| /dports/math/py-arviz/arviz-0.11.4/arviz/tests/base_tests/ |
| H A D | test_diagnostics_numba.py | 12 from ...utils import Numba
26 state = Numba.numba_flag
29 Numba.disable_numba()
32 Numba.enable_numba()
45 Numba.disable_numba()
47 Numba.enable_numba()
60 Numba.disable_numba()
62 Numba.enable_numba()
72 Numba.disable_numba()
74 Numba.enable_numba()
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| H A D | test_utils_numba.py | 10 from ...utils import Numba, _numba_var, numba_check
64 flag = Numba.numba_flag
66 Numba.disable_numba()
67 val = Numba.numba_flag
69 Numba.enable_numba()
70 val = Numba.numba_flag
72 assert flag == Numba.numba_flag
79 flag = Numba.numba_flag
84 Numba.disable_numba()
87 Numba.enable_numba()
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| H A D | test_stats_numba.py | 9 from ...utils import Numba
28 state = Numba.numba_flag
29 Numba.disable_numba()
31 Numba.enable_numba()
32 assert state == Numba.numba_flag
37 state = Numba.numba_flag # Store the current state of Numba
42 Numba.disable_numba()
45 Numba.enable_numba()
48 assert state == Numba.numba_flag # Ensure that initial state = final state
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| /dports/devel/py-numba/numba-0.51.2/docs/source/user/ |
| H A D | 5minguide.rst | 3 A ~5 minute guide to Numba
9 instruct Numba to compile them. When a call is made to a Numba decorated
13 Out of the box Numba works with the following:
28 Numba also has wheels available::
32 Numba can also be
34 not recommend it for first-time Numba users.
48 Will Numba work for my code?
56 Numba works well on code that looks like this::
88 Note that Pandas is not understood by Numba and as a result Numba would simply
110 How to measure the performance of Numba?
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| H A D | talks.rst | 6 the Numba team as well as Numba users. If you know of a Numba-related talk
9 Talks on Numba
12 * AnacondaCON 2018 - Accelerating Scientific Workloads with Numba - Siu Kwan Lam (`Video <https://w…
13 * `DIANA-HEP Meeting, 23 April 2018 <https://indico.cern.ch/event/709711/>`__ - Overview of Numba -…
15 Talks on Applications of Numba
19 * `DIANA-HEP Meeting, 23 April 2018 <https://indico.cern.ch/event/709711/>`_ - Use of Numba in XENO…
20 * `DIANA-HEP Meeting, 23 April 2018 <https://indico.cern.ch/event/709711/>`_ - Extending Numba for …
24 * FOSDEM 2019 - Extending Numba - Joris Geessels (`Video, Slides & Examples <https://fosdem.org/20…
25 * PyCon India 2019 - Real World Numba: Taking the Path of Least Resistance - Ankit Mahato (`Video <…
28 * SciPy 2019 - Fast Gradient Boosting Decision Trees with PyGBM and Numba - Nicholas Hug (`Video <h…
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| H A D | faq.rst | 13 As of Numba 0.39, you can, so long as the function argument has also been
38 Numba doesn't seem to care when I modify a global variable
93 Does Numba inline functions?
99 Does Numba vectorize array computations (SIMD)?
154 Does Numba automatically parallelize code?
170 Can Numba speed up short-running functions?
223 Can I "freeze" an application which uses Numba?
249 Why does Numba complain about the current locale?
271 Where does the project name "Numba" come from?
275 snakes in the world, and Numba makes your Python code fast.
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| H A D | installing.rst | 8 Numba is compatible with Python 3.6 or later, and Numpy versions 1.15 or later.
26 The easiest way to install Numba and get updates is by using ``conda``,
57 <https://pypi.org/project/numba/>`_. You can install Numba using ``pip``::
62 have LLVM installed to use Numba (in fact, Numba will ignore all LLVM
126 * Then you can install Numba from the ``numba`` channel::
138 Installing Numba from source is fairly straightforward (similar to other
142 Numba development, see :ref:`buildenv` for details on how to create a Numba
232 Numba's threading backend
236 into Numba's threading backend
246 * ``cffi`` - permits use of CFFI bindings in Numba compiled functions
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| H A D | jit.rst | 7 Numba provides several utilities for code generation, but its central
9 a function for optimization by Numba's JIT compiler. Various invocation
21 The recommended way to use the ``@jit`` decorator is to let Numba decide
32 execution. Numba will infer the argument types at call time, and generate
33 optimized code based on this information. Numba will also be able to
46 You can also tell Numba the function signature you are expecting. The
63 ``int32(int32, int32)``, Numba will try to infer it for you. Function
82 Numba-compiled functions can call other compiled functions. The function
95 otherwise Numba may generate much slower code.
134 Numba has two compilation modes: :term:`nopython mode` and
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| /dports/devel/py-numba/numba-0.51.2/docs/source/reference/ |
| H A D | python27-eol.rst | 7 projects, the Numba team has to consider how to time its own end of Python 2.7
8 support. Given how deeply Numba must interface with the Python interpreter,
10 In addition, Numba (specifically via llvmlite) has to deal with some tricky
20 Due to Numba's tight coupling with NumPy, the NumPy timeline has
21 informed the Numba timeline below.
