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+Metadata-Version: 2.1
+Name: threadpoolctl
+Version: 3.5.0
+Summary: threadpoolctl
+Home-page: https://github.com/joblib/threadpoolctl
+License: BSD-3-Clause
+Author: Thomas Moreau
+Author-email: thomas.moreau.2010@gmail.com
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+
+# 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
+...
+```
+
+### Switching the FlexiBLAS backend
+
+`FlexiBLAS` is a BLAS wrapper for which the BLAS backend can be switched at runtime.
+`threadpoolctl` exposes python bindings for this feature. Here's an example but note
+that this part of the API is experimental and subject to change without deprecation:
+
+```python
+>>> from threadpoolctl import ThreadpoolController
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> controller.info()
+[{'user_api': 'blas',
+  'internal_api': 'flexiblas',
+  'num_threads': 1,
+  'prefix': 'libflexiblas',
+  'filepath': '/usr/local/lib/libflexiblas.so.3.3',
+  'version': '3.3.1',
+  'available_backends': ['NETLIB', 'OPENBLASPTHREAD', 'ATLAS'],
+  'loaded_backends': ['NETLIB'],
+  'current_backend': 'NETLIB'}]
+
+# Retrieve the flexiblas controller
+>>> flexiblas_ct = controller.select(internal_api="flexiblas").lib_controllers[0]
+
+# Switch the backend with one predefined at build time (listed in "available_backends")
+>>> flexiblas_ct.switch_backend("OPENBLASPTHREAD")
+>>> controller.info()
+[{'user_api': 'blas',
+  'internal_api': 'flexiblas',
+  'num_threads': 4,
+  'prefix': 'libflexiblas',
+  'filepath': '/usr/local/lib/libflexiblas.so.3.3',
+  'version': '3.3.1',
+  'available_backends': ['NETLIB', 'OPENBLASPTHREAD', 'ATLAS'],
+  'loaded_backends': ['NETLIB', 'OPENBLASPTHREAD'],
+  'current_backend': 'OPENBLASPTHREAD'},
+ {'user_api': 'blas',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'filepath': '/usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so',
+  'version': '0.3.8',
+  'threading_layer': 'pthreads',
+  'architecture': 'Haswell'}]
+
+# It's also possible to directly give the path to a shared library
+>>> flexiblas_controller.switch_backend("/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so")
+>>> controller.info()
+[{'user_api': 'blas',
+  'internal_api': 'flexiblas',
+  'num_threads': 2,
+  'prefix': 'libflexiblas',
+  'filepath': '/usr/local/lib/libflexiblas.so.3.3',
+  'version': '3.3.1',
+  'available_backends': ['NETLIB', 'OPENBLASPTHREAD', 'ATLAS'],
+  'loaded_backends': ['NETLIB',
+   'OPENBLASPTHREAD',
+   '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so'],
+  'current_backend': '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so'},
+ {'user_api': 'openmp',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libomp',
+  'filepath': '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libomp.so',
+  'version': None},
+ {'user_api': 'blas',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'filepath': '/usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so',
+  'version': '0.3.8',
+  'threading_layer': 'pthreads',
+  'architecture': 'Haswell'},
+ {'user_api': 'blas',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'filepath': '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so.2',
+  'version': '2024.0-Product',
+  'threading_layer': 'gnu'}]
+```
+
+You can observe that the previously linked OpenBLAS shared object stays loaded by
+the Python program indefinitely, but FlexiBLAS itself no longer delegates BLAS calls
+to OpenBLAS as indicated by the `current_backend` attribute.
+### Writing a custom library controller
+
+Currently, `threadpoolctl` has support for `OpenMP` and the main `BLAS` libraries.
+However it can also be used to control the threadpool of other native libraries,
+provided that they expose an API to get and set the limit on the number of threads.
+For that, one must implement a controller for this library and register it to
+`threadpoolctl`.
+
+A custom controller must be a subclass of the `LibController` class and implement
+the attributes and methods described in the docstring of `LibController`. Then this
+new controller class must be registered using the `threadpoolctl.register` function.
+An complete example can be found [here](
+  https://github.com/joblib/threadpoolctl/blob/master/tests/_pyMylib/__init__.py).
+
+### 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` and update the
+  release date in `CHANGES.md`.
+
+- Build the distribution archives:
+
+```bash
+pip install flit
+flit build
+```
+
+and check the contents of `dist/`.
+
+- If everything is fine, make a commit for the release, tag it and push the
+tag to github:
+
+```bash
+git tag -a X.Y.Z
+git push git@github.com:joblib/threadpoolctl.git X.Y.Z
+```
+
+- Upload the wheels and source distribution to PyPI using flit. Since PyPI doesn't
+  allow password authentication anymore, the username needs to be changed to the
+  generic name `__token__`:
+
+```bash
+FLIT_USERNAME=__token__ flit publish
+```
+
+  and a PyPI token has to be passed in place of the password.
+
+- Create a PR for the release on the [conda-forge feedstock](https://github.com/conda-forge/threadpoolctl-feedstock) (or wait for the bot to make it).
+
+- Publish the release on github.
+
+### 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.
+