9. Release Notes

9.1. Version 0.22.1

This is a bug-fix release to resolve some packaging issues and other problems found in the 0.22.0 release.


  • PR #1515: Include MANIFEST.in in MANIFEST.in so that sdist still works from source tar files.
  • PR #1518: Fix reference counting bug caused by hidden alias
  • PR #1519: Fix erroneous assert when passing nopython=True to guvectorize.
  • PR #1521: Fix cuda.test()

9.2. Version 0.22.0

This release features several highlights: Python 3.5 support, Numpy 1.10 support, Ahead-of-Time compilation of extension modules, additional vectorization features that were previously only available with the proprietary extension NumbaPro, improvements in array indexing.


  • PR #1497: Allow scalar input type instead of size-1 array to @guvectorize
  • PR #1480: Add distutils support for AOT compilation
  • PR #1460: Create a new API for Ahead-of-Time (AOT) compilation
  • PR #1451: Allow passing Python lists to JIT-compiled functions, and reflect mutations on function return
  • PR #1387: Numpy 1.10 support
  • PR #1464: Support cffi.FFI.from_buffer()
  • PR #1437: Propagate errors raised from Numba-compiled ufuncs; also, let “division by zero” and other math errors produce a warning instead of exiting the function early
  • PR #1445: Support a subset of fancy indexing
  • PR #1454: Support “out-of-line” CFFI modules
  • PR #1442: Improve array indexing to support more kinds of basic slicing
  • PR #1409: Support explicit CUDA memory fences
  • PR #1435: Add support for vectorize() and guvectorize() with HSA
  • PR #1432: Implement numpy.nonzero() and numpy.where()
  • PR #1416: Add support for vectorize() and guvectorize() with CUDA, as originally provided in NumbaPro
  • PR #1424: Support in-place array operators
  • PR #1414: Python 3.5 support
  • PR #1404: Add the parallel ufunc functionality originally provided in NumbaPro
  • PR #1393: Implement sorting on arrays and lists
  • PR #1415: Add functions to estimate the occupancy of a CUDA kernel
  • PR #1360: The JIT cache now stores the compiled object code, yielding even larger speedups.
  • PR #1402: Fixes for the ARMv7 (armv7l) architecture under Linux
  • PR #1400: Add the cuda.reduce() decorator originally provided in NumbaPro


  • PR #1483: Allow np.empty_like() and friends on non-contiguous arrays
  • Issue #1471: Allow caching JIT functions defined in IPython
  • PR #1457: Fix flat indexing of boolean arrays
  • PR #1421: Allow calling Numpy ufuncs, without an explicit output, on non-contiguous arrays
  • Issue #1411: Fix crash when unpacking a tuple containing a Numba-allocated array
  • Issue #1394: Allow unifying range_state32 and range_state64
  • Issue #1373: Fix code generation error on lists of bools

9.3. Version 0.21.0

This release introduces support for AMD’s Heterogeneous System Architecture, which allows memory to be shared directly between the CPU and the GPU. Other major enhancements are support for lists and the introduction of an opt-in compilation cache.


  • PR #1391: Implement print() for CUDA code
  • PR #1366: Implement integer typing enhancement proposal (NBEP 1)
  • PR #1380: Support the one-argument type() builtin
  • PR #1375: Allow boolean evaluation of lists and tuples
  • PR #1371: Support array.view() in CUDA mode
  • PR #1369: Support named tuples in nopython mode
  • PR #1250: Implement numpy.median().
  • PR #1289: Make dispatching faster when calling a JIT-compiled function from regular Python
  • Issue #1226: Improve performance of integer power
  • PR #1321: Document features supported with CUDA
  • PR #1345: HSA support
  • PR #1343: Support lists in nopython mode
  • PR #1356: Make Numba-allocated memory visible to tracemalloc
  • PR #1363: Add an environment variable NUMBA_DEBUG_TYPEINFER
  • PR #1051: Add an opt-in, per-function compilation cache


  • Issue #1372: Some array expressions would fail rewriting when involved the same variable more than once, or a unary operator
  • Issue #1385: Allow CUDA local arrays to be declared anywhere in a function
  • Issue #1285: Support datetime64 and timedelta64 in Numpy reduction functions
  • Issue #1332: Handle the EXTENDED_ARG opcode.
  • PR #1329: Handle the in operator in object mode
  • Issue #1322: Fix augmented slice assignment on Python 2
  • PR #1357: Fix slicing with some negative bounds or step values.

