automatic import of python-threadpoolctl

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+/threadpoolctl-3.1.0.tar.gz
diff --git a/python-threadpoolctl.spec b/python-threadpoolctl.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-threadpoolctl
+Version:	3.1.0
+Release:	1
+Summary:	threadpoolctl
+License:	BSD-3-Clause
+URL:		https://github.com/joblib/threadpoolctl
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/1b/c7/3d85f8b3894ba7228d0c74e16e97a36a72b2cd2b0e0f8f89b5d435d11f71/threadpoolctl-3.1.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Thread-pool Controls [![Build Status](https://dev.azure.com/joblib/threadpoolctl/_apis/build/status/joblib.threadpoolctl?branchName=master)](https://dev.azure.com/joblib/threadpoolctl/_build/latest?definitionId=1&branchName=master) [![codecov](https://codecov.io/gh/joblib/threadpoolctl/branch/master/graph/badge.svg)](https://codecov.io/gh/joblib/threadpoolctl)
+
+Python helpers to limit the number of threads used in the
+threadpool-backed of common native libraries used for scientific
+computing and data science (e.g. BLAS and OpenMP).
+
+Fine control of the underlying thread-pool size can be useful in
+workloads that involve nested parallelism so as to mitigate
+oversubscription issues.
+
+## Installation
+
+- For users, install the last published version from PyPI:
+
+  ```bash
+  pip install threadpoolctl
+  ```
+
+- For contributors, install from the source repository in developer
+  mode:
+
+  ```bash
+  pip install -r dev-requirements.txt
+  flit install --symlink
+  ```
+
+  then you run the tests with pytest:
+
+  ```bash
+  pytest
+  ```
+
+## Usage
+
+### Command Line Interface
+
+Get a JSON description of thread-pools initialized when importing python
+packages such as numpy or scipy for instance:
+
+```
+python -m threadpoolctl -i numpy scipy.linalg
+[
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so",
+    "prefix": "libmkl_rt",
+    "user_api": "blas",
+    "internal_api": "mkl",
+    "version": "2019.0.4",
+    "num_threads": 2,
+    "threading_layer": "intel"
+  },
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so",
+    "prefix": "libiomp",
+    "user_api": "openmp",
+    "internal_api": "openmp",
+    "version": null,
+    "num_threads": 4
+  }
+]
+```
+
+The JSON information is written on STDOUT. If some of the packages are missing,
+a warning message is displayed on STDERR.
+
+### Python Runtime Programmatic Introspection
+
+Introspect the current state of the threadpool-enabled runtime libraries
+that are loaded when importing Python packages:
+
+```python
+>>> from threadpoolctl import threadpool_info
+>>> from pprint import pprint
+>>> pprint(threadpool_info())
+[]
+
+>>> import numpy
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None}]
+
+>>> import xgboost
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libgomp.so.1.0.0',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libgomp',
+  'user_api': 'openmp',
+  'version': None}]
+```
+
+In the above example, `numpy` was installed from the default anaconda channel and comes
+with MKL and its Intel OpenMP (`libiomp5`) implementation while `xgboost` was installed
+from pypi.org and links against GNU OpenMP (`libgomp`) so both OpenMP runtimes are
+loaded in the same Python program.
+
+The state of these libraries is also accessible through the object oriented API:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_info
+>>> from pprint import pprint
+>>> import numpy
+>>> controller = ThreadpoolController()
+>>> pprint(controller.info())
+[{'architecture': 'Haswell',
+  'filepath': '/home/jeremie/miniconda/envs/dev/lib/libopenblasp-r0.3.17.so',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'threading_layer': 'pthreads',
+  'user_api': 'blas',
+  'version': '0.3.17'}]
+
+>>> controller.info() == threadpool_info()
+True
+```
+
+### Setting the Maximum Size of Thread-Pools
+
+Control the number of threads used by the underlying runtime libraries
+in specific sections of your Python program:
+
+```python
+>>> from threadpoolctl import threadpool_limits
+>>> import numpy as np
+
+>>> with threadpool_limits(limits=1, user_api='blas'):
+...     # In this block, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread. They can thus be used jointly
+...     # with thread-parallelism.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+The threadpools can also be controlled via the object oriented API, which is especially
+useful to avoid searching through all the loaded shared libraries each time. It will
+however not act on libraries loaded after the instantiation of the
+`ThreadpoolController`:
+
+```python
+>>> from threadpoolctl import ThreadpoolController
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> with controller.limit(limits=1, user_api='blas'):
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+### Restricting the limits to the scope of a function
+
+`threadpool_limits` and `ThreadpoolController` can also be used as decorators to set
+the maximum number of threads used by the supported libraries at a function level. The
+decorators are accessible through their `wrap` method:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_limits
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> @controller.wrap(limits=1, user_api='blas')
+... # or @threadpool_limits.wrap(limits=1, user_api='blas')
+... def my_func():
+...     # Inside this function, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+...
