esc · Dec 9, 2022 · Dec 7, 2022 · Aug 7, 2022 · Dec 7, 2022 · Dec 9, 2022
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -12,8 +12,8 @@ jobs:
     name: macOS
     vmImage: macos-11
     matrix:
-      py37_np118:
-        PYTHON: '3.7'
+      py38_np118:
+        PYTHON: '3.8'
         NUMPY: '1.18'
         CONDA_ENV: 'azure_ci'
         TEST_START_INDEX: 0
@@ -29,8 +29,8 @@ jobs:
     name: Linux
     vmImage: ubuntu-20.04
     matrix:
-      py37_np118_vanilla:
-        PYTHON: '3.7'
+      py38_np118_vanilla:
+        PYTHON: '3.8'
         NUMPY: '1.18'
         CONDA_ENV: azure_ci
         VANILLA_INSTALL: yes
@@ -67,11 +67,10 @@ jobs:
         CONDA_ENV: azure_ci
         BUILD_DOC: yes
         TEST_START_INDEX: 7
-      py38_np120_pickle5:
+      py38_np120:
         PYTHON: '3.8'
         NUMPY: '1.20'
         CONDA_ENV: azure_ci
-        TEST_PICKLE5: yes
         TEST_START_INDEX: 8
       py38_np120_svml:
         PYTHON: '3.8'

diff --git a/buildscripts/azure/azure-windows.yml b/buildscripts/azure/azure-windows.yml
@@ -13,8 +13,8 @@ jobs:
         NUMPY: '1.23'
         CONDA_ENV: 'testenv'
         TEST_START_INDEX: 17
-      py37_np118:
-        PYTHON: '3.7'
+      py38_np118:
+        PYTHON: '3.8'
         NUMPY: '1.18'
         CONDA_ENV: 'testenv'
         TEST_START_INDEX: 18

diff --git a/buildscripts/condarecipe.local/meta.yaml b/buildscripts/condarecipe.local/meta.yaml
@@ -42,7 +42,7 @@ requirements:
     #       available.
     - tbb-devel >=2021,<2021.6       # [not (aarch64 or ppc64le)]
   run:
-    - python >=3.7
+    - python >=3.8
     # NumPy 1.22.0, 1.22.1, 1.22.2 are all broken for ufuncs, see #7756
     - numpy >=1.18, !=1.22.0, !=1.22.1, !=1.22.2
     - setuptools

diff --git a/buildscripts/incremental/setup_conda_environment.sh b/buildscripts/incremental/setup_conda_environment.sh
@@ -76,8 +76,6 @@ if [ "$RUN_COVERAGE" == "yes" ]; then $PIP_INSTALL codecov; fi
 if [ "$TEST_SVML" == "yes" ]; then $CONDA_INSTALL -c numba icc_rt; fi
 # Install Intel TBB parallel backend
 if [ "$TEST_THREADING" == "tbb" ]; then $CONDA_INSTALL -c numba tbb=2021 "tbb-devel>=2021,<2021.6"; fi
-# Install pickle5
-if [ "$TEST_PICKLE5" == "yes" ]; then $PIP_INSTALL pickle5; fi
 # Install typeguard
 if [ "$RUN_TYPEGUARD" == "yes" ]; then $CONDA_INSTALL conda-forge::typeguard; fi
 

diff --git a/docs/environment.yml b/docs/environment.yml
@@ -5,7 +5,7 @@ name: rtd
 channels:
   - numba/label/dev
 dependencies:
-  - python=3.7
+  - python=3.8
   - llvmlite=0.40
   - numpy
   - numpydoc

diff --git a/docs/source/cuda/cudapysupported.rst b/docs/source/cuda/cudapysupported.rst
@@ -222,7 +222,7 @@ The following functions from the :mod:`math` module are supported:
 * :func:`math.log10`
 * :func:`math.log1p`
 * :func:`math.sqrt`
-* :func:`math.remainder`: Python 3.7+
+* :func:`math.remainder`
 * :func:`math.pow`
 * :func:`math.ceil`
 * :func:`math.floor`

diff --git a/docs/source/developer/caching.rst b/docs/source/developer/caching.rst
@@ -20,10 +20,6 @@ overloaded signatures compiled for the function. The *object code* is stored in
 files with an ``.nbc`` extension, one file per overload. The data in both files
 is serialized with :mod:`pickle`.
 