9.4. Version 0.20.0

This release updates Numba to use LLVM 3.6 and CUDA 7 for CUDA support. Following the platform deprecation in CUDA 7, Numba’s CUDA feature is no longer supported on 32-bit platforms. The oldest supported version of Windows is Windows 7.


  • Issue #1203: Support indexing ndarray.flat
  • PR #1200: Migrate cgutils to llvmlite
  • PR #1190: Support more array methods: .transpose(), .T, .copy(), .reshape(), .view()
  • PR #1214: Simplify setup.py and avoid manual maintenance
  • PR #1217: Support datetime64 and timedelta64 constants
  • PR #1236: Reload environment variables when compiling
  • PR #1225: Various speed improvements in generated code
  • PR #1252: Support cmath module in CUDA
  • PR #1238: Use 32-byte aligned allocator to optimize for AVX
  • PR #1258: Support numpy.frombuffer()
  • PR #1274: Use TravisCI container infrastructure for lower wait time
  • PR #1279: Micro-optimize overload resolution in call dispatch
  • Issue #1248: Improve error message when return type unification fails


  • Issue #1131: Handling of negative zeros in np.conjugate() and np.arccos()
  • Issue #1188: Fix slow array return
  • Issue #1164: Avoid warnings from CUDA context at shutdown
  • Issue #1229: Respect the writeable flag in arrays
  • Issue #1244: Fix bug in refcount pruning pass
  • Issue #1251: Fix partial left-indexing of Fortran contiguous array
  • Issue #1264: Fix compilation error in array expression
  • Issue #1254: Fix error when yielding array objects
  • Issue #1276: Fix nested generator use

9.5. Version 0.19.2

This release fixes the source distribution on pypi. The only change is in the setup.py file. We do not plan to provide a conda package as this release is essentially the same as 0.19.1 for conda users.

9.6. Version 0.19.1

  • Issue #1196:
    • fix double-free segfault due to redundant variable deletion in the Numba IR (#1195)
    • fix use-after-delete in array expression rewrite pass

9.7. Version 0.19.0

This version introduces memory management in the Numba runtime, allowing to allocate new arrays inside Numba-compiled functions. There is also a rework of the ufunc infrastructure, and an optimization pass to collapse cascading array operations into a single efficient loop.


Support for Windows XP and Vista with all compiler targets and support for 32-bit platforms (Win/Mac/Linux) with the CUDA compiler target are deprecated. In the next release of Numba, the oldest version of Windows supported will be Windows 7. CPU compilation will remain supported on 32-bit Linux and Windows platforms.

Known issues:

  • There are some performance regressions in very short running nopython functions due to the additional overhead incurred by memory management. We will work to reduce this overhead in future releases.


  • Issue #1181: Add a Frequently Asked Questions section to the documentation.
  • Issue #1162: Support the cumsum() and cumprod() methods on Numpy arrays.
  • Issue #1152: Support the *args argument-passing style.
  • Issue #1147: Allow passing character sequences as arguments to JIT-compiled functions.
  • Issue #1110: Shortcut deforestation and loop fusion for array expressions.
  • Issue #1136: Support various Numpy array constructors, for example numpy.zeros() and numpy.zeros_like().
  • Issue #1127: Add a CUDA simulator running on the CPU, enabled with the NUMBA_ENABLE_CUDASIM environment variable.
  • Issue #1086: Allow calling standard Numpy ufuncs without an explicit output array from nopython functions.
  • Issue #1113: Support keyword arguments when calling numpy.empty() and related functions.
  • Issue #1108: Support the ctypes.data attribute of Numpy arrays.
  • Issue #1077: Memory management for array allocations in nopython mode.
  • Issue #1105: Support calling a ctypes function that takes ctypes.py_object parameters.
  • Issue #1084: Environment variable NUMBA_DISABLE_JIT disables compilation of @jit functions, instead calling into the Python interpreter when called. This allows easier debugging of multiple jitted functions.
  • Issue #927: Allow gufuncs with no output array.
  • Issue #1097: Support comparisons between tuples.
  • Issue #1075: Numba-generated ufuncs can now be called from nopython functions.
  • Issue #1062: @vectorize now allows omitting the signatures, and will compile the required specializations on the fly (like @jit does).
  • Issue #1027: Support numpy.round().
  • Issue #1085: Allow returning a character sequence (as fetched from a structured array) from a JIT-compiled function.