+```
+
+### Sequential BLAS within OpenMP parallel region
+
+When one wants to have sequential BLAS calls within an OpenMP parallel region, it's
+safer to set `limits="sequential_blas_under_openmp"` since setting `limits=1` and `user_api="blas"` might not lead to the expected behavior in some configurations
+(e.g. OpenBLAS with the OpenMP threading layer
+https://github.com/xianyi/OpenBLAS/issues/2985).
+
+### Known Limitations
+
+- `threadpool_limits` can fail to limit the number of inner threads when nesting
+  parallel loops managed by distinct OpenMP runtime implementations (for instance
+  libgomp from GCC and libomp from clang/llvm or libiomp from ICC).
+
+  See the `test_openmp_nesting` function in [tests/test_threadpoolctl.py](
+  https://github.com/joblib/threadpoolctl/blob/master/tests/test_threadpoolctl.py)
+  for an example. More information can be found at:
+  https://github.com/jeremiedbb/Nested_OpenMP
+
+  Note however that this problem does not happen when `threadpool_limits` is
+  used to limit the number of threads used internally by BLAS calls that are
+  themselves nested under OpenMP parallel loops. `threadpool_limits` works as
+  expected, even if the inner BLAS implementation relies on a distinct OpenMP
+  implementation.
+
+- Using Intel OpenMP (ICC) and LLVM OpenMP (clang) in the same Python program
+  under Linux is known to cause problems. See the following guide for more details
+  and workarounds:
+  https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md
+
+- Setting the maximum number of threads of the OpenMP and BLAS libraries has a global
+  effect and impacts the whole Python process. There is no thread level isolation as
+  these libraries do not offer thread-local APIs to configure the number of threads to
+  use in nested parallel calls.
+
+
+## Maintainers
+
+To make a release:
+
+Bump the version number (`__version__`) in `threadpoolctl.py`.
+
+Build the distribution archives:
+
+```bash
+pip install flit
+flit build
+```
+
+Check the contents of `dist/`.
+
+If everything is fine, make a commit for the release, tag it, push the
+tag to github and then:
+
+```bash
+flit publish
+```
+
+### Credits
+
+The initial dynamic library introspection code was written by @anton-malakhov
+for the smp package available at https://github.com/IntelPython/smp .
+
+threadpoolctl extends this for other operating systems. Contrary to smp,
+threadpoolctl does not attempt to limit the size of Python multiprocessing
+pools (threads or processes) or set operating system-level CPU affinity
+constraints: threadpoolctl only interacts with native libraries via their
+public runtime APIs.
+
+
+%package -n python3-threadpoolctl
+Summary:	threadpoolctl
+Provides:	python-threadpoolctl
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-threadpoolctl
+# Thread-pool Controls [![Build Status](https://dev.azure.com/joblib/threadpoolctl/_apis/build/status/joblib.threadpoolctl?branchName=master)](https://dev.azure.com/joblib/threadpoolctl/_build/latest?definitionId=1&branchName=master) [![codecov](https://codecov.io/gh/joblib/threadpoolctl/branch/master/graph/badge.svg)](https://codecov.io/gh/joblib/threadpoolctl)
+
+Python helpers to limit the number of threads used in the
+threadpool-backed of common native libraries used for scientific
+computing and data science (e.g. BLAS and OpenMP).
+
+Fine control of the underlying thread-pool size can be useful in
+workloads that involve nested parallelism so as to mitigate
+oversubscription issues.
