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
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
|
%global _empty_manifest_terminate_build 0
Name: python-snorkel
Version: 0.9.9
Release: 1
Summary: A system for quickly generating training data with weak supervision
License: Apache License 2.0
URL: https://snorkel.org
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/01/7f/b2bf1995d1bc564392530049cb084152da6301c21179bfbd97a7db8e77d5/snorkel-0.9.9.tar.gz
BuildArch: noarch
Requires: python3-munkres
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-pandas
Requires: python3-tqdm
Requires: python3-scikit-learn
Requires: python3-torch
Requires: python3-tensorboard
Requires: python3-networkx
%description
<img src="figs/logo_01.png" width="150"/>
[](https://circleci.com/gh/snorkel-team/snorkel/tree/master)
[](https://snorkel.readthedocs.io/en/master)
[](https://codecov.io/gh/snorkel-team/snorkel/branch/master)
[](https://opensource.org/licenses/Apache-2.0)
[](https://spectrum.chat/snorkel)
***Programmatically Build and Manage Training Data***
## Announcement
**The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI application development platform based on the core ideas behind Snorkel—you can check it out [here](https://snorkel.ai) or [join us](www.snorkel.ai/careers) in building it!**
The [Snorkel project](https://snorkel.ai/how-to-use-snorkel-to-build-ai-applications/) started at Stanford in 2015 with a simple technical bet: that it would increasingly be the **training data**, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to **programmatically label, build, and manage** training data.
To say that the Snorkel project succeeded and expanded beyond what we had ever expected would be an understatement. The basic goals of a research repo like Snorkel are to provide a minimum viable framework for testing and validating hypotheses. Four years later, we’ve been fortunate to do not just this, but to develop and deploy early versions of Snorkel in partnership with some of the world’s leading organizations like [Google](https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html), [Intel](https://dl.acm.org/doi/abs/10.1145/3329486.3329492), [Stanford Medicine](https://www.cell.com/patterns/fulltext/S2666-3899(20)30019-2), and many more; author over [thirty-six peer-reviewed publications](https://snorkel.ai/technology.html) on our findings around Snorkel and related innovations in weak supervision modeling, data augmentation, multi-task learning, and more; be included in courses at top-tier universities; support production deployments in systems that you’ve likely used in the last few hours; and work with an amazing community of researchers and practitioners from industry, medicine, government, academia, and beyond.
However, we realized increasingly–from conversations with users in weekly office hours, workshops, online discussions, and industry partners–that the Snorkel project was just the very first step. The ideas behind Snorkel change not just how you label training data, but so much of the entire lifecycle and pipeline of building, deploying, and managing ML: how users inject their knowledge; how models are constructed, trained, inspected, versioned, and monitored; how entire pipelines are developed iteratively; and how the full set of stakeholders in any ML deployment, from subject matter experts to ML engineers, are incorporated into the process.
Over the last year, we have been building the platform to support this broader vision: [Snorkel Flow](https://snorkel.ai/snorkel-flow-platform/), an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring, monitoring and analysis, and more, all of which integrate in a way that is greater than the sum of its parts–and that we believe makes ML truly faster, more flexible, and more practical than ever before.
Moving forward, we will be focusing our efforts on Snorkel Flow. We are extremely grateful for all of you that have contributed to the Snorkel project, and are excited for you to check out our next chapter [here](https://snorkel.ai).
# Quick Links
* [Snorkel website](https://snorkel.org)
* [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials)
* [Snorkel documentation](https://snorkel.readthedocs.io/)
* [Snorkel community forum](https://spectrum.chat/snorkel)
* [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml)
* [Snorkel Twitter](https://twitter.com/SnorkelAI)
# Getting Started
The quickest way to familiarize yourself with the Snorkel library is to walk through the [Get Started](https://snorkel.org/get-started/) page on the Snorkel website, followed by the full-length tutorials in the [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials) repository.
