FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
docker-python/test_build.py at cuda · Seth-Rothschild/docker-python · GitHub
Seth-Rothschild
/
docker-python
Public
forked from
Kaggle/docker-python
Notifications
You must be signed in to change notification settings
Fork
0
Star
0
Code
Pull requests
0
Actions
Projects
Wiki
Security and quality
0
Insights
Additional navigation options
Code
Pull requests
Actions
Projects
Wiki
Security and quality
Insights
Expand file tree
Breadcrumbs
docker-python
/
test_build.py
Copy path
More file actions
More file actions
Latest commit
History
History
History
142 lines (106 loc) · 3.58 KB
Breadcrumbs
docker-python
/
test_build.py
Copy path
File metadata and controls
142 lines (106 loc) · 3.58 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
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
# This script should run without errors whenever we update the
# kaggle/python container. It checks that all our most popular packages can
# be loaded and used without errors.
import
numpy
as
np
print
(
"Numpy imported ok"
)
print
(
"Your lucky number is: "
+
str
(
np
.
random
.
randint
(
100
)))
# Numpy must be linked to the MKL. (Occasionally, a third-party package will muck up the installation
# and numpy will be reinstalled with an OpenBLAS backing.)
from
numpy
.
distutils
.
system_info
import
get_info
# This will throw an exception if the MKL is not linked correctly.
get_info
(
"blas_mkl"
)
import
pandas
as
pd
print
(
"Pandas imported ok"
)
from
sklearn
import
datasets
print
(
"sklearn imported ok"
)
iris
=
datasets
.
load_iris
()
X
,
y
=
iris
.
data
,
iris
.
target
from
sklearn
.
ensemble
import
RandomForestClassifier
rf1
=
RandomForestClassifier
()
rf1
.
fit
(
X
,
y
)
print
(
"sklearn RandomForestClassifier: ok"
)
from
sklearn
.
linear_model
import
LinearRegression
boston
=
datasets
.
load_boston
()
X
,
y
=
boston
.
data
,
boston
.
target
lr1
=
LinearRegression
()
lr1
.
fit
(
X
,
y
)
print
(
"sklearn LinearRegression: ok"
)
from
xgboost
import
XGBClassifier
xgb1
=
XGBClassifier
(
n_estimators
=
3
)
xgb1
.
fit
(
X
[
0
:
70
],
y
[
0
:
70
])
print
(
"xgboost XGBClassifier: ok"
)
import
matplotlib
.
pyplot
as
plt
plt
.
plot
(
np
.
linspace
(
0
,
1
,
50
),
np
.
random
.
rand
(
50
))
plt
.
savefig
(
"plot1.png"
)
print
(
"matplotlib.pyplot ok"
)
from
mpl_toolkits
.
basemap
import
Basemap
print
(
"Basemap ok"
)
import
plotly
.
plotly
as
py
import
plotly
.
graph_objs
as
go
print
(
"plotly ok"
)
from
ggplot
import
*
print
(
"ggplot ok"
)
import
theano
print
(
"Theano ok"
)
from
keras
.
models
import
Sequential
from
keras
.
layers
.
core
import
Dense
,
Dropout
,
Activation
,
Flatten
from
keras
.
layers
.
convolutional
import
Convolution2D
,
MaxPooling2D
from
keras
.
optimizers
import
SGD
print
(
"keras ok"
)
import
nltk
from
nltk
.
stem
import
WordNetLemmatizer
print
(
"nltk ok"
)
import
tensorflow
as
tf
with
tf
.
device
(
'/cpu:0'
):
hello
=
tf
.
constant
(
'TensorFlow ok'
)
sess
=
tf
.
Session
(
config
=
tf
.
ConfigProto
(
log_device_placement
=
True
))
print
(
sess
.
run
(
hello
).
decode
())
import
cv2
img
=
cv2
.
imread
(
'plot1.png'
,
0
)
print
(
"OpenCV ok"
)
from
skimage
.
io
import
imread
print
(
"skimage ok"
)
from
wordbatch
.
extractors
import
WordBag
print
(
"wordbatch ok"
)
import
pyfasttext
print
(
"pyfasttext ok"
)
import
fastText
print
(
"fastText ok"
)
import
mxnet
import
mxnet
.
gluon
print
(
"mxnet ok"
)
import
pycuda
print
(
"pycuda ok"
)
import
torch
# Note: torch.cuda.is_available() returns whether GPU support is present AND at least one GPU is available.
print
(
"torch ok (gpu available: %s, count: %d)"
%
(
torch
.
cuda
.
is_available
(),
torch
.
cuda
.
device_count
()))
# bigquery proxy
import
os
import
threading
from
http
.
server
import
BaseHTTPRequestHandler
,
HTTPServer
from
google
.
cloud
import
bigquery
HOSTNAME
=
"127.0.0.1"
PORT
=
8000
URL
=
"http://%s:%s"
%
(
HOSTNAME
,
PORT
)
fake_bq_called
=
False
fake_bq_header_found
=
False
class
HTTPHandler
(
BaseHTTPRequestHandler
):
def
do_HEAD
(
s
):
s
.
send_response
(
200
)
def
do_GET
(
s
):
global
fake_bq_called
global
fake_bq_header_found
fake_bq_called
=
True
fake_bq_header_found
=
any
(
k
for
k
in
s
.
headers
if
k
==
"X-KAGGLE-PROXY-DATA"
and
s
.
headers
[
k
]
==
"test-key"
)
s
.
send_response
(
200
)
httpd
=
HTTPServer
((
HOSTNAME
,
PORT
),
HTTPHandler
)
threading
.
Thread
(
target
=
httpd
.
serve_forever
).
start
()
client
=
bigquery
.
Client
()
try
:
for
ds
in
client
.
list_datasets
():
pass
except
:
pass
httpd
.
shutdown
()
assert
fake_bq_called
,
"Fake server did not recieve a request from the BQ client."
assert
fake_bq_header_found
,
"X-KAGGLE-PROXY-DATA header was missing from the BQ request."
print
(
"bigquery proxy ok"
)
Back
|
FazBrowse Home
|
New Git URL