+
+## Installation
+
+- For users, install the last published version from PyPI:
+
+  ```bash
+  pip install threadpoolctl
+  ```
+
+- For contributors, install from the source repository in developer
+  mode:
+
+  ```bash
+  pip install -r dev-requirements.txt
+  flit install --symlink
+  ```
+
+  then you run the tests with pytest:
+
+  ```bash
+  pytest
+  ```
+
+## Usage
+
+### Command Line Interface
+
+Get a JSON description of thread-pools initialized when importing python
+packages such as numpy or scipy for instance:
+
+```
+python -m threadpoolctl -i numpy scipy.linalg
+[
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so",
+    "prefix": "libmkl_rt",
+    "user_api": "blas",
+    "internal_api": "mkl",
+    "version": "2019.0.4",
+    "num_threads": 2,
+    "threading_layer": "intel"
+  },
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so",
+    "prefix": "libiomp",
+    "user_api": "openmp",
+    "internal_api": "openmp",
+    "version": null,
+    "num_threads": 4
+  }
+]
+```
+
+The JSON information is written on STDOUT. If some of the packages are missing,
+a warning message is displayed on STDERR.
+
+### Python Runtime Programmatic Introspection
+
+Introspect the current state of the threadpool-enabled runtime libraries
+that are loaded when importing Python packages:
+
+```python
+>>> from threadpoolctl import threadpool_info
+>>> from pprint import pprint
+>>> pprint(threadpool_info())
+[]
+
+>>> import numpy
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None}]
+
+>>> import xgboost
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libgomp.so.1.0.0',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libgomp',
+  'user_api': 'openmp',
+  'version': None}]
+```
+
+In the above example, `numpy` was installed from the default anaconda channel and comes
+with MKL and its Intel OpenMP (`libiomp5`) implementation while `xgboost` was installed
+from pypi.org and links against GNU OpenMP (`libgomp`) so both OpenMP runtimes are
+loaded in the same Python program.
+
+The state of these libraries is also accessible through the object oriented API:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_info
+>>> from pprint import pprint
+>>> import numpy
+>>> controller = ThreadpoolController()
+>>> pprint(controller.info())
+[{'architecture': 'Haswell',
+  'filepath': '/home/jeremie/miniconda/envs/dev/lib/libopenblasp-r0.3.17.so',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'threading_layer': 'pthreads',
+  'user_api': 'blas',
+  'version': '0.3.17'}]
+
+>>> controller.info() == threadpool_info()
+True
+```
+
+### Setting the Maximum Size of Thread-Pools
+
+Control the number of threads used by the underlying runtime libraries
+in specific sections of your Python program:
+
+```python
+>>> from threadpoolctl import threadpool_limits
+>>> import numpy as np
+
+>>> with threadpool_limits(limits=1, user_api='blas'):
+...     # In this block, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread. They can thus be used jointly
+...     # with thread-parallelism.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+The threadpools can also be controlled via the object oriented API, which is especially
+useful to avoid searching through all the loaded shared libraries each time. It will
+however not act on libraries loaded after the instantiation of the
+`ThreadpoolController`:
+
+```python
+>>> from threadpoolctl import ThreadpoolController
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> with controller.limit(limits=1, user_api='blas'):
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+### Restricting the limits to the scope of a function
+
+`threadpool_limits` and `ThreadpoolController` can also be used as decorators to set
+the maximum number of threads used by the supported libraries at a function level. The
+decorators are accessible through their `wrap` method:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_limits
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> @controller.wrap(limits=1, user_api='blas')
+... # or @threadpool_limits.wrap(limits=1, user_api='blas')
+... def my_func():
+...     # Inside this function, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+...
+```
+
+### Sequential BLAS within OpenMP parallel region
+
+When one wants to have sequential BLAS calls within an OpenMP parallel region, it's
+safer to set `limits="sequential_blas_under_openmp"` since setting `limits=1` and `user_api="blas"` might not lead to the expected behavior in some configurations
+(e.g. OpenBLAS with the OpenMP threading layer
+https://github.com/xianyi/OpenBLAS/issues/2985).
+
+### Known Limitations
+
+- `threadpool_limits` can fail to limit the number of inner threads when nesting
+  parallel loops managed by distinct OpenMP runtime implementations (for instance
+  libgomp from GCC and libomp from clang/llvm or libiomp from ICC).
+
+  See the `test_openmp_nesting` function in [tests/test_threadpoolctl.py](
+  https://github.com/joblib/threadpoolctl/blob/master/tests/test_threadpoolctl.py)
+  for an example. More information can be found at:
+  https://github.com/jeremiedbb/Nested_OpenMP
+
+  Note however that this problem does not happen when `threadpool_limits` is
+  used to limit the number of threads used internally by BLAS calls that are
+  themselves nested under OpenMP parallel loops. `threadpool_limits` works as
+  expected, even if the inner BLAS implementation relies on a distinct OpenMP
+  implementation.