These tutorials demonstrate a variety of tasks, domains, labeling techniques, and integrations that can serve as templates as you apply Snorkel to your own applications.
# Installation
Snorkel requires Python 3.6 or later. To install Snorkel, we recommend using `pip`:
```bash
pip install snorkel
```
or `conda`:
```bash
conda install snorkel -c conda-forge
```
For information on installing from source and contributing to Snorkel, see our
[contributing guidelines](./CONTRIBUTING.md).
<details><summary><b>Details on installing with <tt>conda</tt></b></summary>
<p>
The following example commands give some more color on installing with `conda`.
These commands assume that your `conda` installation is Python 3.6,
and that you want to use a virtual environment called `snorkel-env`.
```bash
# [OPTIONAL] Activate a virtual environment called "snorkel"
conda create --yes -n snorkel-env python=3.6
conda activate snorkel-env
# We specify PyTorch here to ensure compatibility, but it may not be necessary.
conda install pytorch==1.1.0 -c pytorch
conda install snorkel==0.9.0 -c conda-forge
```
</p>
</details>
<details><summary><b>A quick note for Windows users</b></summary>
<p>
If you're using Windows, we highly recommend using Docker
(you can find an example in our
[tutorials repo](https://github.com/snorkel-team/snorkel-tutorials/blob/master/Dockerfile))
or the [Linux subsystem](https://docs.microsoft.com/en-us/windows/wsl/faq).
We've done limited testing on Windows, so if you want to contribute instructions
or improvements, feel free to open a PR!
</p>
</details>
# Discussion
## Issues
We use [GitHub Issues](https://github.com/snorkel-team/snorkel/issues) for posting bugs and feature requests — anything code-related.
Just make sure you search for related issues first and use our Issues templates.
We may ask for contributions if a prompt fix doesn't fit into the immediate roadmap of the core development team.
## Contributions
We welcome contributions from the Snorkel community!
This is likely the fastest way to get a change you'd like to see into the library.
Small contributions can be made directly in a pull request (PR).
If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion.
For ideas about what to work on, we've labeled specific issues as [`help wanted`](https://github.com/snorkel-team/snorkel/issues?utf8=%E2%9C%93&q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22+).
To set up a development environment for contributing back to Snorkel, see our [contributing guidelines](./CONTRIBUTING.md).
All PRs must pass the continuous integration tests and receive approval from a member of the Snorkel development team before they will be merged.
## Community Forum
For broader Q&A, discussions about using Snorkel, tutorial requests, etc., use the [Snorkel community forum](https://spectrum.chat/snorkel) hosted on Spectrum.
We hope this will be a venue for you to interact with other Snorkel users — please don't be shy about posting!
## Announcements
To stay up-to-date on Snorkel-related announcements (e.g. version releases, upcoming workshops), subscribe to the [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml). We promise to respect your inboxes — communication will be sparse!
## Twitter
Follow us on Twitter [@SnorkelAI](https://twitter.com/SnorkelAI).
%package -n python3-snorkel
Summary: A system for quickly generating training data with weak supervision
Provides: python-snorkel
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-snorkel
<img src="figs/logo_01.png" width="150"/>
[](https://circleci.com/gh/snorkel-team/snorkel/tree/master)
[](https://snorkel.readthedocs.io/en/master)
[](https://codecov.io/gh/snorkel-team/snorkel/branch/master)
[](https://opensource.org/licenses/Apache-2.0)
[](https://spectrum.chat/snorkel)
***Programmatically Build and Manage Training Data***
## Announcement
**The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI application development platform based on the core ideas behind Snorkel—you can check it out [here](https://snorkel.ai) or [join us](www.snorkel.ai/careers) in building it!**
The [Snorkel project](https://snorkel.ai/how-to-use-snorkel-to-build-ai-applications/) started at Stanford in 2015 with a simple technical bet: that it would increasingly be the **training data**, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to **programmatically label, build, and manage** training data.
