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In 6.00, we will use the Python programming language. You'll also need an editor and/or development environment for writing and debugging your programs. In 6.00, we will be using IDLE, a programming environment specially designed for Python programming and included with the Python distribution. These programs are available for installation on your personal computers (see below).
Download Cadabra Science On Python Machine Learning
For most Unix systems, you must download and compile the source code. The same source code archive can also be used to build the Windows and Mac versions, and is the starting point for ports to all other platforms. Download the latest Python 3 and Python 2 source. Learn Data Science with Python - Part 1: Introduction to Python, will be the first step on your data science journey. You will learn the python foundations used by all data scientists to analyze and manipulate large amounts of data along with scientific computing using NumPy. Python is a widely-used programming language used in many of the computer science classes here at St. What is PIL/Pillow? PIL (Python Imaging Library) adds many image processing features to Python. Download and install Pillow for Python 2. Install Python 3.4.1 (Python 2.7 is.
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Why Python?
A programming language is the tool we use to construct a sequence of instructions that will tell the computer what we want it to do. There are hundreds of programming languages in the world. Over the course of my career, I've taught programming classes using at least six different languages.
There is no best language (though I could nominate some candidates for worst). Different languages are better or worse for different kinds of applications. MATLAB, for example, is a great language for manipulating vectors and matrices. C is a good language for writing the programs that control data networks.
In this course, we will use Python. Python is a relatively recent addition to the universe of languages, and is still growing in popularity. I want to emphasize that this course is not about Python. You will certainly learn Python, and that's a good thing. What is much more important, however, is that you will learn how to write programs that solve problems, given a set of basic primitives, and ways of combining them into more complex elements, that you can then abstract into primitives. This skill can be transferred to many languages.
Setting Up Python
You can install the 6.00 software on your personal computer if your operating system is GNU/Linux, Windows (7/XP), or MacOS X. For Windows, you will need Python version 2.5.4, while for OS X you'll need Python 2.7.11 (any 2.5.x, 2.6.x, or 2.7.x version of Python will work, but 3.0 versions are NOT compatible). Below are direct links to the most common Python installers:
Windows:
Download and install: Windows Installer
Mac OS X:
For OS X Yosemite and earlier, download and install: Mac Installer. For OS X El Capitan and later, download and install Anaconda with Python 2.7 for OS X. Anaconda comes with numpy and matplotlib preinstalled, along with many other python libraries, so you may ignore the installation instructions for those libraries in Pset 6.
Warning: On the Python homepage, the latest version available for download is actually 3.5. Do not install this! This version is not backwards compatible with the code that you'll be writing in this course (for example, you have to type print('test') instead of print 'test'). Instead, be sure to download the version listed above.
Using IDLE
IDLE is the standard Python development environment. Its name is an acronym of 'Integrated DeveLopment Environment'. It works well on both Unix and Windows platforms.
It has a Python shell window, which gives you access to the Python interactive mode. It also has a file editor that lets you create and edit existing Python source files.
During the following discussion of IDLE's features, instead of passively reading along, you should start IDLE and try to replicate the actions.
You can type Python code directly into this shell, at the '>>>' prompt. Whenever you enter a complete code fragment, it will be executed. For instance, typing:
>>> print'hello world'
and pressing Enter will cause the following to be displayed:
hello world
IDLE can also be used as a calculator:
>>> 4+4
8
Addition, subtraction, and multiplication operators are built into the Python language. This means you can use them right away. If you want to use a square root in your calculation, you need to import the math module. Do not worry about what it means right now; we will cover this later during the course. Below is an example of square root calculation:
>>> import math
>>> math.sqrt(16)
4.0
Math module allows you to do a number of useful operations:
>>> import math
>>> math.pow(3, 2)
9.0
>>> math.cos( 0 )
1.0
Note that you only need to execute the import command once after you start IDLE.
Exercises
For additional practice, try using IDLE to calculate:
- 23.0 to the 5th power
- Positive root of the following equation:
34*x^2 + 68*x - 510
Recall:
a*x^2 + b*x + c
x1 = ( - b + sqrt ( b*b - 4*a*c ) ) / ( 2*a )
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Python is one of the most popular programming languages today for science, engineering, data analytics and deep learning applications. However, as an interpreted language, it has been considered too slow for high-performance computing.
Numba, a Python compiler from Anaconda that can compile Python code for execution on CUDA-capable GPUs, provides Python developers with an easy entry into GPU-accelerated computing and a path for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon. With CUDA Python and Numba, you get the best of both worlds: rapid iterative development with Python combined with the speed of a compiled language targeting both CPUs and NVIDIA GPUs.
SETUP CUDA PYTHON
To run CUDA Python, you will need the CUDA Toolkit installed on a system with CUDA capable GPUs. Use this guide for easy steps to install CUDA. If you do not have a CUDA-capable GPU, you can access one of the thousands of GPUs available from cloud service providers including Amazon AWS, Microsoft Azure and IBM SoftLayer. The NVIDIA-maintained CUDA Amazon Machine Image (AMI) on AWS, for example, comes pre-installed with CUDA and is available for use today.
To get started with Numba, the first step is to download and install the Anaconda python distribution that includes many popular packages (Numpy, Scipy, Matplotlib, iPython, etc) and “conda”, a powerful package manager. Once you have Anaconda installed, install the required CUDA packages by typing conda install numba cudatoolkit pyculib.
GET STARTED WITH CUDA PYTHON
The blog post Numba: High-Performance Python with CUDA Acceleration is a great resource to get you started. Also refer to the Numba tutorial for CUDA on the ContinuumIO github repository and the Numba posts on Anaconda’s blog.
If you are new to Python, explore the beginner section of the Python website for some excellent getting started resources. The blog, An Even Easier Introduction to CUDA, introduces key CUDA concepts through simple examples.
Check out Numbas github repository for additional examples to practice. You can also get the full Jupyter Notebook for the Mandelbrot example on Github.
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ADDITIONAL RESOURCES
The developer blog posts, Seven things you might not know about Numba and GPU-Accelerated Graph Analytics in Python with Numba provide additional insights into GPU Computing with python.
NVIDIA also provides hands-on training through a collection of self-paced courses and instructor-led workshops. The courses guide you step-by-step through editing and execution of code and interaction with visualization tools, woven together into a simple immersive experience. Practice the techniques you learned in the materials above through hands-on content.
NVIDIA DLIDownload Cadabra Science On Python Macintosh
You can register for free access to NVIDIA TESLA GPUs in the cloud to deploy your python applications once they are ready.
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