+
+- Using Intel OpenMP (ICC) and LLVM OpenMP (clang) in the same Python program
+  under Linux is known to cause problems. See the following guide for more details
+  and workarounds:
+  https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md
+
+- Setting the maximum number of threads of the OpenMP and BLAS libraries has a global
+  effect and impacts the whole Python process. There is no thread level isolation as
+  these libraries do not offer thread-local APIs to configure the number of threads to
+  use in nested parallel calls.
+
+
+## Maintainers
+
+To make a release:
+
+Bump the version number (`__version__`) in `threadpoolctl.py`.
+
+Build the distribution archives:
+
+```bash
+pip install flit
+flit build
+```
+
+Check the contents of `dist/`.
+
+If everything is fine, make a commit for the release, tag it, push the
+tag to github and then:
+
+```bash
+flit publish
+```
+
+### Credits
+
+The initial dynamic library introspection code was written by @anton-malakhov
+for the smp package available at https://github.com/IntelPython/smp .
+
+threadpoolctl extends this for other operating systems. Contrary to smp,
+threadpoolctl does not attempt to limit the size of Python multiprocessing
+pools (threads or processes) or set operating system-level CPU affinity
+constraints: threadpoolctl only interacts with native libraries via their
+public runtime APIs.
+
+
+%package help
+Summary:	Development documents and examples for threadpoolctl
+Provides:	python3-threadpoolctl-doc
+%description help
+# Thread-pool Controls [![Build Status](https://dev.azure.com/joblib/threadpoolctl/_apis/build/status/joblib.threadpoolctl?branchName=master)](https://dev.azure.com/joblib/threadpoolctl/_build/latest?definitionId=1&branchName=master) [![codecov](https://codecov.io/gh/joblib/threadpoolctl/branch/master/graph/badge.svg)](https://codecov.io/gh/joblib/threadpoolctl)
+
+Python helpers to limit the number of threads used in the
+threadpool-backed of common native libraries used for scientific
+computing and data science (e.g. BLAS and OpenMP).
+
+Fine control of the underlying thread-pool size can be useful in
+workloads that involve nested parallelism so as to mitigate
+oversubscription issues.
+
+## Installation
+
+- For users, install the last published version from PyPI:
+
+  ```bash
+  pip install threadpoolctl
+  ```
+
+- For contributors, install from the source repository in developer
+  mode:
+
+  ```bash
+  pip install -r dev-requirements.txt
+  flit install --symlink
+  ```
+
+  then you run the tests with pytest:
+
+  ```bash
+  pytest
+  ```
+
+## Usage
+
+### Command Line Interface
+
+Get a JSON description of thread-pools initialized when importing python
+packages such as numpy or scipy for instance:
+
+```
+python -m threadpoolctl -i numpy scipy.linalg
+[
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so",
+    "prefix": "libmkl_rt",
+    "user_api": "blas",
+    "internal_api": "mkl",
+    "version": "2019.0.4",
+    "num_threads": 2,
+    "threading_layer": "intel"
+  },
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so",
+    "prefix": "libiomp",
+    "user_api": "openmp",
+    "internal_api": "openmp",
+    "version": null,
+    "num_threads": 4
+  }
+]
+```
+
+The JSON information is written on STDOUT. If some of the packages are missing,
+a warning message is displayed on STDERR.
+
+### Python Runtime Programmatic Introspection
+
+Introspect the current state of the threadpool-enabled runtime libraries
+that are loaded when importing Python packages:
+
+```python
+>>> from threadpoolctl import threadpool_info
+>>> from pprint import pprint
+>>> pprint(threadpool_info())
+[]
+
+>>> import numpy
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None}]
+
+>>> import xgboost
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libgomp.so.1.0.0',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libgomp',
+  'user_api': 'openmp',
+  'version': None}]
+```
+
+In the above example, `numpy` was installed from the default anaconda channel and comes
+with MKL and its Intel OpenMP (`libiomp5`) implementation while `xgboost` was installed
+from pypi.org and links against GNU OpenMP (`libgomp`) so both OpenMP runtimes are
+loaded in the same Python program.
