oarriaga

from keras.layers.core import Layer
import keras.backend as K

if K.backend() == 'tensorflow':
    import tensorflow as tf
    def K_arange(start, stop=None, step=1, dtype='int32'):
        result = tf.range(start, limit=stop, delta=step, name='arange')
        if dtype != 'int32':
            result = K.cast(result, dtype)
        return result

jfeala

"""
AWS Batch wrapper for Luigi

From the AWS website:

    AWS Batch enables you to run batch computing workloads on the AWS Cloud. 

    Batch computing is a common way for developers, scientists, and engineers 
    to access large amounts of compute resources, and AWS Batch removes the 
    undifferentiated heavy lifting of configuring and managing the required 

shivaram

# Download Spark 1.4 from http://spark.apache.org/downloads.html
# 
# Download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv

# Launch SparkR using 
# ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3

# The SparkSQL context should already be created for you as sqlContext
sqlContext
# Java ref type org.apache.spark.sql.SQLContext id 1

ellisonbg

// Simple IPython Notebook test
// Requires PhantomJS and CasperJS.
// To run:
// 1) Start a notebook server in an empty directory.
// 2) casperjs test_basic.js

var casper = require('casper').create({
    // verbose: true,
    // logLevel: "debug"
});