1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
|
%global _empty_manifest_terminate_build 0
Name: python-prefetch-generator
Version: 1.0.3
Release: 1
Summary: a simple tool to compute arbitrary generator in a background thread
License: The Unlicense
URL: https://github.com/justheuristic/prefetch_generator
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/28/cf/f6c148289fd87832e9f20428b7e842921efac5313cb3ac5920a424543760/prefetch_generator-1.0.3.tar.gz
BuildArch: noarch
%description
Based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch
This is a single-function package that makes it possible to transform any generator into a `BackgroundGenerator` which computes any number of elements from the generator ahead, in a background thread.
It is quite lightweight, but not entirely weightless.
The `BackgroundGenerator` is most useful when you have a GIL releasing task which might take a long time to complete (e.g. Disk I/O, Web Requests, pure C functions, GPU processing, ...), and another task which takes a similar amount of time, but is dependent on the results of the first task (e.g. Computationally intensive processing of data loaded from disk).
Normally these two tasks will constantly wait for one another to finish. If you make one of these tasks a `BackgroundGenerator` (see examples below), they will work in parallel, potentially saving up to 50% of execution time (definitely less in practice).
We personally use the `BackgroundGenerator` when iterating over minibatches of data for deep learning with tensorflow and theano ( lasagne, blocks, raw, etc.).
Quick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb
This package contains two objects:
- The Class `BackgroundGenerator(generator [,max_prefetch=1])`
- The decorator `@prefetch([max_prefetch=1])`
the usage is either
#for item in BackgroundGenerator(my_generator):
# do_stuff(item)
or
#@prefetch()
#def my_generator(some_param):
# while True:
# X = read_heavy_file()
# y = wget_from_cornhub()
# do_pretty_much_anything(some_param)
# yield X, y
More details are written in the `BackgroundGenerator` doc:
See `help(BackgroundGenerator)`
%package -n python3-prefetch-generator
Summary: a simple tool to compute arbitrary generator in a background thread
Provides: python-prefetch-generator
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-prefetch-generator
Based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch
This is a single-function package that makes it possible to transform any generator into a `BackgroundGenerator` which computes any number of elements from the generator ahead, in a background thread.
It is quite lightweight, but not entirely weightless.
The `BackgroundGenerator` is most useful when you have a GIL releasing task which might take a long time to complete (e.g. Disk I/O, Web Requests, pure C functions, GPU processing, ...), and another task which takes a similar amount of time, but is dependent on the results of the first task (e.g. Computationally intensive processing of data loaded from disk).
Normally these two tasks will constantly wait for one another to finish. If you make one of these tasks a `BackgroundGenerator` (see examples below), they will work in parallel, potentially saving up to 50% of execution time (definitely less in practice).
We personally use the `BackgroundGenerator` when iterating over minibatches of data for deep learning with tensorflow and theano ( lasagne, blocks, raw, etc.).
Quick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb
This package contains two objects:
- The Class `BackgroundGenerator(generator [,max_prefetch=1])`
- The decorator `@prefetch([max_prefetch=1])`
the usage is either
#for item in BackgroundGenerator(my_generator):
# do_stuff(item)
or
#@prefetch()
#def my_generator(some_param):
# while True:
# X = read_heavy_file()
# y = wget_from_cornhub()
# do_pretty_much_anything(some_param)
# yield X, y
More details are written in the `BackgroundGenerator` doc:
See `help(BackgroundGenerator)`
%package help
Summary: Development documents and examples for prefetch-generator
Provides: python3-prefetch-generator-doc
%description help
Based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch
This is a single-function package that makes it possible to transform any generator into a `BackgroundGenerator` which computes any number of elements from the generator ahead, in a background thread.
It is quite lightweight, but not entirely weightless.
The `BackgroundGenerator` is most useful when you have a GIL releasing task which might take a long time to complete (e.g. Disk I/O, Web Requests, pure C functions, GPU processing, ...), and another task which takes a similar amount of time, but is dependent on the results of the first task (e.g. Computationally intensive processing of data loaded from disk).
Normally these two tasks will constantly wait for one another to finish. If you make one of these tasks a `BackgroundGenerator` (see examples below), they will work in parallel, potentially saving up to 50% of execution time (definitely less in practice).
We personally use the `BackgroundGenerator` when iterating over minibatches of data for deep learning with tensorflow and theano ( lasagne, blocks, raw, etc.).
Quick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb
This package contains two objects:
- The Class `BackgroundGenerator(generator [,max_prefetch=1])`
- The decorator `@prefetch([max_prefetch=1])`
the usage is either
#for item in BackgroundGenerator(my_generator):
# do_stuff(item)
or
#@prefetch()
#def my_generator(some_param):
# while True:
# X = read_heavy_file()
# y = wget_from_cornhub()
# do_pretty_much_anything(some_param)
# yield X, y
More details are written in the `BackgroundGenerator` doc:
See `help(BackgroundGenerator)`
%prep
%autosetup -n prefetch-generator-1.0.3
%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-prefetch-generator -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.3-1
- Package Spec generated
|