To say that the Snorkel project succeeded and expanded beyond what we had ever expected would be an understatement. The basic goals of a research repo like Snorkel are to provide a minimum viable framework for testing and validating hypotheses. Four years later, we’ve been fortunate to do not just this, but to develop and deploy early versions of Snorkel in partnership with some of the world’s leading organizations like [Google](https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html), [Intel](https://dl.acm.org/doi/abs/10.1145/3329486.3329492), [Stanford Medicine](https://www.cell.com/patterns/fulltext/S2666-3899(20)30019-2), and many more; author over [thirty-six peer-reviewed publications](https://snorkel.ai/technology.html) on our findings around Snorkel and related innovations in weak supervision modeling, data augmentation, multi-task learning, and more; be included in courses at top-tier universities; support production deployments in systems that you’ve likely used in the last few hours; and work with an amazing community of researchers and practitioners from industry, medicine, government, academia, and beyond.
However, we realized increasingly–from conversations with users in weekly office hours, workshops, online discussions, and industry partners–that the Snorkel project was just the very first step. The ideas behind Snorkel change not just how you label training data, but so much of the entire lifecycle and pipeline of building, deploying, and managing ML: how users inject their knowledge; how models are constructed, trained, inspected, versioned, and monitored; how entire pipelines are developed iteratively; and how the full set of stakeholders in any ML deployment, from subject matter experts to ML engineers, are incorporated into the process.
Over the last year, we have been building the platform to support this broader vision: [Snorkel Flow](https://snorkel.ai/snorkel-flow-platform/), an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring, monitoring and analysis, and more, all of which integrate in a way that is greater than the sum of its parts–and that we believe makes ML truly faster, more flexible, and more practical than ever before.
Moving forward, we will be focusing our efforts on Snorkel Flow. We are extremely grateful for all of you that have contributed to the Snorkel project, and are excited for you to check out our next chapter [here](https://snorkel.ai).
# Quick Links
* [Snorkel website](https://snorkel.org)
* [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials)
* [Snorkel documentation](https://snorkel.readthedocs.io/)
* [Snorkel community forum](https://spectrum.chat/snorkel)
* [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml)
* [Snorkel Twitter](https://twitter.com/SnorkelAI)
# Getting Started
The quickest way to familiarize yourself with the Snorkel library is to walk through the [Get Started](https://snorkel.org/get-started/) page on the Snorkel website, followed by the full-length tutorials in the [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials) repository.
These tutorials demonstrate a variety of tasks, domains, labeling techniques, and integrations that can serve as templates as you apply Snorkel to your own applications.
# Installation
Snorkel requires Python 3.6 or later. To install Snorkel, we recommend using `pip`:
```bash
pip install snorkel
```
or `conda`:
```bash
conda install snorkel -c conda-forge
```
For information on installing from source and contributing to Snorkel, see our
[contributing guidelines](./CONTRIBUTING.md).
<details><summary><b>Details on installing with <tt>conda</tt></b></summary>
<p>
The following example commands give some more color on installing with `conda`.
These commands assume that your `conda` installation is Python 3.6,
and that you want to use a virtual environment called `snorkel-env`.
```bash
# [OPTIONAL] Activate a virtual environment called "snorkel"
conda create --yes -n snorkel-env python=3.6
conda activate snorkel-env
# We specify PyTorch here to ensure compatibility, but it may not be necessary.
conda install pytorch==1.1.0 -c pytorch
conda install snorkel==0.9.0 -c conda-forge
```
</p>
</details>
<details><summary><b>A quick note for Windows users</b></summary>
<p>
If you're using Windows, we highly recommend using Docker
(you can find an example in our
[tutorials repo](https://github.com/snorkel-team/snorkel-tutorials/blob/master/Dockerfile))
or the [Linux subsystem](https://docs.microsoft.com/en-us/windows/wsl/faq).
We've done limited testing on Windows, so if you want to contribute instructions
or improvements, feel free to open a PR!
