A Curated List of Awesome Python Frameworks, Libraries, Software and Resources 30/04/2018, 10 16
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Ubuntu Kung Fu
Prepared exclusively for Alison Tyler Download at Boykma.Com What readers are saying about Ubuntu Kung Fu Ubuntu Kung Fu is excellent. The tips are fun and the hope of discov- ering hidden gems makes it a worthwhile task. John Southern Former editor of Linux Magazine I enjoyed Ubuntu Kung Fu and learned some new things. I would rec- ommend this book—nice tips and a lot of fun to be had. Carthik Sharma Creator of the Ubuntu Blog (http://ubuntu.wordpress.com) Wow! There are some great tips here! I have used Ubuntu since April 2005, starting with version 5.04. I found much in this book to inspire me and to teach me, and it answered lingering questions I didn’t know I had. The book is a good resource that I will gladly recommend to both newcomers and veteran users. Matthew Helmke Administrator, Ubuntu Forums Ubuntu Kung Fu is a fantastic compendium of useful, uncommon Ubuntu knowledge. Eric Hewitt Consultant, LiveLogic, LLC Prepared exclusively for Alison Tyler Download at Boykma.Com Ubuntu Kung Fu Tips, Tricks, Hints, and Hacks Keir Thomas The Pragmatic Bookshelf Raleigh, North Carolina Dallas, Texas Prepared exclusively for Alison Tyler Download at Boykma.Com Many of the designations used by manufacturers and sellers to distinguish their prod- ucts are claimed as trademarks. Where those designations appear in this book, and The Pragmatic Programmers, LLC was aware of a trademark claim, the designations have been printed in initial capital letters or in all capitals. The Pragmatic Starter Kit, The Pragmatic Programmer, Pragmatic Programming, Pragmatic Bookshelf and the linking g device are trademarks of The Pragmatic Programmers, LLC. -
Python on Gpus (Work in Progress!)
Python on GPUs (work in progress!) Laurie Stephey GPUs for Science Day, July 3, 2019 Rollin Thomas, NERSC Lawrence Berkeley National Laboratory Python is friendly and popular Screenshots from: https://www.tiobe.com/tiobe-index/ So you want to run Python on a GPU? You have some Python code you like. Can you just run it on a GPU? import numpy as np from scipy import special import gpu ? Unfortunately no. What are your options? Right now, there is no “right” answer ● CuPy ● Numba ● pyCUDA (https://mathema.tician.de/software/pycuda/) ● pyOpenCL (https://mathema.tician.de/software/pyopencl/) ● Rewrite kernels in C, Fortran, CUDA... DESI: Our case study Now Perlmutter 2020 Goal: High quality output spectra Spectral Extraction CuPy (https://cupy.chainer.org/) ● Developed by Chainer, supported in RAPIDS ● Meant to be a drop-in replacement for NumPy ● Some, but not all, NumPy coverage import numpy as np import cupy as cp cpu_ans = np.abs(data) #same thing on gpu gpu_data = cp.asarray(data) gpu_temp = cp.abs(gpu_data) gpu_ans = cp.asnumpy(gpu_temp) Screenshot from: https://docs-cupy.chainer.org/en/stable/reference/comparison.html eigh in CuPy ● Important function for DESI ● Compared CuPy eigh on Cori Volta GPU to Cori Haswell and Cori KNL ● Tried “divide-and-conquer” approach on both CPU and GPU (1, 2, 5, 10 divisions) ● Volta wins only at very large matrix sizes ● Major pro: eigh really easy to use in CuPy! legval in CuPy ● Easy to convert from NumPy arrays to CuPy arrays ● This function is ~150x slower than the cpu version! ● This implies there -
The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 Minutes)
EuroPython 2007 Contribution ID: 62 Type: not specified The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 minutes) This is a re-worked, actualized and improved version of my talk at PyCon 2007. Repeating the abstract: As a surprise for people who think they know Stackless, we present the new Stackless implementation For PyPy, which has led to a significant amount of new insight about parallel programming and its possible implementations. We will isolate the known Stackless as a special case of a general concept. This is a Stackless, not a PyPy talk. But the insights presented here would not exist without PyPy’s existance. Summary Stackless has been around for a long time now. After several versions with different goals in mind, the basic concepts of channels and tasklets turned out to be useful abstractions, and since many versions, Stackless is only ported from version to version, without fundamental changes to the principles. As some spin-off, Armin Rigo invented Greenlets at a Stackless sprint. They are some kind of coroutines and a bit of special semantics. The major benefit is that Greenlets can runon unmodified CPython. In parallel to that, the PyPy project is in its fourth year now, and one of its goals was Stackless integration as an option. And of course, Stackless has been integrated into PyPy in a very nice and elegant way, much nicer than expected. During the design of the Stackless extension to PyPy, it turned out, that tasklets, greenlets and coroutines are not that different in principle, and it was possible to base all known parallel paradigms on one simple coroutine layout, which is as minimalistic as possible. -