-.. note:: On Python <=3.7, Numba extends ``pickle`` using the pure-Python
-          pickler. To use the faster C Pickler, install ``pickle5``
-          from ``pip``. ``pickle5`` backports Python 3.8 pickler features.
-
 
 Requirements for Cacheability
 -----------------------------

diff --git a/docs/source/user/cli.rst b/docs/source/user/cli.rst
@@ -82,60 +82,90 @@ Example output::
     System info:
     --------------------------------------------------------------------------------
     __Time Stamp__
-    2019-05-07 14:15:39.733994
+    Report started (local time)                   : 2022-11-30 15:40:42.368114
+    UTC start time                                : 2022-11-30 15:40:42.368129
+    Running time (s)                              : 2.563586
 
     __Hardware Information__
     Machine                                       : x86_64
-    CPU Name                                      : haswell
-    CPU count                                     : 8
-    CPU Features                                  : 
-    aes avx avx2 bmi bmi2 cmov cx16 f16c fma fsgsbase invpcid lzcnt mmx movbe pclmul
-    popcnt rdrnd sahf sse sse2 sse3 sse4.1 sse4.2 ssse3 xsave xsaveopt
+    CPU Name                                      : ivybridge
+    CPU Count                                     : 3
+    Number of accessible CPUs                     : ?
+    List of accessible CPUs cores                 : ?
+    CFS Restrictions (CPUs worth of runtime)      : None
+
+    CPU Features                                  : 64bit aes avx cmov cx16 cx8 f16c
+                                                    fsgsbase fxsr mmx pclmul popcnt
+                                                    rdrnd sahf sse sse2 sse3 sse4.1
+                                                    sse4.2 ssse3 xsave
+
+    Memory Total (MB)                             : 14336
+    Memory Available (MB)                         : 11540
 
     __OS Information__
-    Platform                                      : Darwin-18.5.0-x86_64-i386-64bit
-    Release                                       : 18.5.0
-    System Name                                   : Darwin
-    Version                                       : Darwin Kernel Version 18.5.0: Mon Mar 11 20:40:32 PDT 2019; root:xnu-4903.251.3~3/RELEASE_X86_64
-    OS specific info                              : 10.14.4   x86_64
+    Platform Name                                 : macOS-10.16-x86_64-i386-64bit
+    Platform Release                              : 20.6.0
+    OS Name                                       : Darwin
+    OS Version                                    : Darwin Kernel Version 20.6.0: Thu Sep 29 20:15:11 PDT 2022; root:xnu-7195.141.42~1/RELEASE_X86_64
+    OS Specific Version                           : 10.16   x86_64
+    Libc Version                                  : ?
 
     __Python Information__
-    Python Compiler                               : Clang 4.0.1 (tags/RELEASE_401/final)
+    Python Compiler                               : Clang 14.0.6
     Python Implementation                         : CPython
-    Python Version                                : 3.7.3
-    Python Locale                                 : en_US UTF-8
+    Python Version                                : 3.10.8
+    Python Locale                                 : en_US.UTF-8
 
-    __LLVM information__
-    LLVM version                                  : 7.0.0
+    __Numba Toolchain Versions__
+    Numba Version                                 : 0+untagged.gb91eec710
+    llvmlite Version                              : 0.40.0dev0+43.g7783803
+
+    __LLVM Information__
+    LLVM Version                                  : 11.1.0
 
     __CUDA Information__
-    CUDA driver library cannot be found or no CUDA enabled devices are present.
-    Error class: <class 'numba.cuda.cudadrv.error.CudaSupportError'>
+    CUDA Device Initialized                       : False
+    CUDA Driver Version                           : ?
+    CUDA Runtime Version                          : ?
+    CUDA NVIDIA Bindings Available                : ?
+    CUDA NVIDIA Bindings In Use                   : ?
+    CUDA Detect Output:
+    None
+    CUDA Libraries Test Output:
+    None
+
+    __NumPy Information__
+    NumPy Version                                 : 1.23.4
+    NumPy Supported SIMD features                 : ('MMX', 'SSE', 'SSE2', 'SSE3', 'SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C')
+    NumPy Supported SIMD dispatch                 : ('SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C', 'FMA3', 'AVX2', 'AVX512F', 'AVX512CD', 'AVX512_KNL', 'AVX512_SKX', 'AVX512_CLX', 'AVX512_CNL', 'AVX512_ICL')
+    NumPy Supported SIMD baseline                 : ('SSE', 'SSE2', 'SSE3')
+    NumPy AVX512_SKX support detected             : False
 