  • Issue #1170: Ensure ndindex(), ndenumerate() and ndarray.flat work properly inside generators.
  • Issue #1151: Disallow unpacking of tuples with the wrong size.
  • Issue #1141: Specify install dependencies in setup.py.
  • Issue #1106: Loop-lifting would fail when the lifted loop does not produce any output values for the function tail.
  • Issue #1103: Fix mishandling of some inputs when a JIT-compiled function is called with multiple array layouts.
  • Issue #1089: Fix range() with large unsigned integers.
  • Issue #1088: Install entry-point scripts (numba, pycc) from the conda build recipe.
  • Issue #1081: Constant structured scalars now work properly.
  • Issue #1080: Fix automatic promotion of booleans to integers.

9.8. Version 0.18.2

Bug fixes:

  • Issue #1073: Fixes missing template file for HTML annotation
  • Issue #1074: Fixes CUDA support on Windows machine due to NVVM API mismatch

9.9. Version 0.18.1

Version 0.18.0 is not officially released.

This version removes the old deprecated and undocumented argtypes and restype arguments to the @jit decorator. Function signatures should always be passed as the first argument to @jit.


  • Issue #960: Add inspect_llvm() and inspect_asm() methods to JIT-compiled functions: they output the LLVM IR and the native assembler source of the compiled function, respectively.
  • Issue #990: Allow passing tuples as arguments to JIT-compiled functions in nopython mode.
  • Issue #774: Support two-argument round() in nopython mode.
  • Issue #987: Support missing functions from the math module in nopython mode: frexp(), ldexp(), gamma(), lgamma(), erf(), erfc().
  • Issue #995: Improve code generation for round() on Python 3.
  • Issue #981: Support functions from the random and numpy.random modules in nopython mode.
  • Issue #979: Add cuda.atomic.max().
  • Issue #1006: Improve exception raising and reporting. It is now allowed to raise an exception with an error message in nopython mode.
  • Issue #821: Allow ctypes- and cffi-defined functions as arguments to nopython functions.
  • Issue #901: Allow multiple explicit signatures with @jit. The signatures must be passed in a list, as with @vectorize.
  • Issue #884: Better error message when a JIT-compiled function is called with the wrong types.
  • Issue #1010: Simpler and faster CUDA argument marshalling thanks to a refactoring of the data model.
  • Issue #1018: Support arrays of scalars inside Numpy structured types.
  • Issue #808: Reduce Numba import time by half.
  • Issue #1021: Support the buffer protocol in nopython mode. Buffer-providing objects, such as bytearray, array.array or memoryview support array-like operations such as indexing and iterating. Furthermore, some standard attributes on the memoryview object are supported.
  • Issue #1030: Support nested arrays in Numpy structured arrays.
  • Issue #1033: Implement the inspect_types(), inspect_llvm() and inspect_asm() methods for CUDA kernels.
  • Issue #1029: Support Numpy structured arrays with CUDA as well.
  • Issue #1034: Support for generators in nopython and object mode.
  • Issue #1044: Support default argument values when calling Numba-compiled functions.
  • Issue #1048: Allow calling Numpy scalar constructors from CUDA functions.
  • Issue #1047: Allow indexing a multi-dimensional array with a single integer, to take a view.
  • Issue #1050: Support len() on tuples.
  • Issue #1011: Revive HTML annotation.


  • Issue #977: Assignment optimization was too aggressive.
  • Issue #561: One-argument round() now returns an int on Python 3.
  • Issue #1001: Fix an unlikely bug where two closures with the same name and id() would compile to the same LLVM function name, despite different closure values.
  • Issue #1006: Fix reference leak when a JIT-compiled function is disposed of.
  • Issue #1017: Update instructions for CUDA in the README.
  • Issue #1008: Generate shorter LLVM type names to avoid segfaults with CUDA.
  • Issue #1005: Properly clean up references when raising an exception from object mode.
  • Issue #1041: Fix incompatibility between Numba and the third-party library “future”.
  • Issue #1053: Fix the size attribute of CUDA shared arrays.

