docker-python/test_build.py at add-rl-libs · HadokenCode/docker-python · GitHub

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# 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 tensorflow as tf

print(tf.__version__)

hello = tf.constant('TensorFlow ok')

sess = tf.Session()

print(sess.run(hello))

print("Tensorflow 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")

# PyTorch smoke test based on http://pytorch.org/tutorials/beginner/nlp/deep_learning_tutorial.html

import torch

import torch.nn as tnn

import torch.autograd as autograd

torch.manual_seed(31337)

linear_torch = tnn.Linear(5,3)

data_torch = autograd.Variable(torch.randn(2, 5))

print(linear_torch(data_torch))

print("PyTorch ok")

import fastai

from fastai.io import get_data

print("fast.ai ok")

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")

import nltk

from nltk.stem import WordNetLemmatizer

print("nltk ok")

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 bokeh

print("bokeh ok")

import seaborn

print("seaborn ok")

# Test BigQuery

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."

docker-python/test_build.py at add-rl-libs · HadokenCode/docker-python · GitHub

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