27 to get more improvements into Numba before ending Python 2.7 support. Note
28 that the Numba versions mentioned below are more concrete than the dates.
30 The end of Python 2.7 support in Numba will be staged:
32 * **Numba 0.47**: (~Dec 2019) This is will be the last version of Numba to
34 * **Numba 0.48 development cycle**: (Jan-Mar 2020) Python 2 support will be
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| H A D | types.rst | 10 As an optimizing compiler, Numba needs to decide on the type of each
15 You will encounter Numba types mainly when trying to inspect the results
16 of Numba's type inference, for :ref:`debugging <numba-envvars>` or
26 compilation), but signatures always involve some representation of Numba
48 by Numba and their aliases.
111 input arguments to output values. Within Numba :term:`JIT` compiled
116 First-class function support is enabled for all Numba :term:`JIT`
196 WAP instance to a Numba :term:`JIT` compiled function.
199 method. This is necessary when calling WAP objects from Numba
240 functions or classes provided by Numba.
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| /dports/devel/py-numba/numba-0.51.2/docs/source/developer/ |
| H A D | roadmap.rst | 2 Numba Project Roadmap
19 * Numba dictionary support
40 * Enhance description of interfaces provided by Numba functions to give more type information
41 * Convert older Numba function implementations to use public extension mechanisms
48 * Declare Numba 1.0 with stable interfaces
50 * Continue refactoring Numba internals to solve common bug reports (see above)
53 * Improve memory management of Numba-allocated memory
66 * Call Numba from other languages,
67 * Call from Numba into other languages
71 * Foster integration of Numba into core PyData packages:
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| H A D | hashing.rst | 6 Numba supports the built-in :func:`hash` and does so by simply calling the
23 The implementation of the Numba hashing functions strictly follows that of
26 ``siphash24`` (default in CPython 3). As a result Numba will match Python 3
32 Both Numba and CPython Unicode string internal representations have a ``hash``
35 a value from cache as it is considerably cheaper to do so. The Numba Unicode
38 potential change in performance) is that Numba always computes and caches the
42 should also be noted that Numba copies in the ``hash`` member of the CPython
51 hashing algorithms for e.g. the purposes of reproduciblity. The Numba hashing
53 as a result the influence of ``PYTHONHASHSEED`` is replicated in Numba's
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| H A D | contributing.rst | 2 Contributing to Numba
5 We welcome people who want to make contributions to Numba, big or small!
16 Numba uses Gitter for public real-time chat. To help improve the
19 * `numba/numba <https://gitter.im/numba/numba>`_: General Numba discussion,
51 `Numba wiki <https://github.com/numba/numba/wiki/Meeting-Minutes>`_.
129 Building Numba
141 C files inside the Numba source tree.
151 running Numba from a source checkout, you can type ``./runtests.py``
290 The Numba documentation is split over two repositories:
293 `Numba repository <https://github.com/numba/numba>`_.
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| H A D | dispatching.rst | 19 Each argument weighs in the selection based on its :ref:`Numba type
20 <numba-types>`. Numba types are often more granular than Python types:
21 for example, Numba types Numpy arrays differently depending on their
24 Once a Numba type is inferred for each argument, a specialization must
34 1. infer the Numba types of the concrete arguments
36 for the inferred Numba types
65 Python type, query various properties to infer the appropriate Numba
86 the overhead of the Numba type system by working with simple integer
89 The goal of the type resolution step becomes: infer a Numba *typecode*
91 with Numba types anymore...
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| /dports/devel/py-numba/numba-0.51.2/docs/source/ |
| H A D | glossary.rst | 23 An expression whose value Numba can infer and freeze at compile-time.
33 Shorthand for "a function :term:`JIT-compiled <JIT>` with Numba using
45 The act of translating :term:`Numba IR` into LLVM IR. The term
47 machine-specific while Numba IR is high-level and abstract.
57 Numba IR
58 Numba intermediate representation
64 A Numba compilation mode that generates code that handles all values
80 The process by which Numba determines the specialized types of all
82 if arguments or globals have Python types unknown to Numba, or if
83 functions are used that are not recognized by Numba. Successful
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| /dports/devel/py-numba/numba-0.51.2/docs/source/extending/ |
| H A D | entrypoints.rst | 5 extensions to the Numba compiler. In those situations, the new types and
6 overloads can registered with Numba when the package is imported by the user.
7 However, there are situations where a Numba extension would not normally be
8 imported directly by the user, but must still be registered with the Numba
11 SciPy functions to Numba. The end user does not need to ``import
15 Numba discovers extensions using the `entry points
26 A package can register an initialization function with Numba by adding the
41 Numba currently only looks for the ``init`` entry point in the
45 overloads, or call other Numba extension APIs. The order of initialization of
51 Numba loads all entry points when the first function is compiled. To test your
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| H A D | index.rst | 2 Extending Numba
7 This chapter describes how to extend Numba to make it recognize and support
8 additional operations, functions or types. Numba provides two categories
12 for simple uses. They require little knowledge of Numba's internal
15 * The low-level APIs reflect Numba's internal compilation chain and allow
17 and experience with Numba internals.
