Changelog
### 1.2.0
```
-------------
- Fix a security issue where ``eval(pre_dispatch)`` could potentially run
arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327
- Make sure that joblib works even when multiprocessing is not available,
for instance with Pyodide
https://github.com/joblib/joblib/pull/1256
- Avoid unnecessary warnings when workers and main process delete
the temporary memmap folder contents concurrently.
https://github.com/joblib/joblib/pull/1263
- Vendor loky 3.1.0 with several fixes to more robustly forcibly terminate
worker processes in case of a crash.
https://github.com/joblib/joblib/pull/1269
- Fix memory alignment bug for pickles containing numpy arrays.
This is especially important when loading the pickle with
``mmap_mode != None`` as the resulting ``numpy.memmap`` object
would not be able to correct the misalignment without performing
a memory copy.
This bug would cause invalid computation and segmentation faults
with native code that would directly access the underlying data
buffer of a numpy array, for instance C/C++/Cython code compiled
with older GCC versions or some old OpenBLAS written in platform
specific assembly.
https://github.com/joblib/joblib/pull/1254
- Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.
- Vendor loky 3.3.0 which fixes a bug with leaking processes in case of
nested loky parallel calls and more reliability spawn the correct
number of reusable workers.
```
### 1.1.0
```
--------------
- Fix byte order inconsistency issue during deserialization using joblib.load
in cross-endian environment: the numpy arrays are now always loaded to
use the system byte order, independently of the byte order of the system
that serialized the pickle.
https://github.com/joblib/joblib/pull/1181
- Fix joblib.Memory bug with the ``ignore`` parameter when the cached function
is a decorated function.
https://github.com/joblib/joblib/pull/1165
- Fix `joblib.Memory` to properly handle caching for functions defined
interactively in a IPython session or in Jupyter notebook cell.
https://github.com/joblib/joblib/pull/1214
- Update vendored loky (from version 2.9 to 3.0) and cloudpickle (from
version 1.6 to 2.0)
https://github.com/joblib/joblib/pull/1218
```
Links
- PyPI: https://pypi.org/project/joblib
- Changelog: https://pyup.io/changelogs/joblib/
- Docs: https://joblib.readthedocs.io
pyup-bot
commented
1 year ago
This PR updates joblib from 1.0.1 to 1.2.0.
Changelog
### 1.2.0 ``` ------------- - Fix a security issue where ``eval(pre_dispatch)`` could potentially run arbitrary code. Now only basic numerics are supported. https://github.com/joblib/joblib/pull/1327 - Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide https://github.com/joblib/joblib/pull/1256 - Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. https://github.com/joblib/joblib/pull/1263 - Vendor loky 3.1.0 with several fixes to more robustly forcibly terminate worker processes in case of a crash. https://github.com/joblib/joblib/pull/1269 - Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with ``mmap_mode != None`` as the resulting ``numpy.memmap`` object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. https://github.com/joblib/joblib/pull/1254 - Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+. - Vendor loky 3.3.0 which fixes a bug with leaking processes in case of nested loky parallel calls and more reliability spawn the correct number of reusable workers. ``` ### 1.1.0 ``` -------------- - Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. https://github.com/joblib/joblib/pull/1181 - Fix joblib.Memory bug with the ``ignore`` parameter when the cached function is a decorated function. https://github.com/joblib/joblib/pull/1165 - Fix `joblib.Memory` to properly handle caching for functions defined interactively in a IPython session or in Jupyter notebook cell. https://github.com/joblib/joblib/pull/1214 - Update vendored loky (from version 2.9 to 3.0) and cloudpickle (from version 1.6 to 2.0) https://github.com/joblib/joblib/pull/1218 ```Links
- PyPI: https://pypi.org/project/joblib - Changelog: https://pyup.io/changelogs/joblib/ - Docs: https://joblib.readthedocs.io