An Implementation of Python for Racket
An Implementation of Python for Racket Pedro Palma Ramos António Menezes Leitão INESC-ID, Instituto Superior Técnico, INESC-ID, Instituto Superior Técnico, Universidade de Lisboa Universidade de Lisboa Rua Alves Redol 9 Rua Alves Redol 9 Lisboa, Portugal Lisboa, Portugal [email protected] [email protected] ABSTRACT Keywords Racket is a descendent of Scheme that is widely used as a Python; Racket; Language implementations; Compilers first language for teaching computer science. To this end, Racket provides DrRacket, a simple but pedagogic IDE. On the other hand, Python is becoming increasingly popular 1. INTRODUCTION in a variety of areas, most notably among novice program- The Racket programming language is a descendent of Scheme, mers. This paper presents an implementation of Python a language that is well-known for its use in introductory for Racket which allows programmers to use DrRacket with programming courses. Racket comes with DrRacket, a ped- Python code, as well as adding Python support for other Dr- agogic IDE [2], used in many schools around the world, as Racket based tools. Our implementation also allows Racket it provides a simple and straightforward interface aimed at programs to take advantage of Python libraries, thus signif- inexperienced programmers. Racket provides different lan- icantly enlarging the number of usable libraries in Racket. guage levels, each one supporting more advanced features, that are used in different phases of the courses, allowing Our proposed solution involves compiling Python code into students to benefit from a smoother learning curve. Fur- semantically equivalent Racket source code. For the run- thermore, Racket and DrRacket support the development of time implementation, we present two different strategies: additional programming languages [13]. -
A Modern Introduction to Programming
Eloquent JavaScript A Modern Introduction to Programming Marijn Haverbeke Copyright © 2014 by Marijn Haverbeke This work is licensed under a Creative Commons attribution-noncommercial license (http://creativecommons.org/licenses/by-nc/3.0/). All code in the book may also be considered licensed under an MIT license (http://opensource. org/licenses/MIT). The illustrations are contributed by various artists: Cover by Wasif Hyder. Computer (introduction) and unicycle people (Chapter 21) by Max Xiantu. Sea of bits (Chapter 1) and weresquirrel (Chapter 4) by Margarita Martínez and José Menor. Octopuses (Chapter 2 and 4) by Jim Tierney. Object with on/off switch (Chapter 6) by Dyle MacGre- gor. Regular expression diagrams in Chapter 9 generated with regex- per.com by Jeff Avallone. Game concept for Chapter 15by Thomas Palef. Pixel art in Chapter 16 by Antonio Perdomo Pastor. The second edition of Eloquent JavaScript was made possible by 454 financial backers. You can buy a print version of this book, with an extra bonus chapter included, printed by No Starch Press at http://www.amazon.com/gp/product/ 1593275846/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN= 1593275846&linkCode=as2&tag=marijhaver-20&linkId=VPXXXSRYC5COG5R5. i Contents On programming .......................... 2 Why language matters ....................... 5 What is JavaScript? ......................... 9 Code, and what to do with it ................... 11 Overview of this book ........................ 12 Typographic conventions ...................... 14 1 Values, Types, and Operators 15 Values ................................. 16 Numbers ............................... 17 Strings ................................ 21 Unary operators ........................... 22 Boolean values ............................ 23 Undefined values ........................... 26 Automatic type conversion ..................... 27 Summary ............................... 30 2 Program Structure 32 Expressions and statements .................... 32 Variables .............................. -
Introduction Shrinkage Factor Reference
Comparison study for implementation efficiency of CUDA GPU parallel computation with the fast iterative shrinkage-thresholding algorithm Younsang Cho, Donghyeon Yu Department of Statistics, Inha university 4. TensorFlow functions in Python (TF-F) Introduction There are some functions executed on GPU in TensorFlow. So, we implemented our algorithm • Parallel computation using graphics processing units (GPUs) gets much attention and is just using that functions. efficient for single-instruction multiple-data (SIMD) processing. 5. Neural network with TensorFlow in Python (TF-NN) • Theoretical computation capacity of the GPU device has been growing fast and is much higher Neural network model is flexible, and the LASSO problem can be represented as a simple than that of the CPU nowadays (Figure 1). neural network with an ℓ1-regularized loss function • There are several platforms for conducting parallel computation on GPUs using compute 6. Using dynamic link library in Python (P-DLL) unified device architecture (CUDA) developed by NVIDIA. (Python, PyCUDA, Tensorflow, etc. ) As mentioned before, we can load DLL files, which are written in CUDA C, using "ctypes.CDLL" • However, it is unclear what platform is the most efficient for CUDA. that is a built-in function in Python. 7. Using dynamic link library in R (R-DLL) We can also load DLL files, which are written in CUDA C, using "dyn.load" in R. FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) We consider FISTA (Beck and Teboulle, 2009) with backtracking as the following: " Step 0. Take �! > 0, some � > 1, and �! ∈ ℝ . Set �# = �!, �# = 1. %! Step k. � ≥ 1 Find the smallest nonnegative integers �$ such that with �g = � �$&# � �(' �$ ≤ �(' �(' �$ , �$ . -
