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Vowpal wabbit python windows

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  1. Vowpal wabbit python windows
  2. Fixes
  3. Fixes
  4. Fixes:
  5. Internal improvements
  6. Other notable changes:
  7. All Changes
  8. Changes
  9. Build System
  10. Vowpal wabbit python windows
  11. About
  12. MLWave
  13. Install Vowpal Wabbit on Windows and Cygwin
  14. Install Cygwin
  15. Install Vowpal Wabbit
  16. Utility scripts
  17. 41 thoughts on “Install Vowpal Wabbit on Windows and Cygwin”

Vowpal wabbit python windows

jackgerrits released this Apr 13, 2021

This release includes no code changes but fixes the Python source distribution, and adds support for Python 3.9 binary wheels on MacOS and Windows.

Since this only affects Python, only the PyPi release channel will be updated.

  • ci: run CI on release branches (#2942)
  • ci: update brew to mitigate bintray brownout (#2941)
  • chore: update version to 8.10.1
  • build: add 3.9 to windows python build (#2939)
  • Fix python manifest for ext_libs (#2938)
  • build: Update build_python_wheels_macos.yml (#2937)

jackgerrits released this Apr 1, 2021

This release includes quadratic interaction speed improvements, ARM support, logging updates and more.

lalo released this Mar 4, 2021

This patch release contains a fix for Neural Network reduction (—nn).

Fixes

jackgerrits released this Jan 27, 2021

This patch release contains a fix for the Java bindings. It changes no other code paths apart from the version number changing.

Fixes

  • Don’t export lib symbols from vw_jni binary when static linking (#2789)

jackgerrits released this Nov 12, 2020

This release includes major features such as continuous actions, square CB, probabilistic label tree, slates, CB distributionally robust optimization, CB ADF RND, Python wheels and many bug fixes.

jackgerrits released this Mar 3, 2020

This patch release fixes an issue in the Python bindings where parsing examples from text sometimes caused crashes.

Fixes:

  • Fix delete behavior for examples created using parse function (#2206)

JohnLangford released this Dec 7, 2019

  • There has significant work in streamlining and improving support for the Python bindings. Thanks @peterychang! (#1928)
  • The Conditional Contextual Bandit reduction got merged in, this reduction allows you to express problems where there are multiple slots to fill. See here for the wiki page. (#1816) (#1995) (#2078) (#2141)
  • CMake install targets have been added to the build files (#2172) (#2135)
    • Now you can find and link VW easily in other projects:

    Internal improvements

    We are at work overhauling and modernizing VW, some of the relevant changes on that front are below:

    • Migrate c arrays to std::array (#2094)
    • Make hashing constexpr in C++14 and unify rotl impl (#2093)
    • Make is_example_header const (#2095)
    • Allow constructor arugments for calloc_or_throw (#2070)
    • Learner now holds type erased reduction data (#2060)
    • Use numeric_limits (#2107)
    • Unify throwing of exceptions to use vw_exception instead of bare std:exception (#2171)
    • Cb explore adf atomization (#2069)
    • Refactor cb_adf reduction (#2057)
    • Move cb_sample to be class based (#2087)
    • Atomize topk reduction (#2050)
    • Atomize autolink reduction (#2047)

    Other notable changes:

    • —version now includes commit id if available at build time (#1951)
    • Macos added as CI target (#1965)
    • Allow escaped command lines (#2157)
    • Update MSVC Toolchain to v14.1 (#1988)
    • Multiinstance mode for multiline examples (#1934)

    All Changes

    JohnLangford released this Jun 7, 2019

    • The repo has moved to the VowpalWabbit organization
    • The group of core maintainers has been growing with steady improvements

    Changes

    As always, lots of bug fixes.

    Build System

    The build system for VW has been overhauled to use CMake. This means, easier dependency resolution, faster build times and easier consumption as a dependency. The old automake + make systems have been replaced by this and eventually the .sln file will be replaced too. (#1624)

    Vowpal wabbit python windows

    This is the Vowpal Wabbit fast online learning code.

    Why Vowpal Wabbit?

    Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state of the art algorithms with performance in mind.

    • Input Format. The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free form text in different namespaces.
    • Speed. The learning algorithm is fast — similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function.
    • Scalability. This is not the same as fast. Instead, the important characteristic here is that the memory footprint of the program is bounded independent of data. This means the training set is not loaded into main memory before learning starts. In addition, the size of the set of features is bounded independent of the amount of training data using the hashing trick.
    • Feature Interaction. Subsets of features can be internally paired so that the algorithm is linear in the cross-product of the subsets. This is useful for ranking problems. The alternative of explicitly expanding the features before feeding them into the learning algorithm can be both computation and space intensive, depending on how it’s handled.

    For the most up to date instructions for getting started on Windows, MacOS or Linux please see the wiki. This includes:

    About

    Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

    MLWave

    Install Vowpal Wabbit on Windows and Cygwin

    There are already instructions on how to install Vowpal Wabbit on other operating systems, but we could not find a clear one for Windows. We will use Cygwin to install the latest version of Vowpal Wabbit.

