effectively using open source with conda
DESCRIPTION
Conda is a cross-platform package manager that lets you quickly and easily build environments containing complicated software stacks. It was built to manage the NumPy stack in Python but can be used to manage any complex software dependencies.TRANSCRIPT
Effectively using Open Source with conda
Travis E. Oliphant, PhD Continuum Analytics, Inc
The Opportunity
• Millions of projects that can be used in the enterprise
• Not enough to just adopt once — these projects change rapidly
• Effective use requires a plan for managing updates
The ChallengeSeparation of Concerns leads to granular libraries with
often deep dependencies
The Challenge
• Different “entry-points” (end-user applications or scripts) can have different dependencies. Often many of the dependencies are shared but a few applications need different versions of some packages.
• Not specific to any particular language or ecosystem. Python, Ruby, Node.Js, C/C++, .NET, Java, all have the same problem: How do you manage software life-cycle effectively?
• Production deployments need stability. IT managers want ease of deployment and testing. Developers want agility and ease of development.
The Challenge
How can developers and domain experts in an organization quickly and easily take advantage of the latest software developments yet still have stable production deployments of complex software?
You cannot take full advantage of the pace of open-source development if
you don’t address this!
Case Study: SciPy
There was this thing called the Internet and one could make a web-page and put code up on it and people started using it ...
Facebook for Hackers
I started SciPy in 1999 while I was in grad-school at the Mayo Clinic
(it was called Multipack back then)
Case Study: SciPy
Packaging circa 1999: Source tar ball and make file (users had to build)
SciPy is basically a bunch of C/C++/Fortran routines with Python interfaces
Observation: Popularity of Multipack (Early SciPy) grew significantly when Robert Kern made pre-
built binaries for Windows
Case Study: SciPy• Difficulty of producing binaries plus the desire to avoid
the dependency chain and lack of broad packaging solutions led to early SciPy being a “distribution” instead of separate inter-related libraries.
• There were (and are) too many different projects in SciPy (projects need 1-5 core contributors for communication dynamic reasons related to team-sizes)
Case Study: NumPy
I started writing NumPy in 2005 while I was teaching at BYU (it was a merger
of Numeric and Numarray)
NumPy ABI has not changed “officially” since 1.0 came out in 2006
Presumably extension modules (SciPy, scikit-learn, matplotlib, etc.) compiled against NumPy 1.0 will still work on NumPy 1.8.1
This was not a design goal!!!
Case Study: NumPyThis was a point of some contention and community difficulty when date-time was added in version 1.4 (impossible without changing the ABI in some way) but not really settled until version 1.7
The fundamental reason was a user-driven obsession with keeping ABI compatibility.
Windows users lacked useful packaging solution in face of NumPy-Stack
NumPy Stack (cry for conda...)
NumPy
SciPy Pandas Matplotlib
scikit-learnscikit-image statsmodels
PyTables
OpenCV
Cython
Numba SymPy NumExpr
astropy BioPython GDALPySAL
... many many more ...
Fundamental Principles• Complex things are built out of simple things • Fundamental principle of software engineering is
“separation of concerns” (modularity) • Reusability is enhanced when you “do one thing
and do it well” • But, to deploy you need to bring the pieces back
together. !
• This means you need a good packaging system for binary artifacts — with multiple-environments.
Continuum Solutions (Free)Conda
binstar.org Anaconda
Free all-in-one distribution of Python for Analytics and Visualization
• numpy, scipy, ipython • matplotlib, bokeh, • pandas, statsmodels, scikit-learn • many, many more… 100+
Miniconda
Python + conda — with these you can install exactly what you want…
• Binary repository of packages (public) • Multiple package types • Free public build queue • Current focus on:
• Python pypi-compatible packages (source distributions)
• conda packages (binary distributions)
$ conda install anaconda
• Cross-platform package manager • Dependency management (uses SAT
solver to resolve all dependencies) • System-level virtual environments (more
flexible than virtualenv)
Continuum Solutions (Premium)Anaconda
Server• Binary repository for private package Premium features: • hosting of private packages (public
packages are free) • access to priority build queue
• $10 / month (individuals) • 25 private packages • 5 GB disk space
• $50 / month (organizations) • 200 private packages • 30 GB disk space • right to have private packages in
organizations • $1500 / year
• unlimited private packages • 100 GB of disk space
binstar.org
• Internal mirror of public repositories • Mix private internal packages with public
repositories • Build customized versions of Anaconda
installers • Environment to .exe and .rpm tools • Comprehensive licensing • Comprehensive support • On-premise version of binstar.org
System Packaging solutions
yum (rpm) apt-get (dpkg)
Linux OSX
macports homebrew
Windows
chocolatey npackd
Cross-platform
conda
With virtual environments conda provides a modern, cross-platform, system-level packaging and deployment solution
Conda Features• Excellent support for “system-level” environments (like
having mini VMs but much lighter weight than docker.io) • Minimizes code-copies (uses hard/soft links if possible) • Dependency solver using fast satisfiability solver (SAT
solver) • Simple format binary tar-ball + meta-data • Meta-data allows static analysis of dependencies • Easy to create multiple “channels” which are repositories
for binary packages • User installable (no root privileges needed) • Can still use tools like pip --- conda fills in where they
fail.
