Download - PyData Boston 2013
Continuum Analytics: What we are doing and why
Travis Oliphant, PhDContinuum Analytics, Inc
This talk will be about (mostly) the free and /or open source software we are building.
Enterprise
Python
Scientific
Computing
Data Processing
Data Analysis
Visualisation
Scalable
Computing
• Products• Training• Support• Consulting
Began operations in January of 2012 with 5 people with big dreams
Python
We are big backers of NumFOCUS and organizers of PyData
Spyder
Our TeamScientists Developers
+5 contractors+4 interns
+1 Part-time
Business
Big Picture
expertise
insights
NumPy and SciPy are quite successful
Thanks to large and diverse community around it (Matplotlib, IPython, SymPy, Pandas, etc.)I estimate 1.5 million to 2 million usersOnly incremental improvements possible with these projects at this point.
Thus, we needed to start new projects...
Related Open Source Projects
Blaze: High-performance Python library for modern vector computing, distributed and streaming data
Numba: Vectorizing Python compiler for multicore and GPU, using LLVM
Bokeh: Interactive, grammar-based visualization system for large datasets
Common Thread: High-level, expressive language for domain experts; innovative compilers & runtimes for efficient, powerful data transformation
Conda and Anaconda
• Cross-platform package management• Multiple environments allows you to have multiple
versions of packages installed in system• Easy app-deployment• Taming open-source
Free for all users
Enterprise support available!
Why Conda?
• Linux users stopped complaining about Python deployment
• I made major mistakes in management of NumPy/SciPy: • too much in SciPy (SciPy as distribution) --- scikits model is much
better (tighter libraries developed by smaller teams)• gave in to community desire for binary ABI compatibility in
NumPy motivated by difficulty of reproducing Python install• Need for a cross-platform way to install major Python
extensions (many with dependencies on large C or C++ libraries
• Python can’t be ubiquitous if people struggle to just get it and then manage it.
Why conda
Information Technology
What is Conda
• Full package management (like yum or apt-get) but cross-platform
• Control over environments (using hard-link farms) --- better than virtual-env. virtualenv today is like distutils and setuptools of several years ago (great at first but will end up hating it)
• Architected to be able to manage any packages (R, Scala, Clojure, Haskell, Ruby, JS)
• SAT solver to manage dependencies• User-definable repositories
New Features and Binstar• Build command from recipe --- many recipes here: https://
github.com/ContinuumIO/conda-recipes• Upload recipes to Binstar (last mile in binary package
hosting and deployment for any language). • “binstar in beta” is the beta code• Personal conda repositories --- https://conda.binstar.org/
travis• Free Continuum Anaconda repo will be on binstar.org• Private packages and behind-the-firewall satellites available• *Free build queue on Linux (Mac and Windows coming
soon) for hosted conda recipes
Demo
create Python 3 environment with IPython and scipy
create new recipe from PyPI (yunomi)
Packaging and Distribution Solved
• conda and binstar solve most of the problems that we have seen people encounter in managing Python installations (especially in large-scale institutions).
• they are supported solutions that can remove the technology pain of managing Python
• some problems, though, are people
Anaconda (open)Free enterprise-ready Python distribution of open-
source tools for large-scale data processing, predictive analytics, and scientific computing
Anaconda Add-Ons (paid-for)
•Revolutionary Python to GPU compiler•Extends Numba to take a subset of Python to the GPU (program CUDA in Python)
•CUDA FFT / BLAS interfaces
Fast, memory-efficient Python interface for SQL databases, NoSQL stores, Amazon S3, and large data files.
NumPy, SciPy, scikit-learn, NumExpr compiled against Intel’s Math Kernel Library (MKL)
Launcher
Why Numba?• Python is too slow for loops•Most people are not learning C/C++/Fortran today•Cython is an improvment (but still verbose and
needs C-compiler)•NVIDIA using LLVM for the GPU•Many people working with large typed-containers
(NumPy arrays)•We want to take high-level, tarray-oriented
expressions and compile it to fast code
NumPy + Mamba = Numba
LLVM Library
Intel Nvidia AppleAMD
OpenCLISPC CUDA CLANGOpenMP
LLVMPY
Python Function Machine Code
ARM
Example
Numba
Numba
@jit('void(f8[:,:],f8[:,:],f8[:,:])')def filter(image, filt, output): M, N = image.shape m, n = filt.shape for i in range(m//2, M-m//2): for j in range(n//2, N-n//2): result = 0.0 for k in range(m): for l in range(n): result += image[i+k-m//2,j+l-n//2]*filt[k, l] output[i,j] = result
~1500x speed-up
Numba changes the game!