    Thanks to reader Dominic for providing useful feedback! Guide is now updated and suitable for both 32-bit and 64-bit systems.

    Thanks to readers Ray, Phil Culliton, dou, Christophe, Fred and Brian guide is updated for the latest version of Vowpal Wabbit.

    Update (2017): This guide has not been updated for a while, so may be missing dependancies. I suggest you look at Vowpal Wabbit Releases for Windows MSI installers of the latest versions. Thanks to Markus Cozowicz!

    Install Cygwin

    Download the version of Cygwin for your operating system from: http://cygwin.com/install.html

    In the Cygwin setup “Select Packages” window you will need to install the following packages:

    • Search for «git» . From Devel select git: Distributed version control system
    • Search for «make» . From Devel select make: the GNU version of the ‘make’ utility
    • Search for «g++» . From Devel select gcc-g++ GNU compiler Collection (C++)
    • Search for «zlib» . From Libs select zlib-devel: Zlib de/compression library (development)
    • Search for «boost» . From Libs select libboost-devel: Boost C++ Libraries
    • Search for «libtool» . From Devel select libtool: Generic library support script
    • Search for «automake» . From Devel select automake: Wrapper scripts for automake and aclocal .
    • Search for «automake» . From Devel select automake1.9: (1.9) a tool for generating GNU-compliant Makefiles (The others should be installed when resolving dependancies).
    • Search for «libboost» . From Libs select libboost_python-devel: Boost C++ libraries
    • Search for «libboost» . From Libs select libboost_program_options1.55: Boost C++ libraries

    If you get shown a screen with “Resolving Dependancies”, keep the option ‘Select required packages (RECOMMENDED)’ checked.

    You can always run the Cygwin installer/setup again to install more packages.

    Installing the ‘make’ package.

    Install Vowpal Wabbit

    Open a command prompt with cmd, and run Cygwin.bat. If you installed on the c: disk and in the cygwin64 directory, then run:

    or go to the directory and click on cygwin.bat (file extension may not show)

    You should now be in the Cygwin terminal (starts with a $ ). Use ls to get a directory listing.

    Here you run the following command to clone the Vowpal Wabbit Github Repo (latest master version):

    This will start the download. When it is done go to the vowpal_wabbit directory with:

    And when that is done, run:

    That’s it! Go into the vowpalwabbit directory

    To get help on all the command line arguments.

    To get the version.

    If you installed on c: , in the cygwin64 directory, in Windows you can go to:

    to quickly put some .vw datasets there or copy the output predictions.

    You could also use the cygdrive -prefix to run VW on files stored outside your user directory (or to output your model to another directory/drive). If your training set is on c:\data\train.vw try:

    Utility scripts

    Perl should now also be installed. If not search for perl in the Cygwin setup and from perl select perl: Perl programming language interpreter . In the vowpal_wabbit/utl directory you’ll find some utility scripts, of which vw-varinfo is particularly useful.

    The illustration with this post came from “The Velveteen Rabbit (1922)” and is in the Public Domain.

    41 thoughts on “Install Vowpal Wabbit on Windows and Cygwin”

    I’m coming from Kaggle and installed your version of vowpal wabbit. Thanks for providing the executable! Really lowering the entry barriers when trying this tool 🙂

    There was one problem though that I ran into. Cygwin is not installing boost by default and vowpal wabbit needs it. Copying it as described in “the harder way” didn’t work. You can though just tell the installer to include it, which was quite easy and worked well. I’ve also installed make, g++ and zlib to be one the save side. Don’t know if that was necessary too.

    Hey Dominic! Thank you for the tip on using the Cygwin installer for boost! I will update the tutorial!

    Hi ,
    Thanks for posting this.

    I’m having trouble with the “make” command. There seems to be a problem compiling, so vw is not created. Have you encountered this problem. I’ve included the errors that resulted from executing “make” below:

