5 \
  --images mnist/csv/train/images \
  --labels mnist/csv/train/labels \
  --format csv \
  --mode train \
  --model mnist/mnist_csv_model

If you want to use GPUs you can use (but make sure the cluster size matches the number of GPU instances)

eval \
  spark-submit \
  ${SPARK_OPTS} \
  --verbose \
  --conf spark.mesos.executor.docker.image=dcoslabs/dcos-jupyterlab:1.2.0-0.33.7-gpu \
  --conf spark.mesos.gpus.max=2 \
  --conf spark.mesos.executor.gpus=1 \
  --py-files $(pwd)/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
  $(pwd)/TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
  --cluster_size 2 \
  --images mnist/csv/train/images \
  --labels mnist/csv/train/labels \
  --format csv \
  --mode train \
  --model mnist/mnist_csv_model

As we configured TensorBoard to be enabled, we can go to <VHOST>/jupyterlab-notebook/tensorboard and check the Training progress.

Check if trained model exists on HDFS using the notebook's Terminal

nobody@2442bc8f-94d4-4f74-8321-b8b8b40436d7:~$ hdfs dfs -ls -R mnist/mnist_csv_model
-rw-r--r--   3 nobody supergroup        128 2018-08-08 02:37 mnist/mnist_csv_model/checkpoint
-rw-r--r--   3 nobody supergroup    4288367 2018-08-08 02:37 mnist/mnist_csv_model/events.out.tfevents.1533695777.ip-10-0-7-250.us-west-2.compute.internal
-rw-r--r--   3 nobody supergroup         40 2018-08-08 02:36 mnist/mnist_csv_model/events.out.tfevents.1533695778.ip-10-0-7-250.us-west-2.compute.internal
-rw-r--r--   3 nobody supergroup     156424 2018-08-08 02:36 mnist/mnist_csv_model/graph.pbtxt
-rw-r--r--   3 nobody supergroup     814168 2018-08-08 02:36 mnist/mnist_csv_model/model.ckpt-0.data-00000-of-00001
-rw-r--r--   3 nobody supergroup        408 2018-08-08 02:36 mnist/mnist_csv_model/model.ckpt-0.index
-rw-r--r--   3 nobody supergroup      69583 2018-08-08 02:36 mnist/mnist_csv_model/model.ckpt-0.meta
-rw-r--r--   3 nobody supergroup     814168 2018-08-08 02:37 mnist/mnist_csv_model/model.ckpt-600.data-00000-of-00001
-rw-r--r--   3 nobody supergroup        408 2018-08-08 02:37 mnist/mnist_csv_model/model.ckpt-600.index
-rw-r--r--   3 nobody supergroup      74941 2018-08-08 02:37 mnist/mnist_csv_model/model.ckpt-600.meta

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README.md

Fast Data: Data Analytics with JupyterLab, Spark and TensorFlow

Prerequisites

Install

HDFS

Marathon-LB

JupyterLab

Demo

Login

SparkPi Job

SparkPi with Apache Toree

[Optional for GPU enabled JupyterLab] Check available GPUs

MNIST TensorFlowOnSpark

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parent directory

README.md

Fast Data: Data Analytics with JupyterLab, Spark and TensorFlow

Prerequisites

Install

HDFS

Marathon-LB

JupyterLab

Demo

Login

SparkPi Job

SparkPi with Apache Toree

[Optional for GPU enabled JupyterLab] Check available GPUs

MNIST TensorFlowOnSpark