9.10. Version 0.17.0

The major focus in this release has been a rewrite of the documentation. The new documentation is better structured and has more detailed coverage of Numba features and APIs. It can be found online at http://numba.pydata.org/numba-doc/dev/index.html


  • Issue #895: LLVM can now inline nested function calls in nopython mode.
  • Issue #863: CUDA kernels can now infer the types of their arguments (“autojit”-like).
  • Issue #833: Support numpy.{min,max,argmin,argmax,sum,mean,var,std} in nopython mode.
  • Issue #905: Add a nogil argument to the @jit decorator, to release the GIL in nopython mode.
  • Issue #829: Add a identity argument to @vectorize and @guvectorize, to set the identity value of the ufunc.
  • Issue #843: Allow indexing 0-d arrays with the empty tuple.
  • Issue #933: Allow named arguments, not only positional arguments, when calling a Numba-compiled function.
  • Issue #902: Support numpy.ndenumerate() in nopython mode.
  • Issue #950: AVX is now enabled by default except on Sandy Bridge and Ivy Bridge CPUs, where it can produce slower code than SSE.
  • Issue #956: Support constant arrays of structured type.
  • Issue #959: Indexing arrays with floating-point numbers isn’t allowed anymore.
  • Issue #955: Add support for 3D CUDA grids and thread blocks.
  • Issue #902: Support numpy.ndindex() in nopython mode.
  • Issue #951: Numpy number types (numpy.int8, etc.) can be used as constructors for type conversion in nopython mode.


  • Issue #889: Fix NUMBA_DUMP_ASSEMBLY for the CUDA backend.
  • Issue #903: Fix calling of stdcall functions with ctypes under Windows.
  • Issue #908: Allow lazy-compiling from several threads at once.
  • Issue #868: Wrong error message when multiplying a scalar by a non-scalar.
  • Issue #917: Allow vectorizing with datetime64 and timedelta64 in the signature (only with unit-less values, though, because of a Numpy limitation).
  • Issue #431: Allow overloading of cuda device function.
  • Issue #917: Print out errors occurred in object mode ufuncs.
  • Issue #923: Numba-compiled ufuncs now inherit the name and doc of the original Python function.
  • Issue #928: Fix boolean return value in nested calls.
  • Issue #915: @jit called with an explicit signature with a mismatching type of arguments now raises an error.
  • Issue #784: Fix the truth value of NaNs.
  • Issue #953: Fix using shared memory in more than one function (kernel or device).
  • Issue #970: Fix an uncommon double to uint64 conversion bug on CentOS5 32-bit (C compiler issue).

9.11. Version 0.16.0

This release contains a major refactor to switch from llvmpy to llvmlite as our code generation backend. The switch is necessary to reconcile different compiler requirements for LLVM 3.5 (needs C++11) and Python extensions (need specific compiler versions on Windows). As a bonus, we have found the use of llvmlite speeds up compilation by a factor of 2!

Other Major Changes:

  • Faster dispatch for numpy structured arrays
  • Optimized array.flat()
  • Improved CPU feature selection
  • Fix constant tuple regression in macro expansion code

Known Issues:

  • AVX code generation is still disabled by default due to performance regressions when operating on misaligned NumPy arrays. We hope to have a workaround in the future.
  • In extremely rare circumstances, a known issue with LLVM 3.5 code generation can cause an ELF relocation error on 64-bit Linux systems.

9.12. Version 0.15.1

(This was a bug-fix release that superceded version 0.15 before it was announced.)


  • Workaround for missing __ftol2 on Windows XP.
  • Do not lift loops for compilation that contain break statements.
  • Fix a bug in loop-lifting when multiple values need to be returned to the enclosing scope.
  • Handle the loop-lifting case where an accumulator needs to be updated when the loop count is zero.