ExamplesSetup a test environment
$ conda update conda $ conda create -n test python pip $ source activate test
Install another package
(test)$ conda install scikit-learn
$ activate test
Windows
First steps
$ conda create -n py3k python=3.3 $ source activate py3k
Create an environment
Install IPython notebook
(py3k) $ conda install ipython-notebook
$ conda create -n py3k python=3.3 ipython-notebook $ source activate py3k
All in One
Anaconda installationROOT_DIR!The directory that Anaconda was installed into; for example, /opt/Anaconda or C:\Anaconda!
/pkgs!Also referred to as PKGS_DIR. This directory contains exploded packages, ready to be linked in conda environments. Each package resides in a subdirectory corresponding to its canonical name.!
/envs!The system location for additional conda environments to be created.!
!the default, or root, environment! /bin! /include! /lib! /share
Look at conda package --- a simple .tar.bz2
http://docs.continuum.io/conda/intro.html
Anatomy of unpacked conda package
/lib /include /bin /man
/info files index.json
bzipped tarfile of all the files comprising the package at the full-paths they would
be installed to relative to a “system” install or “chroot jail”
an environment is just a “union” of these paths
All conda packages have this info directory which contains meta-data for tracked files,
dependency information, etc.
EnvironmentsOne honking great idea! Let’s do more of those!
Easy to make Easy to throw away
Uses: • Testing (python 2.6, 2.7, 3.3) • Development • Trying new packages from PyPI • Separating deployed apps with
different dependency needs • Trying new versions of Python • Reproducing someone’s work conda create -h
conda info -e
Getting System information
Basic info
conda info
Named-environment info
conda info --all
System info
conda info --system
conda install -n py3k scipy pip
http://repo.continuum.io/pkgs/dev Experimental or developmental versions of packages
http://repo.continuum.io/pkgs/gpl GPL licensed packages
http://repo.continuum.io/pkgs/free non GPL open source packages
Default package repositories (configurable)
Installing packages
How it works
Channel 1
Channel 2
Channel N
metadata
metadata
metadata
conda merged metadata
l l l
Create channels
• Create a directory of conda packages • Run conda index <dirname> • Either use file:///path/to/dir in .condarc or
use simple web server on the /path/to/dir
Option 1
Option 2
Use binstar.org (also available as on-premise solution with Anaconda Server)
Binstar.org — channels (request invite)
conda install -c <channel name>
<pkg name> !
will install from binstar channel
!or you can add channel to your
config file
free for public packages
conda list also includes packages installed via pip!
List Installed packages
conda create -n py3k scipy pip source activate py3k pip install pint
$ conda list
# packages in environment at /Users/travis/anaconda/envs/py3k: # numpy 1.8.1 py27_0 openssl 1.0.1g 0 pint 0.4.2 <pip> pip 1.5.4 py27_0 python 2.7.6 1 readline 6.2 2 scipy 0.13.3 np18py27_0 setuptools 3.1 py27_0 sqlite 3.7.13 1 tk 8.5.13 1 wsgiref 0.1.2 <pip> zlib 1.2.7 1
Output
Update a package to latest
conda update pandas get the latest pandas from the channels you are subscribed to
conda update anaconda change to the latest released anaconda including its specific dependencies
this can downgrade packages if they are newer than those in
the “released” Anaconda
conda update --allTo update all the packages in an
environment to the latest versions use the --all option
conda search <regex>Search for a package
Find packages and channels they are in
conda search --outdated sympy Only show packages matching regex that are installed but outdated
conda search typo
typogrify * 2.0.0 py27_0 http://conda.binstar.org/travis/osx-64/ 2.0.0 py33_1 http://conda.binstar.org/asmeurer/osx-64/ 2.0.0 py26_1 http://conda.binstar.org/asmeurer/osx-64/
sympy 0.7.1 py27_0 defaults ! 0.7.4 py26_0 defaults 0.7.4.1 py33_0 defaults * 0.7.4.1 py27_0 defaults 0.7.4.1 py26_0 defaults 0.7.5 py34_0 defaults 0.7.5 py33_0 defaults
l l l
l l l
conda remove -n py3k scipy matplotlib
Removing files and environments
Removing Packages
Removing Environment
conda remove -n py3k --all
Note: packages are just “unlinked” from environment. All the files are still available
unpacked in a package cache.
