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
|
%global _empty_manifest_terminate_build 0
Name: python-gpuinfo
Version: 1.0.0a7
Release: 1
Summary: A quick access to nvidia gpu information
License: MIT License
URL: https://pypi.org/project/gpuinfo/
Source0: https://mirrors.aliyun.com/pypi/web/packages/03/94/0d99b8b788cd770cbd85f9324d874208c350f6c6d91596374a612ae558d3/gpuinfo-1.0.0a7.tar.gz
BuildArch: noarch
%description
# gpuinfo
I implement some functions that can help users to obtain nvidia gpu information.
To use gpuinfo, you need to be able to run 'ps' and 'nvidia-smi' in your terminal.
# Install with pip
```
pip install gpuinfo
```
I only tested on linux system with python3.
https://pypi.org/project/gpuinfo/
# Usage
```python
from gpuinfo import GPUInfo
```
GPUInfo has the following functions:
get_users(gpu_id)
return a dict. show every user and memory on a certain gpu
check_empty()
check_empty()
return a list containing all GPU ids that no process is using currently.
get_info()
pid_list,percent,memory,gpu_used=get_info()
return a dict and three lists. pid_list has pids as keys and gpu ids as values, showing which gpu the process is using
get_user(pid)
get_user(pid)
Input a pid number , return its creator by linux command ps
gpu_usage()
gpu_usage()
return two lists. The first list contains usage percent of every GPU. The second list contains the memory used of every GPU. The information is obtained by command 'nvidia-smi'
# Example
```python
from gpuinfo import GPUInfo
available_device=GPUInfo.check_empty()
#available_device就是一个含有所有没有任务的gpu编号的列表
percent,memory=GPUInfo.gpu_usage()
#获得所有gpu的使用百分比和显存占用量
min_percent=percent.index(min([percent[i] for i in available_device]))
#未被使用的gpu里percent最小的
min_memory=memory.index(min([memory[i] for i in available_device]))
#未被使用的gpu里显存占用量最少的
#如果你使用pytorch
torch.cuda.set_device(min_percent) 或者(min_memory)
```
%package -n python3-gpuinfo
Summary: A quick access to nvidia gpu information
Provides: python-gpuinfo
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-gpuinfo
# gpuinfo
I implement some functions that can help users to obtain nvidia gpu information.
To use gpuinfo, you need to be able to run 'ps' and 'nvidia-smi' in your terminal.
# Install with pip
```
pip install gpuinfo
```
I only tested on linux system with python3.
https://pypi.org/project/gpuinfo/
# Usage
```python
from gpuinfo import GPUInfo
```
GPUInfo has the following functions:
get_users(gpu_id)
return a dict. show every user and memory on a certain gpu
check_empty()
check_empty()
return a list containing all GPU ids that no process is using currently.
get_info()
pid_list,percent,memory,gpu_used=get_info()
return a dict and three lists. pid_list has pids as keys and gpu ids as values, showing which gpu the process is using
get_user(pid)
get_user(pid)
Input a pid number , return its creator by linux command ps
gpu_usage()
gpu_usage()
return two lists. The first list contains usage percent of every GPU. The second list contains the memory used of every GPU. The information is obtained by command 'nvidia-smi'
# Example
```python
from gpuinfo import GPUInfo
available_device=GPUInfo.check_empty()
#available_device就是一个含有所有没有任务的gpu编号的列表
percent,memory=GPUInfo.gpu_usage()
#获得所有gpu的使用百分比和显存占用量
min_percent=percent.index(min([percent[i] for i in available_device]))
#未被使用的gpu里percent最小的
min_memory=memory.index(min([memory[i] for i in available_device]))
#未被使用的gpu里显存占用量最少的
#如果你使用pytorch
torch.cuda.set_device(min_percent) 或者(min_memory)
```
%package help
Summary: Development documents and examples for gpuinfo
Provides: python3-gpuinfo-doc
%description help
# gpuinfo
I implement some functions that can help users to obtain nvidia gpu information.
To use gpuinfo, you need to be able to run 'ps' and 'nvidia-smi' in your terminal.
# Install with pip
```
pip install gpuinfo
```
I only tested on linux system with python3.
https://pypi.org/project/gpuinfo/
# Usage
```python
from gpuinfo import GPUInfo
```
GPUInfo has the following functions:
get_users(gpu_id)
return a dict. show every user and memory on a certain gpu
check_empty()
check_empty()
return a list containing all GPU ids that no process is using currently.
get_info()
pid_list,percent,memory,gpu_used=get_info()
return a dict and three lists. pid_list has pids as keys and gpu ids as values, showing which gpu the process is using
get_user(pid)
get_user(pid)
Input a pid number , return its creator by linux command ps
gpu_usage()
gpu_usage()
return two lists. The first list contains usage percent of every GPU. The second list contains the memory used of every GPU. The information is obtained by command 'nvidia-smi'
# Example
```python
from gpuinfo import GPUInfo
available_device=GPUInfo.check_empty()
#available_device就是一个含有所有没有任务的gpu编号的列表
percent,memory=GPUInfo.gpu_usage()
#获得所有gpu的使用百分比和显存占用量
min_percent=percent.index(min([percent[i] for i in available_device]))
#未被使用的gpu里percent最小的
min_memory=memory.index(min([memory[i] for i in available_device]))
#未被使用的gpu里显存占用量最少的
#如果你使用pytorch
torch.cuda.set_device(min_percent) 或者(min_memory)
```
%prep
%autosetup -n gpuinfo-1.0.0a7
%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-gpuinfo -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0a7-1
- Package Spec generated
|