+
+The state of these libraries is also accessible through the object oriented API:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_info
+>>> from pprint import pprint
+>>> import numpy
+>>> controller = ThreadpoolController()
+>>> pprint(controller.info())
+[{'architecture': 'Haswell',
+  'filepath': '/home/jeremie/miniconda/envs/dev/lib/libopenblasp-r0.3.17.so',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'threading_layer': 'pthreads',
+  'user_api': 'blas',
+  'version': '0.3.17'}]
+
+>>> controller.info() == threadpool_info()
+True
+```
+
+### Setting the Maximum Size of Thread-Pools
+
+Control the number of threads used by the underlying runtime libraries
+in specific sections of your Python program:
+
+```python
+>>> from threadpoolctl import threadpool_limits
+>>> import numpy as np
+
+>>> with threadpool_limits(limits=1, user_api='blas'):
+...     # In this block, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread. They can thus be used jointly
+...     # with thread-parallelism.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+The threadpools can also be controlled via the object oriented API, which is especially
+useful to avoid searching through all the loaded shared libraries each time. It will
+however not act on libraries loaded after the instantiation of the
+`ThreadpoolController`:
+
+```python
+>>> from threadpoolctl import ThreadpoolController
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> with controller.limit(limits=1, user_api='blas'):
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+### Restricting the limits to the scope of a function
+
+`threadpool_limits` and `ThreadpoolController` can also be used as decorators to set
+the maximum number of threads used by the supported libraries at a function level. The
+decorators are accessible through their `wrap` method:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_limits
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> @controller.wrap(limits=1, user_api='blas')
+... # or @threadpool_limits.wrap(limits=1, user_api='blas')
+... def my_func():
+...     # Inside this function, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+...
+```
+
+### Sequential BLAS within OpenMP parallel region
+
+When one wants to have sequential BLAS calls within an OpenMP parallel region, it's
+safer to set `limits="sequential_blas_under_openmp"` since setting `limits=1` and `user_api="blas"` might not lead to the expected behavior in some configurations
+(e.g. OpenBLAS with the OpenMP threading layer
+https://github.com/xianyi/OpenBLAS/issues/2985).
+
+### Known Limitations
+
+- `threadpool_limits` can fail to limit the number of inner threads when nesting
+  parallel loops managed by distinct OpenMP runtime implementations (for instance
+  libgomp from GCC and libomp from clang/llvm or libiomp from ICC).
+
+  See the `test_openmp_nesting` function in [tests/test_threadpoolctl.py](
+  https://github.com/joblib/threadpoolctl/blob/master/tests/test_threadpoolctl.py)
+  for an example. More information can be found at:
+  https://github.com/jeremiedbb/Nested_OpenMP
+
+  Note however that this problem does not happen when `threadpool_limits` is
+  used to limit the number of threads used internally by BLAS calls that are
+  themselves nested under OpenMP parallel loops. `threadpool_limits` works as
+  expected, even if the inner BLAS implementation relies on a distinct OpenMP
+  implementation.
+
+- Using Intel OpenMP (ICC) and LLVM OpenMP (clang) in the same Python program
+  under Linux is known to cause problems. See the following guide for more details
+  and workarounds:
+  https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md
+
+- Setting the maximum number of threads of the OpenMP and BLAS libraries has a global
+  effect and impacts the whole Python process. There is no thread level isolation as
+  these libraries do not offer thread-local APIs to configure the number of threads to
+  use in nested parallel calls.
+
+
+## Maintainers
+
+To make a release:
+
+Bump the version number (`__version__`) in `threadpoolctl.py`.
+
+Build the distribution archives:
+
+```bash
+pip install flit
+flit build
+```
+
+Check the contents of `dist/`.
+
+If everything is fine, make a commit for the release, tag it, push the
+tag to github and then:
+
+```bash
+flit publish
+```
+
+### Credits
+
+The initial dynamic library introspection code was written by @anton-malakhov
+for the smp package available at https://github.com/IntelPython/smp .
+
+threadpoolctl extends this for other operating systems. Contrary to smp,
+threadpoolctl does not attempt to limit the size of Python multiprocessing
+pools (threads or processes) or set operating system-level CPU affinity
+constraints: threadpoolctl only interacts with native libraries via their
+public runtime APIs.
+
+
+%prep
+%autosetup -n threadpoolctl-3.1.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-threadpoolctl -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.1.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..06e1725
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e278b89038d9c9b39e7afafb8f5f87a3  threadpoolctl-3.1.0.tar.gz