</p>
</details>
# Discussion
## Issues
We use [GitHub Issues](https://github.com/snorkel-team/snorkel/issues) for posting bugs and feature requests — anything code-related.
Just make sure you search for related issues first and use our Issues templates.
We may ask for contributions if a prompt fix doesn't fit into the immediate roadmap of the core development team.
## Contributions
We welcome contributions from the Snorkel community!
This is likely the fastest way to get a change you'd like to see into the library.
Small contributions can be made directly in a pull request (PR).
If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion.
For ideas about what to work on, we've labeled specific issues as [`help wanted`](https://github.com/snorkel-team/snorkel/issues?utf8=%E2%9C%93&q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22+).
To set up a development environment for contributing back to Snorkel, see our [contributing guidelines](./CONTRIBUTING.md).
All PRs must pass the continuous integration tests and receive approval from a member of the Snorkel development team before they will be merged.
## Community Forum
For broader Q&A, discussions about using Snorkel, tutorial requests, etc., use the [Snorkel community forum](https://spectrum.chat/snorkel) hosted on Spectrum.
We hope this will be a venue for you to interact with other Snorkel users — please don't be shy about posting!
## Announcements
To stay up-to-date on Snorkel-related announcements (e.g. version releases, upcoming workshops), subscribe to the [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml). We promise to respect your inboxes — communication will be sparse!
## Twitter
Follow us on Twitter [@SnorkelAI](https://twitter.com/SnorkelAI).
%package help
Summary: Development documents and examples for snorkel
Provides: python3-snorkel-doc
%description help
<img src="figs/logo_01.png" width="150"/>
[](https://circleci.com/gh/snorkel-team/snorkel/tree/master)
[](https://snorkel.readthedocs.io/en/master)
[](https://codecov.io/gh/snorkel-team/snorkel/branch/master)
[](https://opensource.org/licenses/Apache-2.0)
[](https://spectrum.chat/snorkel)
***Programmatically Build and Manage Training Data***
## Announcement
**The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI application development platform based on the core ideas behind Snorkel—you can check it out [here](https://snorkel.ai) or [join us](www.snorkel.ai/careers) in building it!**
The [Snorkel project](https://snorkel.ai/how-to-use-snorkel-to-build-ai-applications/) started at Stanford in 2015 with a simple technical bet: that it would increasingly be the **training data**, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to **programmatically label, build, and manage** training data.
To say that the Snorkel project succeeded and expanded beyond what we had ever expected would be an understatement. The basic goals of a research repo like Snorkel are to provide a minimum viable framework for testing and validating hypotheses. Four years later, we’ve been fortunate to do not just this, but to develop and deploy early versions of Snorkel in partnership with some of the world’s leading organizations like [Google](https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html), [Intel](https://dl.acm.org/doi/abs/10.1145/3329486.3329492), [Stanford Medicine](https://www.cell.com/patterns/fulltext/S2666-3899(20)30019-2), and many more; author over [thirty-six peer-reviewed publications](https://snorkel.ai/technology.html) on our findings around Snorkel and related innovations in weak supervision modeling, data augmentation, multi-task learning, and more; be included in courses at top-tier universities; support production deployments in systems that you’ve likely used in the last few hours; and work with an amazing community of researchers and practitioners from industry, medicine, government, academia, and beyond.
However, we realized increasingly–from conversations with users in weekly office hours, workshops, online discussions, and industry partners–that the Snorkel project was just the very first step. The ideas behind Snorkel change not just how you label training data, but so much of the entire lifecycle and pipeline of building, deploying, and managing ML: how users inject their knowledge; how models are constructed, trained, inspected, versioned, and monitored; how entire pipelines are developed iteratively; and how the full set of stakeholders in any ML deployment, from subject matter experts to ML engineers, are incorporated into the process.
Over the last year, we have been building the platform to support this broader vision: [Snorkel Flow](https://snorkel.ai/snorkel-flow-platform/), an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring, monitoring and analysis, and more, all of which integrate in a way that is greater than the sum of its parts–and that we believe makes ML truly faster, more flexible, and more practical than ever before.
