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Using GPU for deep learning has seen a tremendous performance. It has been reported that execution time using GPU is 10x -50x times faster than CPU-based deep learning and It is also a lot cheaper than CPU-based system. You can see this below in the picture.

deep-learning-with-gpus-27-638

I was curious to check deep learning performance on my laptop which has GeForce GT 940M GPU.
Today I will walk you through how to set up GPU based deep learning machine to make use of GPUs. I have used Tensorflow for deep learning on a windows system. Using GPU in windows system is really a pain. You can’t get it to work if you don’t follow correct steps. But if you follow the steps it will be very easy to set up Tensorflow with GPU for windows.

Requirement:

Python 3.5 – Currently Tensorflow on windows doesn’t support python 2.7.
nvidia cuda GPU

Installation:

CUDA toolkit
Use this link to install cuda- https://developer.nvidia.com/cuda-downloads
According to your windows version, you can install this toolkit.
Recommended version: Cuda Toolkit 8.0
cuDNN
Use this link to install cuDNN -https://developer.nvidia.com/cudnn
You need to register to install this. You need to choose cuDNN v5.1. I have tried latest version but it didn’t work out.After downloading, You need to copy and replace these files
into this location C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0

Now you also need to set path for environment variables. Check below snapshots and make the required changes. If they are not there you have to do it manually.
Python
Install using anaconda . Use whatever anaconda python 2.7 or 3.5 you want to use for your daily tasks because we will create a separate environment for python 3.5 .
Tensorflow with GPU
Create a virtual environment for tensorflow
```
conda create --name tensorflow-gpu python=3.5  
```
Then activate this virtual environment:
```
activate tensorflow-gpu  
```
And finally, install TensorFlow with GPU support:
```
pip install tensorflow-gpu  
```
Test the TensorFlow installation
```
python  
...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!  
```
If you run into any error check below link-

https://www.tensorflow.org/install/install_windows
Any other link might lead you to different problems.
Let’s play with Tensorflow GPU
Let’s check performance on MNIST data using convolution neural network.
download the code- https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.pyNow lets run it and check its performance
GPU based Tensorflow

We can see each step is taking roughly around ~40 ms. Now we want to see if this gpu performanvce worth or not.

CPU Tensorflow

Let’s take a look at CPU performance. Really? Each step is taking ~370 ms . Wow what a performance!! Tensorflow with GPU is 10x faster than Tensorflow with CPU.

Next steps:

Further, You can install Keras library to do more advance things in deep learning. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation.Keras uses Tensorflow as backend. Keras also work seamlessly on CPU and GPU. Follow below commands. Install jupyter notebook too if you love working with notebooks.

conda install jupyter
conda install scipy pandas 
conda install mingw libpython (theano dependencies) 
conda install theano 
pip install keras

In case of any trouble, leave comments and let me know your thoughts about this articles.

Happy hunting with deep learning !!

What Is Apache Spark?

Apache Spark is an open source processing engine built around speed, ease of use, & Analytics. Apache Spark is a cluster computing platform designed to be fast and general-purpose. Spark is alternative for large amounts of data that requires low latency processing that a typical Map Reduce program cannot provide. Spark performs at speeds up to 100 times faster than Map Reduce for iterative algorithms or interactive data mining. Spark provides in-memory cluster computing for lightning fast speed and supports Java, Scala, and Python

Spark combines SQL, streaming and complex Analytics together seamlessly in the same application to handle a wide range of data processing scenarios. Spark runs on top of Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources such as HDFS, Cassandra, HBase, or S3.

At its core, Spark is a “computational engine” that is responsible for scheduling, distributing, and monitoring applications consisting of many computational tasks across many worker machines, or a computing cluster. Because the core engine of Spark is both fast and general-purpose, it powers multiple higher-level components specialized for various workloads, such as SQL or machine learning. These components are designed to inter-operate closely, letting you combine them like libraries in a software project.

Components of Spark

Spark Core Concepts

At a high level, a Spark application consists of a driver program that launches various parallel operations on a cluster. The driver program contains the main function of your application which will be then distributed to the clusters members for execution. The SparkContext object is used by the driver program to access the computing cluster. For the shell applications the SparkContext is by default available through the sc variable.

A very important concept in Spark is RDD – resilient distributed data-set. This is an immutable collection of objects. Each RDD is split into multiple partitions, which may be computed on different nodes of the cluster. RDD can contain any type of object from Java, Scala, Python or R including user-defined classes. The RDDs can be created in two ways: by loading an external data-set or by distributing a collection of objects like list or sets.

After creation we can have two types of operation on the RDDS:

Transformations – construct a new RDD from an existing one
Actions – compute a result based on an RDD

RDDs are computed in a lazy way – that is when they are used in an action. Once Spark sees a chain of transformations, it can compute just the data needed for its result. Each time an action is run on an RDD, it is recomputed. If you need the RDD for multiple actions, you can ask Spark to persist it using RDD.persist().

You can use Spark from a shell session or as a standalone program. Either way you will have the following workflow:

create input RDDs
transform them using transformations
ask Spark to persist them if needed for reuse
launch actions to start parallel computation, which is then optimized and executed by Spark

Brief intro on Logistic Regression

Logistic Regression is a classification algorithm. Classification involves looking at data and assigning a class (or a label) to it. Usually there are more than one classes, but in our example, we’ll be tackling Binary Classification, in which there at two classes: 0 or 1.

PySpark

Most importantly for us, Spark supports a Python API to write Python Spark jobs or interact with data on cluster through a shell

Steps to launch spark and python shell.

1. First step is to go to folder where spark is build writing

cd /usr/local/spark

Launch python shell:

./bin/pyspark

To Launch Ipython notebook:

IPYTHON_OPTS=”notebook –pylab inline” ./bin/pyspark

After performing IPython Command, IPython(Now its called jupyter) notebook will appear on screen. Lets get started…

you can preview the data by using

#Preview data

raw_data.take(1)

Labeled Point

A labeled point is a local vector associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms and they are stored as doubles. For binary classification, a label should be either 0 (negative) or 1 (positive).

Remember…
Spark provides specific functions to deal with RDDs which elements are key/value pairs. They are used to perform aggregations and other processings by key.

We have used the map function to create key-value pair. you can also notice Labeled Point output and that’s why Labeled Point library was imported.

References

1 Spark: http://spark.apache.org/

2 Logistic Regression (Wikipedia) : http://en.wikipedia.org/wiki/Logistic_regression

3 Link to Spark Basics :http://spark.apache.org/docs/0.9.1/quick-start.html#basics

4.Spark Logistic Regression Example :http://spark.apache.org/docs/0.9.0/mllib-guide.html

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Category: Python

Setting up a GPU based Deep Learning Machine

Building Your First Spark : Logistic Regression Model

Brief intro on Logistic Regression

PySpark

Most importantly for us, Spark supports a Python API to write Python Spark jobs or interact with data on cluster through a shell

Labeled Point