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| H A D | interval-example.rst | 5 We will extend the Numba frontend to support a class that it does not
8 * Passing an instance of the class to a Numba function
9 * Accessing attributes of the class in a Numba function
39 Creating a new Numba type
42 As the ``Interval`` class is not known to Numba, we must create a new Numba
63 In itself, creating a Numba type doesn't do anything. We must teach Numba
111 We have finished teaching Numba about our type inference additions.
155 the same names for use in Numba functions. Numba provides a convenience
266 interval values from Numba functions::
315 * Teach Numba how to infer the Numba type of Python values of a certain class,
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| /dports/devel/py-numba/numba-0.51.2/ |
| H A D | README.rst | 2 Numba chapter
16 Numba is an open source, NumPy-aware optimizing compiler for Python sponsored
20 Numba can compile a large subset of numerically-focused Python, including many
21 NumPy functions. Additionally, Numba has support for automatic
25 For more information about Numba, see the Numba homepage:
59 The easiest way to install Numba and get updates is by using the Anaconda
77 Join the Numba mailing list numba-users@continuum.io:
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| H A D | PKG-INFO | 10 Numba
24 Numba is an open source, NumPy-aware optimizing compiler for Python sponsored
28 Numba can compile a large subset of numerically-focused Python, including many
29 NumPy functions. Additionally, Numba has support for automatic
33 For more information about Numba, see the Numba homepage:
67 The easiest way to install Numba and get updates is by using the Anaconda
85 Join the Numba mailing list numba-users@continuum.io:
|
| /dports/www/grafana8/grafana-8.3.6/vendor/github.com/apache/arrow/docs/source/python/ |
| H A D | cuda.rst | 80 Numba Integration
84 but they support interoperation with `Numba <https://numba.pydata.org/>`_,
87 Arrow to Numba
90 First let's define a Numba CUDA kernel operating on an ``int32`` array. Here,
101 Then we need to wrap our CUDA buffer into a Numba "device array" with the right
102 array metadata (shape, strides and datatype). This is necessary so that Numba
113 memory without losing type information, and then invoked the Numba kernel on it
116 Finally we can run the Numba CUDA kernel on the Numba device array (here
126 Numba to Arrow
132 For the sake of example, let's first create a Numba device array::
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| /dports/databases/arrow/apache-arrow-6.0.1/docs/source/python/ |
| H A D | cuda.rst | 80 Numba Integration
84 but they support interoperation with `Numba <https://numba.pydata.org/>`_,
87 Arrow to Numba
90 First let's define a Numba CUDA kernel operating on an ``int32`` array. Here,
101 Then we need to wrap our CUDA buffer into a Numba "device array" with the right
102 array metadata (shape, strides and datatype). This is necessary so that Numba
113 memory without losing type information, and then invoked the Numba kernel on it
116 Finally we can run the Numba CUDA kernel on the Numba device array (here
126 Numba to Arrow
132 For the sake of example, let's first create a Numba device array::
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| /dports/security/vault/vault-1.8.2/vendor/github.com/apache/arrow/docs/source/python/ |
| H A D | cuda.rst | 80 Numba Integration
84 but they support interoperation with `Numba <https://numba.pydata.org/>`_,
87 Arrow to Numba
90 First let's define a Numba CUDA kernel operating on an ``int32`` array. Here,
101 Then we need to wrap our CUDA buffer into a Numba "device array" with the right
102 array metadata (shape, strides and datatype). This is necessary so that Numba
113 memory without losing type information, and then invoked the Numba kernel on it
116 Finally we can run the Numba CUDA kernel on the Numba device array (here
126 Numba to Arrow
132 For the sake of example, let's first create a Numba device array::
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| /dports/devel/py-numba/numba-0.51.2/docs/source/proposals/ |
| H A D | external-memory-management.rst | 38 Provide an *External Memory Manager (EMM)* interface in Numba.
139 Numba.
260 need to be invoked directly by a Numba user.
292 compatible with the version of Numba in use.
457 Numba as a Dependency
475 Plugin, is only imported if Numba is already present. If Numba is not available,
481 environments without Numba, to avoid introducing an accidental Numba dependency.
628 Numba internal changes
740 is handled within Numba.
858 Numba CUDA Unit tests
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| /dports/devel/py-numba/numba-0.51.2/docs/source/cuda/ |
| H A D | overview.rst | 5 Numba supports CUDA GPU programming by directly compiling a restricted subset
7 execution model. Kernels written in Numba appear to have direct access
29 Most CUDA programming facilities exposed by Numba map directly to the CUDA
40 Numba supports CUDA-enabled GPU with compute capability 2.0 or above with an
52 toolkit, the following describe how Numba searches for a CUDA toolkit
60 Numba searches for a CUDA toolkit installation in the following order:
71 <(https://developer.nvidia.com/cuda-downloads)>`_, the CUDA target in Numba
84 Numba does not implement all features of CUDA, yet. Some missing features
|