Moving forward, we will be focusing our efforts on Snorkel Flow. We are extremely grateful for all of you that have contributed to the Snorkel project, and are excited for you to check out our next chapter [here](https://snorkel.ai).
# Quick Links
* [Snorkel website](https://snorkel.org)
* [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials)
* [Snorkel documentation](https://snorkel.readthedocs.io/)
* [Snorkel community forum](https://spectrum.chat/snorkel)
* [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml)
* [Snorkel Twitter](https://twitter.com/SnorkelAI)
# Getting Started
The quickest way to familiarize yourself with the Snorkel library is to walk through the [Get Started](https://snorkel.org/get-started/) page on the Snorkel website, followed by the full-length tutorials in the [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials) repository.
These tutorials demonstrate a variety of tasks, domains, labeling techniques, and integrations that can serve as templates as you apply Snorkel to your own applications.
# Installation
Snorkel requires Python 3.6 or later. To install Snorkel, we recommend using `pip`:
```bash
pip install snorkel
```
or `conda`:
```bash
conda install snorkel -c conda-forge
```
For information on installing from source and contributing to Snorkel, see our
[contributing guidelines](./CONTRIBUTING.md).
<details><summary><b>Details on installing with <tt>conda</tt></b></summary>
<p>
The following example commands give some more color on installing with `conda`.
These commands assume that your `conda` installation is Python 3.6,
and that you want to use a virtual environment called `snorkel-env`.
```bash
# [OPTIONAL] Activate a virtual environment called "snorkel"
conda create --yes -n snorkel-env python=3.6
conda activate snorkel-env
# We specify PyTorch here to ensure compatibility, but it may not be necessary.
conda install pytorch==1.1.0 -c pytorch
conda install snorkel==0.9.0 -c conda-forge
```
</p>
</details>
<details><summary><b>A quick note for Windows users</b></summary>
<p>
If you're using Windows, we highly recommend using Docker
(you can find an example in our
[tutorials repo](https://github.com/snorkel-team/snorkel-tutorials/blob/master/Dockerfile))
or the [Linux subsystem](https://docs.microsoft.com/en-us/windows/wsl/faq).
We've done limited testing on Windows, so if you want to contribute instructions
or improvements, feel free to open a PR!
</p>
</details>
# Discussion
## Issues
We use [GitHub Issues](https://github.com/snorkel-team/snorkel/issues) for posting bugs and feature requests — anything code-related.
Just make sure you search for related issues first and use our Issues templates.
We may ask for contributions if a prompt fix doesn't fit into the immediate roadmap of the core development team.
## Contributions
We welcome contributions from the Snorkel community!
This is likely the fastest way to get a change you'd like to see into the library.
Small contributions can be made directly in a pull request (PR).
If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion.
For ideas about what to work on, we've labeled specific issues as [`help wanted`](https://github.com/snorkel-team/snorkel/issues?utf8=%E2%9C%93&q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22+).
To set up a development environment for contributing back to Snorkel, see our [contributing guidelines](./CONTRIBUTING.md).
All PRs must pass the continuous integration tests and receive approval from a member of the Snorkel development team before they will be merged.
## Community Forum
For broader Q&A, discussions about using Snorkel, tutorial requests, etc., use the [Snorkel community forum](https://spectrum.chat/snorkel) hosted on Spectrum.
We hope this will be a venue for you to interact with other Snorkel users — please don't be shy about posting!
## Announcements
To stay up-to-date on Snorkel-related announcements (e.g. version releases, upcoming workshops), subscribe to the [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml). We promise to respect your inboxes — communication will be sparse!
## Twitter
Follow us on Twitter [@SnorkelAI](https://twitter.com/SnorkelAI).
%prep
%autosetup -n snorkel-0.9.9
%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-snorkel -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.9-1
- Package Spec generated
|
