joblib 0.10.3+git55-g660fe5d-1 source package in Ubuntu
Changelog
joblib (0.10.3+git55-g660fe5d-1) unstable; urgency=medium * New upstream snapshot from 0.10.2-54-gca8a159 (0.10.2 was released from a feature branch) - should address random build failures (Closes: #846228) * debian/{rules,control} - upstream switched to py.test from nose -- Yaroslav Halchenko <email address hidden> Wed, 07 Dec 2016 14:52:19 -0500
Upload details
- Uploaded by:
- Yaroslav Halchenko
- Uploaded to:
- Sid
- Original maintainer:
- Yaroslav Halchenko
- Architectures:
- all
- Section:
- python
- Urgency:
- Medium Urgency
See full publishing history Publishing
Series | Published | Component | Section |
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Downloads
File | Size | SHA-256 Checksum |
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joblib_0.10.3+git55-g660fe5d-1.dsc | 2.1 KiB | 32101cd853905b46e434cb4c215e01d4a4a8d4f2df9ad5ad30ea6cbb85bf23b5 |
joblib_0.10.3+git55-g660fe5d.orig.tar.gz | 164.8 KiB | 4bc460c55039f1bdd9db50d65a127b283246e4c91df9d4d9da4106b8fc936b7c |
joblib_0.10.3+git55-g660fe5d-1.debian.tar.xz | 5.3 KiB | 842c372df6ca77a979ccd4d4abbdcd4b8bc0f9034ffb8be960e1dcc4f9919cf3 |
Available diffs
No changes file available.
Binary packages built by this source
- python-joblib: tools to provide lightweight pipelining in Python
Joblib is a set of tools to provide lightweight pipelining in
Python. In particular, joblib offers:
.
- transparent disk-caching of the output values and lazy
re-evaluation (memoize pattern)
- easy simple parallel computing
- logging and tracing of the execution
.
Joblib is optimized to be fast and robust in particular on large,
long-running functions and has specific optimizations for numpy arrays.
.
This package contains the Python 2 version.
- python3-joblib: tools to provide lightweight pipelining in Python
Joblib is a set of tools to provide lightweight pipelining in
Python. In particular, joblib offers:
.
- transparent disk-caching of the output values and lazy
re-evaluation (memoize pattern)
- easy simple parallel computing
- logging and tracing of the execution
.
Joblib is optimized to be fast and robust in particular on large,
long-running functions and has specific optimizations for numpy arrays.
.
This package contains the Python 3 version.