9.13. Version 0.15


  • Support for the Python cmath module. (NumPy complex functions were already supported.)
  • Support for .real, .imag, and .conjugate()` on non-complex numbers.
  • Add support for math.isfinite() and math.copysign().
  • Compatibility mode: If enabled (off by default), a failure to compile in object mode will fall back to using the pure Python implementation of the function.
  • Experimental support for serializing JIT functions with cloudpickle.
  • Loop-jitting in object mode now works with loops that modify scalars that are accessed after the loop, such as accumulators.
  • @vectorize functions can be compiled in object mode.
  • Numba can now be built using the Visual C++ Compiler for Python 2.7 on Windows platforms.
  • CUDA JIT functions can be returned by factory functions with variables in the closure frozen as constants.
  • Support for “optional” types in nopython mode, which allow None to be a valid value.


  • If nopython mode compilation fails for any reason, automatically fall back to object mode (unless nopython=True is passed to @jit) rather than raise an exeception.
  • Allow function objects to be returned from a function compiled in object mode.
  • Fix a linking problem that caused slower platform math functions (such as exp()) to be used on Windows, leading to performance regressions against NumPy.
  • min() and max() no longer accept scalars arguments in nopython mode.
  • Fix handling of ambigous type promotion among several compiled versions of a JIT function. The dispatcher will now compile a new version to resolve the problem. (issue #776)
  • Fix float32 to uint64 casting bug on 32-bit Linux.
  • Fix type inference to allow forced casting of return types.
  • Allow the shape of a 1D cuda.shared.array and cuda.local.array to be a one-element tuple.
  • More correct handling of signed zeros.
  • Add custom implementation of atan2() on Windows to handle special cases properly.
  • Eliminated race condition in the handling of the pagelocked staging area used when transferring CUDA arrays.
  • Fix non-deterministic type unification leading to varying performance. (issue #797)

9.14. Version 0.14


  • Support for nearly all the Numpy math functions (including comparison, logical, bitwise and some previously missing float functions) in nopython mode.
  • The Numpy datetime64 and timedelta64 dtypes are supported in nopython mode with Numpy 1.7 and later.
  • Support for Numpy math functions on complex numbers in nopython mode.
  • ndarray.sum() is supported in nopython mode.
  • Better error messages when unsupported types are used in Numpy math functions.
  • Set NUMBA_WARNINGS=1 in the environment to see which functions are compiled in object mode vs. nopython mode.
  • Add support for the two-argument pow() builtin function in nopython mode.
  • New developer documentation describing how Numba works, and how to add new types.
  • Support for Numpy record arrays on the GPU. (Note: Improper alignment of dtype fields will cause an exception to be raised.)
  • Slices on GPU device arrays.
  • GPU objects can be used as Python context managers to select the active device in a block.
  • GPU device arrays can be bound to a CUDA stream. All subsequent operations (such as memory copies) will be queued on that stream instead of the default. This can prevent unnecessary synchronization with other streams.


  • Generation of AVX instructions has been disabled to avoid performance bugs when calling external math functions that may use SSE instructions, especially on OS X.
  • JIT functions can be removed by the garbage collector when they are no longer accessible.
  • Various other reference counting fixes to prevent memory leaks.
  • Fixed handling of exception when input argument is out of range.
  • Prevent autojit functions from making unsafe numeric conversions when called with different numeric types.
  • Fix a compilation error when an unhashable global value is accessed.
  • Gracefully handle failure to enable faulthandler in the IPython Notebook.
  • Fix a bug that caused loop lifting to fail if the loop was inside an else block.
  • Fixed a problem with selecting CUDA devices in multithreaded programs on Linux.
  • The pow() function (and ** operation) applied to two integers now returns an integer rather than a float.
  • Numpy arrays using the object dtype no longer cause an exception in the autojit.
  • Attempts to write to a global array will cause compilation to fall back to object mode, rather than attempt and fail at nopython mode.
  • range() works with all negative arguments (ex: range(-10, -12, -1))

9.15. Version 0.13.4


  • Setting and deleting attributes in object mode
  • Added documentation of supported and currently unsupported numpy ufuncs
  • Assignment to 1-D numpy array slices
  • Closure variables and functions can be used in object mode
  • All numeric global values in modules can be used as constants in JIT compiled code
  • Support for the start argument in enumerate()
  • Inplace arithmetic operations (+=, -=, etc.)
  • Direct iteration over a 1D numpy array (e.g. “for x in array: ...”) in nopython mode


  • Support for NVIDIA compute capability 5.0 devices (such as the GTX 750)
  • Vectorize no longer crashes/gives an error when bool_ is used as return type
  • Return the correct dictionary when globals() is used in JIT functions
  • Fix crash bug when creating dictionary literals in object
  • Report more informative error message on import if llvmpy is too old
  • Temporarily disable pycc –header, which generates incorrect function signatures.