    g++ -MM hash.cc > hash.d
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c main.cc -o main.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c hash.cc -o hash.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c memory.cc -o memory.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c global_data.cc -o global_data.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c io_buf.cc -o io_buf.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c parse_regressor.cc -o parse_regressor.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c parse_primitives.cc -o parse_primitives.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c unique_sort.cc -o unique_sort.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c cache.cc -o cache.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c rand48.cc -o rand48.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c simple_label.cc -o simple_label.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c multiclass.cc -o multiclass.o
    rand48.cc:11:14: warning: use of C++0x long long integer constant [-Wlong-long]
    uint64_t a = 0xeece66d5deece66dULL;
    ^
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c oaa.cc -o oaa.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c ect.cc -o ect.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c autolink.cc -o autolink.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c binary.cc -o binary.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c lrq.cc -o lrq.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c cost_sensitive.cc -o cost_sensitive.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c csoaa.cc -o csoaa.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c cb.cc -o cb.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c cb_algs.cc -o cb_algs.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c wap.cc -o wap.o
    lrq.cc: In instantiation of ‘void LRQ::predict_or_learn(LRQ::LRQstate&, LEARNER::learner&, example&) [with bool is_learn = true]’:
    lrq.cc:239:53: required from here
    lrq.cc:136:76: warning: narrowing conversion of ‘left’ from ‘unsigned char’ to ‘char’ inside < >is ill-formed in C++11 [-Wnarrowing]
    char subname[4] = < left, ‘^’, right, ” >;
    ^
    lrq.cc:136:76: warning: narrowing conversion of ‘right’ from ‘unsigned char’ to ‘char’ inside < >is ill-formed in C++11 [-Wnarrowing]
    lrq.cc: In instantiation of ‘void LRQ::predict_or_learn(LRQ::LRQstate&, LEARNER::learner&, example&) [with bool is_learn = false]’:
    lrq.cc:240:56: required from here
    lrq.cc:136:76: warning: narrowing conversion of ‘left’ from ‘unsigned char’ to ‘char’ inside < >is ill-formed in C++11 [-Wnarrowing]
    lrq.cc:136:76: warning: narrowing conversion of ‘right’ from ‘unsigned char’ to ‘char’ inside < >is ill-formed in C++11 [-Wnarrowing]
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c searn.cc -o searn.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c searn_sequencetask.cc -o searn_sequencetask.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c parse_example.cc -o parse_example.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c scorer.cc -o scorer.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c network.cc -o network.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c parse_args.cc -o parse_args.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c accumulate.cc -o accumulate.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c gd.cc -o gd.o
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c learner.cc -o learner.o
    searn.cc:283:31: warning: integer constant is too large for ‘long’ type [-Wlong-long]
    (int)(ceil( log10((float)10000000000+1) ))) + 1; // max action id
    ^
    searn.cc:1864:5: warning: integer constant is too large for ‘long’ type [-Wlong-long]
    size_t neighbor_constant = 8349204823;
    ^
    g++ -march=native -Wall -pedantic -O3 -fomit-frame-pointer -fno-strict-aliasing -ffast-math -D_FILE_OFFSET_BITS=64 -DNDEBUG -I /usr/include -c lda_core.cc -o lda_core.o
    searn_sequencetask.cc: In function ‘void SequenceSpanTask::structured_predict(Searn::searn&, std::vector)’:
    searn_sequencetask.cc:244:91: error: call of overloaded ‘predict(example*&, uint32_t, v_array*&)’ is ambiguous
    last_prediction = srn.predict(ec[i], MULTICLASS::get_example_label(ec[i]), y_allowed);
    ^
    searn_sequencetask.cc:244:91: note: candidates are:
    In file included from searn_sequencetask.h:9:0,
    from searn_sequencetask.cc:6:
    searn.h:47:14: note: uint32_t Searn::searn::predict(example*, uint32_t, v_array*)
    uint32_t predict(example* ec, uint32_t one_ystar, v_array* yallowed=NULL); // if there is a single oracle action
    ^
    In file included from searn_sequencetask.h:9:0,
    from searn_sequencetask.cc:6:
    searn.h:51:14: note: uint32_t Searn::searn::predict(example*, size_t, v_array*, v_array*)
    uint32_t predict(example* ecs, size_t ec_len, v_array* ystar, v_array* yallowed=NULL); // if there is a single oracle action
    ^
    Makefile:28: recipe for target ‘searn_sequencetask.o’ failed
    make[1]: *** [searn_sequencetask.o] Error 1
    make[1]: *** Waiting for unfinished jobs….
    searn.cc: In function ‘void Searn::print_update(vw&, Searn::searn&)’:
    searn.cc:1847:43: warning: format ‘%lu’ expects argument of type ‘long unsigned int’, but argument 3 has type ‘size_t ’ [-Wformat=]
    fprintf(stderr, ” %15lusec”, num_sec);
    ^
    searn.cc:1847:43: warning: format ‘%lu’ expects argument of type ‘long unsigned int’, but argument 3 has type ‘size_t ’ [-Wformat=]
    searn.cc: In function ‘void Searn::add_neighbor_features(Searn::searn&)’:
    searn.cc:1864:32: warning: large integer implicitly truncated to unsigned type [-Woverflow]
    size_t neighbor_constant = 8349204823;
    ^
    make[1]: Leaving directory ‘/cygdrive/c/vowpal_wabbit/vowpalwabbit’
    Makefile:75: recipe for target ‘vw’ failed
    make: *** [vw] Error 2

    Unfortunately I don’t recognize that error. I’ve tried above tutorial on two laptops and I can get VW to compile (It does show a few warnings to me, but nothing show-stopping).

    If you did install all the required packages, but just can’t make the VW executable, take a look at: https://github.com/MLWave/vowpal-wabbit-cygwin-executables for pre-made executables. Do note that these are for 64-bit systems.

    If all else fails, you could have a look at: https://github.com/MLWave/Kaggle_Rotten_Tomatoes which has a Windows executable of VW version 7.1.

    A thing you can try: If libtools is not installed, install libtools.

    If you are really desperate for some Vowpal Wabbit, install VirtualBox with an Ubuntu image. Then you can simply type ‘make’ or ‘apt-get’ what you need.

    Hope you get it to work!

    That error is caused by GCC being tetchy about ambiguous overloads (as it often is). It looks like the issue has been cleared up in more recent commits, though, so just pulling the latest code should clear it up.

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