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D6655.id.diff
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diff --git a/Makefile b/Makefile
new file mode 100644
--- /dev/null
+++ b/Makefile
@@ -0,0 +1 @@
+include ../Makefile.common
diff --git a/package.yml b/package.yml
new file mode 100644
--- /dev/null
+++ b/package.yml
@@ -0,0 +1,18 @@
+name : python-joblib
+version : 0.13.2
+release : 1
+source :
+ - https://github.com/joblib/joblib/archive/0.13.2.tar.gz : 1a45a4d8d3728245a941946e0741cbfb7822c5a91cf5fbad3b3efbe2b27050ae
+license : BSD-3-Clause
+component : programming.python
+summary : Computing with Python functions
+description: |
+ Joblib is a set of tools to provide lightweight pipelining in Python. In particular:
+ - transparent disk-caching of functions and lazy re-evaluation (memoize pattern)
+ - easy simple parallel computing
+
+ Joblib is optimized to be fast and robust on large data in particular and has specific optimizations for numpy arrays.
+build : |
+ %python3_setup
+install : |
+ %python3_install
\ No newline at end of file
diff --git a/pspec_x86_64.xml b/pspec_x86_64.xml
new file mode 100644
--- /dev/null
+++ b/pspec_x86_64.xml
@@ -0,0 +1,262 @@
+<PISI>
+ <Source>
+ <Name>python-joblib</Name>
+ <Packager>
+ <Name>Rabin Adhikari</Name>
+ <Email>rabin.adk1@gmail.com</Email>
+ </Packager>
+ <License>BSD-3-Clause</License>
+ <PartOf>programming.python</PartOf>
+ <Summary xml:lang="en">Computing with Python functions</Summary>
+ <Description xml:lang="en">Joblib is a set of tools to provide lightweight pipelining in Python. In particular:
+- transparent disk-caching of functions and lazy re-evaluation (memoize pattern)
+- easy simple parallel computing
+
+Joblib is optimized to be fast and robust on large data in particular and has specific optimizations for numpy arrays.
+</Description>
+ <Archive type="binary" sha1sum="79eb0752a961b8e0d15c77d298c97498fbc89c5a">https://getsol.us/sources/README.Solus</Archive>
+ </Source>
+ <Package>
+ <Name>python-joblib</Name>
+ <Summary xml:lang="en">Computing with Python functions</Summary>
+ <Description xml:lang="en">Joblib is a set of tools to provide lightweight pipelining in Python. In particular:
+- transparent disk-caching of functions and lazy re-evaluation (memoize pattern)
+- easy simple parallel computing
+
+Joblib is optimized to be fast and robust on large data in particular and has specific optimizations for numpy arrays.
+</Description>
+ <PartOf>programming.python</PartOf>
+ <Files>
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+ </Package>
+ <History>
+ <Update release="1">
+ <Date>2019-07-02</Date>
+ <Version>0.13.2</Version>
+ <Comment>Packaging update</Comment>
+ <Name>Rabin Adhikari</Name>
+ <Email>rabin.adk1@gmail.com</Email>
+ </Update>
+ </History>
+</PISI>
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