9.16. Version 0.13.3


  • Support for enumerate() and zip() in nopython mode
  • Increased LLVM optimization of JIT functions to -O1, enabling automatic vectorization of compiled code in some cases
  • Iteration over tuples and unpacking of tuples in nopython mode
  • Support for dict and set (Python >= 2.7) literals in object mode


  • JIT functions have the same __name__ and __doc__ as the original function.
  • Numerous improvements to better match the data types and behavior of Python math functions in JIT compiled code on different platforms.
  • Importing Numba will no longer throw an exception if the CUDA driver is present, but cannot be initialized.
  • guvectorize now properly supports functions with scalar arguments.
  • CUDA driver is lazily initialized

9.17. Version 0.13.2


  • @vectorize ufunc now can generate SIMD fast path for unit strided array
  • Added cuda.gridsize
  • Added preliminary exception handling (raise exception class)


  • Handling of closures and dynamically generated functions
  • Global None value

9.18. Version 0.13.1


  • Initial support for CUDA array slicing


  • Indirectly fixes numbapro when the system has a incompatible CUDA driver
  • Fix numba.cuda.detect
  • Export numba.intp and numba.intc

9.19. Version 0.13


  • Opensourcing NumbaPro CUDA python support in numba.cuda
  • Add support for ufunc array broadcasting
  • Add support for mixed input types for ufuncs
  • Add support for returning tuple from jitted function


  • Fix store slice bytecode handling for Python2
  • Fix inplace subtract
  • Fix pycc so that correct header is emitted
  • Allow vectorize to work on functions with jit decorator

9.20. Version 0.12.2


  • Improved NumPy ufunc support in nopython mode
  • Misc bug fixes

9.21. Version 0.12.1

This version fixed many regressions reported by user for the 0.12 release. This release contains a new loop-lifting mechanism that specializes certains loop patterns for nopython mode compilation. This avoid direct support for heap-allocating and other very dynamic operations.


  • Add loop-lifting–jit-ing loops in nopython for object mode code. This allows functions to allocate NumPy arrays and use Python objects, while the tight loops in the function can still be compiled in nopython mode. Any arrays that the tight loop uses should be created before the loop is entered.


  • Add support for majority of “math” module functions
  • Fix for...else handling
  • Add support for builtin round()
  • Fix tenary if...else support
  • Revive “numba” script
  • Fix problems with some boolean expressions
  • Add support for more NumPy ufuncs

9.22. Version 0.12

Version 0.12 contains a big refactor of the compiler. The main objective for this refactor was to simplify the code base to create a better foundation for further work. A secondary objective was to improve the worst case performance to ensure that compiled functions in object mode never run slower than pure Python code (this was a problem in several cases with the old code base). This refactor is still a work in progress and further testing is needed.

Main improvements:

  • Major refactor of compiler for performance and maintenance reasons
  • Better fallback to object mode when native mode fails
  • Improved worst case performance in object mode

The public interface of numba has been slightly changed. The idea is to make it cleaner and more rational:

  • jit decorator has been modified, so that it can be called without a signature. When called without a signature, it behaves as the old autojit. Autojit has been deprecated in favour of this approach.
  • Jitted functions can now be overloaded.
  • Added a “njit” decorator that behaves like “jit” decorator with nopython=True.
  • The numba.vectorize namespace is gone. The vectorize decorator will be in the main numba namespace.
  • Added a guvectorize decorator in the main numba namespace. It is similiar to numba.vectorize, but takes a dimension signature. It generates gufuncs. This is a replacement for the GUVectorize gufunc factory which has been deprecated.