     __SVML Information__
-    SVML state, config.USING_SVML                 : False
-    SVML library found and loaded                 : False
-    llvmlite using SVML patched LLVM              : True
-    SVML operational                              : False
+    SVML State, config.USING_SVML                 : False
+    SVML Library Loaded                           : False
+    llvmlite Using SVML Patched LLVM              : True
+    SVML Operational                              : False
 
     __Threading Layer Information__
-    TBB Threading layer available                 : False
-    +--> Disabled due to                          : Unknown import problem.
-    OpenMP Threading layer available              : False
-    +--> Disabled due to                          : Unknown import problem.
-    Workqueue Threading layer available           : True
+    TBB Threading Layer Available                 : True
+    +-->TBB imported successfully.
+    OpenMP Threading Layer Available              : True
+    +-->Vendor: Intel
+    Workqueue Threading Layer Available           : True
+    +-->Workqueue imported successfully.
 
     __Numba Environment Variable Information__
-    None set.
+    None found.
 
     __Conda Information__
-    conda_build_version                           : 3.17.8
-    conda_env_version                             : 4.6.14
-    platform                                      : osx-64
-    python_version                                : 3.7.3.final.0
-    root_writable                                 : True
+    Conda Build                                   : not installed
+    Conda Env                                     : 4.12.0
+    Conda Platform                                : osx-64
+    Conda Python Version                          : 3.9.12.final.0
+    Conda Root Writable                           : True
 
-    __Current Conda Env__
+    __Installed Packages__
     (output truncated due to length)
 
 .. _cli_debug:
@@ -153,7 +183,7 @@ To try it out, create an example script called ``myscript.py``::
     @numba.jit
     def f(x):
         return 2 * x
-    
+
     f(42)
 
 and then execute one of the following commands::

diff --git a/docs/source/user/installing.rst b/docs/source/user/installing.rst
@@ -5,7 +5,7 @@ Installation
 Compatibility
 -------------
 
-Numba is compatible with Python 3.7--3.10, and NumPy versions 1.18 or later.
+Numba is compatible with Python 3.8--3.10, and NumPy versions 1.18 or later.
 
 Our supported platforms are:
 
@@ -229,8 +229,6 @@ vary with target operating system and hardware. The following lists them all
     inspection. `See here <https://github.com/radareorg/radare2>`_ for
     information on obtaining and installing.
   * ``graphviz`` - for some CFG inspection functionality.
-  * ``pickle5`` - provides Python 3.8 pickling features for faster pickling in
-    Python 3.7.
   * ``typeguard`` - used by ``runtests.py`` for
     :ref:`runtime type-checking <type_anno_check>`.
   * ``cuda-python`` - The NVIDIA CUDA Python bindings. See :ref:`cuda-bindings`.

diff --git a/numba/__init__.py b/numba/__init__.py
@@ -137,8 +137,8 @@ def _ensure_critical_deps():
     from numba.np.numpy_support import numpy_version
     from numba.core.utils import PYVERSION
 
-    if PYVERSION < (3, 7):
-        msg = ("Numba needs Python 3.7 or greater. Got Python "
+    if PYVERSION < (3, 8):
+        msg = ("Numba needs Python 3.8 or greater. Got Python "
                f"{PYVERSION[0]}.{PYVERSION[1]}.")
         raise ImportError(msg)
 

diff --git a/numba/_dispatcher.cpp b/numba/_dispatcher.cpp
@@ -1155,15 +1155,11 @@ static PyTypeObject DispatcherType = {
     0,                                           /* tp_del */
     0,                                           /* tp_version_tag */
     0,                                           /* tp_finalize */
-#if PY_MAJOR_VERSION == 3
-/* Python 3.8 has two slots, 3.9 has one. */
-#if PY_MINOR_VERSION > 7
     0,                                           /* tp_vectorcall */
-#if PY_MINOR_VERSION == 8
+/* Python 3.8 has two slots. */
+#if (PY_MAJOR_VERSION == 3) && (PY_MINOR_VERSION == 8)
     0,                                           /* tp_print */
 #endif
-#endif
-#endif
 };
 