Removing unused packages
conda clean -t conda clean -p
Remove unused tarballs Remove unused directories
conda package -u conda package --pkg-name bulk --pkg-version 0.1
Untracked Files
Easy way to install into an environment using anything (pip, make, setup.py, etc.) and then package up all of it into a binary tar-ball deployable via conda install <pkg-name>.tar.bz2 !
pickle for binary code!
# This is a sample .condarc file !# channel locations. These override conda defaults, i.e., conda will # search *only* the channels listed here, in the order given. Use "default" to # automatically include all default channels. !channels: - defaults - http://some.custom/channel !# Proxy settings # http://[username]:[password]@[server]:[port] proxy_servers: http: http://user:[email protected]:8080 https: https://user:[email protected]:8080 !envs_dirs: - /opt/anaconda/envs - /home/joe/my-envs !pkg_dirs: - /home/joe/user-pkg-cache - /opt/system/pkgs !changeps1: False !# binstar.org upload (not defined here means ask) binstar_upload: True
Conda configuration
Scripting interface
conda config —add KEY VALUE
conda config —remove-key KEY
conda config —get KEY
conda config —set KEY BOOL
conda config —remove KEY VALUE
conda skeleton pypi <pypi-name>
Building new packages
conda build <recipe-dir>
Option 1
Option 2
conda pipbuild <pypi-name>
conda install conda-build
Conda Recipe is a directory
build.sh BASH build commands (POSIX) bld.bat CMD build commands (Win) meta.yaml extended yaml declarative meta-data
Required
Optional
run_test.py will be executed during test phase *.patch patch-files for the source * any other resources needed by build but not included
in sources described in meta.yaml file
Recipe MetaDatapackage: name: # name of package version: # version of package about: home: # home-page license: # license !# All optional from here.... source: fn: # filename of source url: # url of source md5: # hash of source # or from git: git_url: git_tag: patches: # list of patches to source - fix.patch build: entry_points: # entry-points (binary commands or scripts) - name = module:function number: # defaults to 0 requirements: # lists of requirements build: # requirements for build (as a list) run: # requirements for running (as a list) test: requires: # list of requirements for testing commands: # commands to run for testing (entry-points) imports: # modules to import for testing
http://docs.continuum.io/conda/build.html
Converting to another platformConda packages are specific to a particular
platform. However, if there are no platform-specific binary files in a package, it can be
converted automatically to a package that can be installed on another platform.
conda convert --output-dir win32 --platform win-32 <package-file>
Example
Binstar.org (request invite)
Once you have built a
conda package, you can share it
with the world on binstar.org
!conda install -c <name> <pkgname>
free for public packages
Binstar
$ conda config --add channels 'http://conda.binstar.org/travis' $ conda config --add channels
'http://conda.binstar.org/asmuerer'
Adding channels
Uploading packages
binstar upload /full/path/to/package.tar.bz2
binstar register /full/path/to/package.tar.bz2
if package never uploaded before
Binstar Package Types
Permissions Description
Private Only people given permission can see this package.
Personal Everyone will be able to see this package in your user repository.
Publish This package will be published in the global public repository.
Useful aliases
workon=‘source activate’ workoff=‘source deactivate’
• Cross-platform Tested and Supported Python Distribution
• Enterprise Python Deployment • Private, Secure On-premise package repository • Comprehensive Licensing • Customized Installers and Mirrors • Additional Products • Enhanced Support • Optional, On-premise binstar.org
Thanks!
Aaron Meurer conda and binstar developer
Sean Ross-Ross (principal binstar.org)
Bryan Van de Ven (original conda author)
Ilan Schnell (principal conda developer)