Main regressions (will be fixed in a future release):

  • Creating new NumPy arrays is not supported in nopython mode
  • Returning NumPy arrays is not supported in nopython mode
  • NumPy array slicing is not supported in nopython mode
  • lists and tuples are not supported in nopython mode
  • string, datetime, cdecimal, and struct types are not implemented yet
  • Extension types (classes) are not supported in nopython mode
  • Closures are not supported
  • Raise keyword is not supported
  • Recursion is not support in nopython mode

9.23. Version 0.11

  • Experimental support for NumPy datetime type

9.24. Version 0.10

  • Annotation tool (./bin/numba –annotate –fancy) (thanks to Jay Bourque)
  • Open sourced prange
  • Support for raise statement
  • Pluggable array representation
  • Support for enumerate and zip (thanks to Eugene Toder)
  • Better string formatting support (thanks to Eugene Toder)
  • Builtins min(), max() and bool() (thanks to Eugene Toder)
  • Fix some code reloading issues (thanks to Björn Linse)
  • Recognize NumPy scalar objects (thanks to Björn Linse)

9.25. Version 0.9

  • Improved math support
  • Open sourced generalized ufuncs
  • Improved array expressions

9.26. Version 0.8

  • Support for autojit classes
    • Inheritance not yet supported
  • Python 3 support for pycc

  • Allow retrieval of ctypes function wrapper
    • And hence support retrieval of a pointer to the function
  • Fixed a memory leak of array slicing views

9.27. Version 0.7.2

9.28. Version 0.7.1

  • Various bug fixes

9.29. Version 0.7

  • Open sourced single-threaded ufunc vectorizer

  • Open sourced NumPy array expression compilation

  • Open sourced fast NumPy array slicing

  • Experimental Python 3 support

  • Support for typed containers
    • typed lists and tuples
  • Support for iteration over objects

  • Support object comparisons

  • Preliminary CFFI support
    • Jit calls to CFFI functions (passed into autojit functions)
    • TODO: Recognize ffi_lib.my_func attributes
  • Improved support for ctypes

  • Allow declaring extension attribute types as through class attributes

  • Support for type casting in Python
    • Get the same semantics with or without numba compilation
  • Support for recursion
    • For jit methods and extension classes
  • Allow jit functions as C callbacks

  • Friendlier error reporting

  • Internal improvements

  • A variety of bug fixes

9.30. Version 0.6.1

  • Support for bitwise operations

9.31. Version 0.6

  • Python 2.6 support

  • Programmable typing
    • Allow users to add type inference for external code
  • Better NumPy type inference
    • outer, inner, dot, vdot, tensordot, nonzero, where, binary ufuncs + methods (reduce, accumulate, reduceat, outer)
  • Type based alias analysis
    • Support for strict aliasing
  • Much faster autojit dispatch when calling from Python

  • Faster numerical loops through data and stride pre-loading

  • Integral overflow and underflow checking for conversions from objects

  • Make Meta dependency optional

9.32. Version 0.5

  • SSA-based type inference
    • Allows variable reuse
    • Allow referring to variables before lexical definition
  • Support multiple comparisons

  • Support for template types

  • List comprehensions

  • Support for pointers

  • Many bug fixes

  • Added user documentation

9.33. Version 0.4

9.34. Version 0.3.2

  • Add support for object arithmetic (issue 56).
  • Bug fixes (issue 55).

9.35. Version 0.3

  • Changed default compilation approach to ast
  • Added support for cross-module linking
  • Added support for closures (can jit inner functions and return them) (see examples/closure.py)
  • Added support for dtype structures (can access elements of structure with attribute access) (see examples/structures.py)
  • Added support for extension types (numba classes) (see examples/numbaclasses.py)
  • Added support for general Python code (use nopython to raise an error if Python C-API is used to avoid unexpected slowness because of lack of implementation defaulting to generic Python)
  • Fixed many bugs
  • Added support to detect math operations.
  • Added with python and with nopython contexts
  • Added more examples

Many features need to be documented still. Look at examples and tests for more information.

9.36. Version 0.2

  • Added an ast approach to compilation
  • Removed d, f, i, b from numba namespace (use f8, f4, i4, b1)
  • Changed function to autojit2
  • Added autojit function to decorate calls to the function and use types of the variable to create compiled versions.
  • changed keyword arguments to jit and autojit functions to restype and argtypes to be consistent with ctypes module.
  • Added pycc – a python to shared library compiler