Method, 224 Vs. Typing Module Types
Index A migration metadata, 266 running migrations, 268, 269 Abstract base classes, 223 setting up a new project, 264 @abc.abstractmethod decorator, 224 using constants in migrations, 267 __subclasshook__(...) method, 224 __anext__() method, 330 vs. typing module types, 477 apd.aggregation package, 397, 516, 524 virtual subclasses, 223 clean functions (see clean functions) Actions, 560 database, 254 analysis process, 571, 572 get_data_by_deployment(...) config file, 573, 575 function, 413, 416 DataProcessor class, 561 get_data(...) function, 415 extending, 581 plot_sensor(...) function, 458 IFTTT service, 566 plotting data, 429 ingesting data, 567, 571 plotting functions, 421 logs, 567 query functions, 417 trigger, 563, 564 apd.sensors package, 106 Adafruit, 42, 486 APDSensorsError, 500 Adapter pattern, 229 DataCollectionError, 500 __aenter__() method, 331, 365 directory structure, 108 __aexit__(...) method, 331, 365 extending, 149 Aggregation process, 533 IntermittentSensorFailureError, 500 aiohttp library, 336 making releases, 141 __aiter__() method, 330 sensors script, 32, 36, 130, 147, 148, Alembic, 264 153, 155, 156, 535 ambiguous changes, 268 UserFacingCLIError, 502 creating a new revision, 265 apd.sunnyboy_solar package, 148, current version, 269 155, 173 downgrading, 268, 270 API design, 190 irreversible, migrations, 270 authentication, 190 listing migrations, 269 versioning, 240, 241, 243 merging, migrations, 269 589 © Matthew Wilkes 2020 M. Wilkes, Advanced Python Development, https://doi.org/10.1007/978-1-4842-5793-7 INDEX AssertionError, -
Python Concurrency
Python Concurrency Threading, parallel and GIL adventures Chris McCafferty, SunGard Global Services Overview • The free lunch is over – Herb Sutter • Concurrency – traditionally challenging • Threading • The Global Interpreter Lock (GIL) • Multiprocessing • Parallel Processing • Wrap-up – the Pythonic Way Reminder - The Free Lunch Is Over How do we get our free lunch back? • Herb Sutter’s paper at: • http://www.gotw.ca/publications/concurrency-ddj.htm • Clock speed increase is stalled but number of cores is increasing • Parallel paths of execution will reduce time to perform computationally intensive tasks • But multi-threaded development has typically been difficult and fraught with danger Threading • Use the threading module, not thread • Offers usual helpers for making concurrency a bit less risky: Threads, Locks, Semaphores… • Use logging, not print() • Don’t start a thread in module import (bad) • Careful importing from daemon threads Traditional management view of Threads Baby pile of snakes, Justin Guyer Managing Locks with ‘with’ • With keyword is your friend • (compare with the ‘with file’ idiom) import threading rlock = threading.RLock() with rlock: print "code that can only be executed while we acquire rlock" #lock is released at end of code block, regardless of exceptions Atomic Operations in Python • Some operations can be pre-empted by another thread • This can lead to bad data or deadlocks • Some languages offer constructs to help • CPython has a set of atomic operations due to the operation of something called the GIL and the way the underlying C code is implemented • This is a fortuitous implementation detail – ideally use RLocks to future-proof your code CPython Atomic Operations • reading or replacing a single instance attribute • reading or replacing a single global variable • fetching an item from a list • modifying a list in place (e.g. -
Introduction to Docker Version: A2622f1
Introduction to Docker Version: a2622f1 An Open Platform to Build, Ship, and Run Distributed Applications Docker Fundamentals a2622f1 1 © 2015 Docker Inc Logistics • Updated copy of the slides: http://lisa.dckr.info/ • I'm Jérôme Petazzoni • I work for Docker Inc. • You should have a little piece of paper, with your training VM IP address + credentials • Can't find the paper? Come get one here! • We will make a break halfway through • Don't hesitate to use the LISA Slack (#docker channel) • This will be fast-paced, but DON'T PANIC • To contact me: [email protected] / Twitter: @jpetazzo Those slides were made possible by Leon Licht, Markus Meinhardt, Ninette, Yetti Messner, and a plethora of other great artist of the Berlin techno music scene, alongside with what is probably an unhealthy amount of Club Mate. Docker Fundamentals a2622f1 2 © 2015 Docker Inc Part 1 • About Docker • Your training Virtual Machine • Install Docker • Our First Containers • Background Containers • Restarting and Attaching to Containers • Understanding Docker Images • Building Docker images • A quick word about the Docker Hub Docker Fundamentals a2622f1 3 © 2015 Docker Inc Part 2 • Naming and inspecting containers • Container Networking Basics • Local Development Work flow with Docker • Working with Volumes • Connecting Containers • Ambassadors • Compose For Development Stacks Docker Fundamentals a2622f1 4 © 2015 Docker Inc Extra material • Advanced Dockerfiles • Security • Dealing with Vulnerabilities • Securing Docker with TLS • The Docker API Docker Fundamentals -
Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python
Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python GPU-Accelerated Computing NVIDIA® CUDA® technology Why Use Python with GPUs? Agenda Methods: PyCUDA, Numba, CuPy, and scikit-cuda Summary Q&A 2 Introduction to Python Released by Guido van Rossum in 1991 The Zen of Python: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Interpreted language (CPython, Jython, ...) Dynamically typed; based on objects 3 Introduction to Python Small core structure: ~30 keywords ~ 80 built-in functions Indentation is a pretty serious thing Dynamically typed; based on objects Binds to many different languages Supports GPU acceleration via modules 4 Introduction to Python 5 Introduction to Python 6 Introduction to Python 7 GPU-Accelerated Computing “[T]the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications” (NVIDIA) Most common GPU-accelerated operations: Large vector/matrix operations (Basic Linear Algebra Subprograms - BLAS) Speech recognition Computer vision 8 GPU-Accelerated Computing Important concepts for GPU-accelerated computing: Host ― the machine running the workload (CPU) Device ― the GPUs inside of a host Kernel ― the code part that runs on the GPU SIMT ― Single Instruction Multiple Threads 9 GPU-Accelerated Computing 10 GPU-Accelerated Computing 11 CUDA Parallel computing -
Coala Documentation Release 0.4.1.Dev0
coala Documentation Release 0.4.1.dev0 The coala Developers February 16, 2016 Home i ii CHAPTER 1 coala Installation This document contains information on how to install coala. Supported platforms are Linux and Windows. coala is known to work on OS X as well. coala is tested against CPython 3.3, 3.4 and 3.5. In order to run coala you need to install Python. It is recommended, that you install Python3 >= 3.3 from http://www.python.org. The easiest way to install coala is using pip (Pip Installs Packages). If you don’t already have pip, you can install it like described on https://pip.pypa.io/en/stable/installing.html. Note that pip is shipped with recent python versions by default. 1.1 System wide installation The simplest way to install coa ais to do it system-wide. But, This is generally discouraged in favor or using a virtualenv. To install the latest most stable version of coala system-wide, use: $ pip3 install coala To install the nightly build from our master branch, you can do: Note: For this and all future steps, some steps require root access (also known as administrative privileges in Win- dows). Unix based (Max, Linux) - This can be achieved by using sudo in front of the command sudo command_name instead of command_name Windows - The easiest way on windows is to start a command prompt as an administrator and start setup.py. $ pip3 install coala --pre 1.2 Installing inside a virtualenv Virtualenv is probably what you want to use during development, you’ll probably want to use it there, too. -
Sphinx Documentation Release 1.5.6
Sphinx Documentation Release 1.5.6 Georg Brandl Nov 13, 2017 Contents 1 Introduction 1 1.1 Conversion from other systems....................................1 1.2 Use with other systems........................................2 1.3 Prerequisites..............................................2 1.4 Usage..................................................2 2 First Steps with Sphinx 3 2.1 Install Sphinx..............................................3 2.2 Setting up the documentation sources................................3 2.3 Defining document structure.....................................4 2.4 Adding content.............................................5 2.5 Running the build...........................................5 2.6 Documenting objects..........................................5 2.7 Basic configuration...........................................6 2.8 Autodoc.................................................7 2.9 Intersphinx...............................................7 2.10 More topics to be covered.......................................8 3 Man Pages 9 3.1 Core Applications...........................................9 3.2 Additional Applications........................................ 15 4 reStructuredText Primer 19 4.1 Paragraphs............................................... 19 4.2 Inline markup.............................................. 19 4.3 Lists and Quote-like blocks...................................... 20 4.4 Source Code............................................... 21 4.5 Tables.................................................