LLVM IR
x86C++
ARM
PTX
C
Fortran
Python
Numba turns (a subset of) Python into a “compiled language” as fast as C (but much more
flexible). You don’t have to reach for C/C++
Laplace Example
@jit('void(double[:,:], double, double)')def numba_update(u, dx2, dy2): nx, ny = u.shape for i in xrange(1,nx-1): for j in xrange(1, ny-1): u[i,j] = ((u[i+1,j] + u[i-1,j]) * dy2 + (u[i,j+1] + u[i,j-1]) * dx2) / (2*(dx2+dy2))
Adapted from http://www.scipy.org/PerformancePython originally by Prabhu Ramachandran
@jit('void(double[:,:], double, double)')def numbavec_update(u, dx2, dy2): u[1:-1,1:-1] = ((u[2:,1:-1]+u[:-2,1:-1])*dy2 + (u[1:-1,2:] + u[1:-1,:-2])*dx2) / (2*(dx2+dy2))
Results of Laplace example
Version Time Speed UpNumPy 3.19 1.0Numba 2.32 1.38
Vect. Numba 2.33 1.37Cython 2.38 1.34Weave 2.47 1.29
Numexpr 2.62 1.22Fortran Loops 2.30 1.39Vect. Fortran 1.50 2.13
https://github.com/teoliphant/speed.git
LLVMPy worth looking at
LLVM (via LLVMPy) has done
much heavy lifting
LLVMPy = Compilers for
everybody
What is wrong with NumPy?• Dtype system is difficult to extend• Many Dtypes needed (missing data, enums, variable length
strings)• Immediate mode creates huge temporaries (spawning
Numexpr)• “Almost” an in-memory data-base comparable to SQL-
lite (missing indexes)• Integration with sparse arrays • Standard structure of arrays representation...• Missing Multi-methods• Optimization / Minimal support for multi-core / GPU
Now What?
After watching NumPy and SciPy get used all over Wall Street and by many scientists / engineers in
industry --- what would we do differently?
New Project
Blaze
NumPy
Out of Core,Distributed and Optimized
NumPy
Blaze Array or Table
Data Descriptor
Data BufferIndex Operation
NumPy BLZPersistent Format
RDBMS
CSVData Stream
Blaze Deferred Arrays
+"
A" *"
B" C"
A + B*C
• Symbolic objects which build a graph• Represents deferred computation
Usually what you have when you have a Blaze Array
DataShape Type System
• A data description language• A super-set of NumPy’s dtype• Provides more flexibility
Shape DType
DataShape
Blaze
Database
GPU Node
Array Server
NFS
Array Server
Array Server
Blaze Client
SynthesizedArray/Table view
array+sql://
array://
file:// array://
Python REPL, Scripts
Viz Data Server
C, C++, FORTRAN
JVM languages
Progress
• Basic calculations work out-of-core (via Numba and LLVM)
• Hard dependency on dynd and dynd-python (a dynamic C++-only multi-dimensional library like NumPy but with many improvements)
• Persistent arrays from BLZ• Basic array-server functionality for layering over CSV
files• 0.2 release in 1-2 weeks. 0.3 within a month after that
(first usable release)
DARPA providing help
DARPA-BAA-12-38: XDATA
TA-1: Scalable analytics and data processing technology TA-2: Visual user interface technology
Bokeh Plotting Library
• Interactive graphics for the web• Designed for large datasets• Designed for streaming data• Native interface in Python• Fast JavaScript component• DARPA funded• v0.1 release imminent
Reasons for Bokeh
1. Plotting must happen near the data too2. Quick iteration is essential => interactive visualization3. Interactive visualization on remote-data => use the browser4. Almost all web plotting libraries are either:
1. Designed for javascript programmers 2. Designed to output static graphs
5. We designed Bokeh to be dynamic graphing in the web for Python programmers
6. Will include “Abstract” or “synthetic” rendering (working on Hadoop and Spark compatibility)
Wakari
• Browser-based data analysis and visualization platform
• Wordpress / YouTube / Github for data analysis
• Full Linux environment with Anaconda Python
• Can be installed on internal clusters & servers
Why Wakari?• Data is too big to fit on your desktop • You need compute power but don’t have easy access to a
large cluster (cloud is sitting there with lots of power)• Configuration of software on a new system stinks
(especially a cluster).• Collaborative Data Analytics --- you want to build a
complex technical workflow and then share it with others easily (without requiring they do painful configuration to see your results)
• IPython Notebook is awesome --- let’s share it (but we also need the dependencies and data).
Wakari
• Free account has 512 MB RAM / 10 GB disk and shared multi-core CPU
• Easily spin-up map-reduce (Disco and Hadoop clusters)• Use IPython Parallel on many-nodes in the cloud• Develop GUI apps (possibly in Anaconda) and publish
them easily to Wakari (based on full power of scientific python --- complex technical workflows (IPython notebook for now)
Basic Data Explorer
Continuum Data Explorer (CDX)
• Open Source • Goal is interactivity• Combination of IPython REPL, Bokeh, and tables• Tight integration between GUI elements and REPL• Current features
- Namespace viewer (mapped to IPython namespace)- DataTable widget with group-by, computed columns, advanced-
filters- Interactive Plots connected to tables
CDX
Conclusion
Projects circle around giving tools to experts (occasional programmers or domain experts) to enable them to move their expertise to the data to get insights --- keep data where it is and move high-level but performant code)
Join us or ask how we can help you!