 

diff --git a/numba/_dynfunc.c b/numba/_dynfunc.c
@@ -135,15 +135,11 @@ static PyTypeObject EnvironmentType = {
     0,                         /* tp_del */
     0,                         /* tp_version_tag */
     0,                         /* tp_finalize */
-#if PY_MAJOR_VERSION == 3
-/* Python 3.8 has two slots, 3.9 has one. */
-#if PY_MINOR_VERSION > 7
     0,                         /* tp_vectorcall */
-#if PY_MINOR_VERSION == 8
+/* Python 3.8 has two slots. */
+#if (PY_MAJOR_VERSION == 3) && (PY_MINOR_VERSION == 8)
     0,                         /* tp_print */
 #endif
-#endif
-#endif
 };
 
 /* A closure object is created for each call to make_function(), and stored
@@ -245,15 +241,11 @@ static PyTypeObject ClosureType = {
     0,                         /* tp_del */
     0,                         /* tp_version_tag */
     0,                         /* tp_finalize */
-#if PY_MAJOR_VERSION == 3
-/* Python 3.8 has two slots, 3.9 has one. */
-#if PY_MINOR_VERSION > 7
     0,                         /* tp_vectorcall */
-#if PY_MINOR_VERSION == 8
+/* Python 3.8 has two slots. */
+#if (PY_MAJOR_VERSION == 3) && (PY_MINOR_VERSION == 8)
     0,                         /* tp_print */
 #endif
-#endif
-#endif
 };
 
 
@@ -382,13 +374,7 @@ generator_dealloc(GeneratorObject *gen)
         PyObject_ClearWeakRefs((PyObject *) gen);
     /* XXX The finalizer may be called after the LLVM module has been
        destroyed (typically at interpreter shutdown) */
-#if PY_MAJOR_VERSION >= 3
-#if PY_MINOR_VERSION >= 7
     if (!_Py_IsFinalizing())
-#else
-    if (!_Py_Finalizing)
-#endif
-#endif
         if (gen->finalizer != NULL)
             gen->finalizer(gen->state);
     Py_XDECREF(gen->env);
@@ -462,15 +448,11 @@ static PyTypeObject GeneratorType = {
     0,                                        /* tp_del */
     0,                                        /* tp_version_tag */
     0,                                        /* tp_finalize */
-#if PY_MAJOR_VERSION == 3
-/* Python 3.8 has two slots, 3.9 has one. */
-#if PY_MINOR_VERSION > 7
     0,                         /* tp_vectorcall */
-#if PY_MINOR_VERSION == 8
+/* Python 3.8 has two slots. */
+#if (PY_MAJOR_VERSION == 3) && (PY_MINOR_VERSION == 8)
     0,                         /* tp_print */
 #endif
-#endif
-#endif
 };
 
 /* Dynamically create a new generator object */

diff --git a/numba/_helperlib.c b/numba/_helperlib.c
@@ -867,11 +867,7 @@ int reraise_exc_is_none(void) {
     /* Reraise */
     PyThreadState *tstate = PyThreadState_GET();
     PyObject *tb, *type, *value;
-#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION >= 7)
     _PyErr_StackItem *tstate_exc = tstate->exc_info;
-#else
-    PyThreadState *tstate_exc = tstate;
-#endif
     type = tstate_exc->exc_type;
     value = tstate_exc->exc_value;
     tb = tstate_exc->exc_traceback;

diff --git a/numba/_version.py b/numba/_version.py
@@ -45,9 +45,7 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
         if verbose:
             print("unable to find command, tried %s" % (commands,))
         return None
-    stdout = p.communicate()[0].strip()
-    if sys.version_info[0] >= 3:
-        stdout = stdout.decode()
+    stdout = p.communicate()[0].strip().decode()
     if p.returncode != 0:
         if verbose:
             print("unable to run %s (error)" % args[0])

diff --git a/numba/core/base.py b/numba/core/base.py
@@ -3,6 +3,7 @@
 import sys
 from itertools import permutations, takewhile
 from contextlib import contextmanager
+from functools import cached_property
 
 from llvmlite import ir as llvmir
 from llvmlite.ir import Constant
@@ -329,14 +330,14 @@ def get_data_packer(self, fe_types):
     def target_data(self):
         raise NotImplementedError
 
-    @utils.cached_property
+    @cached_property
     def nonconst_module_attrs(self):
         """
         All module attrs are constant for targets using BaseContext.
         """
         return tuple()
 
-    @utils.cached_property
+    @cached_property
     def nrt(self):
         from numba.core.runtime.context import NRTContext
         return NRTContext(self, self.enable_nrt)