BigDL | Fast, distributed, secure AI for Big Data
kandi X-RAY | BigDL Summary
kandi X-RAY | BigDL Summary
BigDL makes it easy for data scientists and data engineers to build end-to-end, distributed AI applications. The BigDL 2.0 release combines the original BigDL and Analytics Zoo projects, providing the following features:. For more information, you may read the docs.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of BigDL
BigDL Key Features
BigDL Examples and Code Snippets
error = pd.concat([error, pd.DataFrame({'Bal4': Bal4})], axis=1)
print(error)
Bal2 Bal3 Bal4
0 2.0 1.0 1
1 NaN 3.0 2
2 NaN NaN 3
3 NaN NaN 4
4 NaN NaN 5
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © vitruvius
//@version=5
strategy(title="GOLDEN", overlay=true)
in_start_time = input(defval=timestamp("01 Jan 2021 00:00 +
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © vitruvius
//@version=5
indicator("My script", overlay=true, max_lines_count=500)
line_cnt = input.int(5)
var line_arr = a
#["SELECT * FROM mulesoft
WHERE " ++ vars.SORT_KEY.FILTER_KEY ++ " = '" ++ vars.SORT_KEY.FILTER_VALS ++ "'"]
#["SELEC
Comparator numberValueComparator = Comparator.comparing(BigDecimal::new);
List groupedValue = partitions.stream()
.map(p -> String.format("%s High: %s, Low: %s",
String.join(", ", p),
Collections
In [94]: txt = """0 0 0 0.0
...: 1 0 0 0.0
...: 2 0 0 2.0
...: 0 1 0 0.0
...: 1 1 0 0.0
...: 2 1 0 2.0
...: 0 2 0 0.0
...: 1 2 0 0.0
...: 2 2 0 2.0
...: 0 0 1 0.0
...: 1 0 1 0.0
...: 2 0 1 2.0
import math
import numpy.random as rd
import scipy.special as sp
# convert 3 uniform [0,1) variates into 3 unit Gaussian variates:
def boxMuller3d(u3):
u0,u1,u2 = u3 # 3 uniform random numbers in [0,1)
gamma = u0
n
%dw 2.0
output application/json
var address = payload.address[0]
---
address ++ (payload - "address")
%dw 2.0
output application/json
---
read(payload, "applicatin/csv", {"header": false, "separator": "|" }) map (
{
id: $[0],
product: $[1],
price: $[2]
}
)
df .= Float64.(df)
transform!(df, All() .=> ByRow(Float64), renamecols=false)
mapcols!(ByRow(Float64), df)
julia> transform!(df, names(df, Int) .=> ByRow(F
Community Discussions
Trending Discussions on BigDL
QUESTION
I am trying to implement image classification using Intel Bigdl. It is using mnist dataset for classification. Since, I don't want to use the mnist dataset I wrote the alternative approach to it as below:
Image Utils.py
...ANSWER
Answered 2017-Jul-12 at 11:25The train_images is a rdd and you can't apply numpy mean on a rdd. one way is to do collect() and over that apply numpy mean,
QUESTION
In a Python 3.5 notebook, backed by an Apache Spark service, I had installed BigDL 0.2 using pip
. When removing that installation and trying to install version 0.3 of BigDL, I get this error: (linebreaks added for readability)
ANSWER
Answered 2017-Nov-09 at 10:31The directory paths in the error message are wrong. The Python 3.5 kernel on DSX specifies a build directory for pip
by setting the environment variable PIP_BUILD. The multiple dist-info directories are there:
QUESTION
I would like to use Intel BigDL in notebooks on Data Science Experience on Cloud.
How can I install it?
...ANSWER
Answered 2018-Apr-18 at 05:13If your notebooks are backed by an Apache Spark as a Service instance in DSX, installing BigDL is simple. But you have to collect some version information first.
- Which Spark version? Currently, 2.1 is the latest supported by DSX.
With Python, you can only install BigDL for one Spark version per service. - Which BigDL version? Currently, 0.3.0 is the latest, and it supports Spark 2.1.
If in doubt, check the download page. The Spark fixlevel does not matter.
With this information, you can determine the URL of the required BigDL JAR file in the Maven repository.
For the example versions, BigDL 0.3.0 with Spark 2.1, the download URL is
https://repo1.maven.org/maven2/com/intel/analytics/bigdl/bigdl-SPARK_2.1/0.3.0/bigdl-SPARK_2.1-0.3.0-jar-with-dependencies.jar
For other versions, replace 0.3.0 and 2.1 in that URL as required. Note that both versions appear twice, once in the path and once in the filename.
Installing for PythonYou need the JAR, and the matching Python package. The Python package depends only on the version of BigDL, not on the Spark version. The installation steps can be executed from a Python notebook:
Install the JAR.
QUESTION
When I run BigDL (https://bigdl-project.github.io/0.4.0/) Text Classifier example (https://github.com/intel-analytics/BigDL/tree/master/pyspark/bigdl/models/textclassifier) with single node PySpark I get the following error. Any ideas how to solve this?
Configuration:
Java:
...ANSWER
Answered 2018-Feb-23 at 06:01The python script is trying to create a bigdl.log
file in the /usr/local/lib/python3.5/dist-packages/bigdl/bigdl.log
which is a protected directory in linux accessible through root-access only.
You can specify a log file path to redire_spark_logs
function something like this redire_spark_logs(log_path='/home/bigdl-projects')
. Look here for more details.
QUESTION
I'm beginner in spark and scala programing, I tried running example with spark-submit in local mode, it's run complete without any error or other message but i can't see any output result in consul or spark history web UI .Where and how can I see the results of my program in spark-submit?
This is a command that I run on spark
...ANSWER
Answered 2018-Apr-26 at 10:50Try to add this while(true) Thread.sleep(1000)
in your code, to keep the server running then check the sparks task in the browser. Normally you should see your application running.
QUESTION
While checking Intel's BigDL repo, I stumbled upon this method:
...ANSWER
Answered 2017-Mar-22 at 20:53If you generalize the idea and think of it as a monad (polymorphic thing working for arbitrary type params) then you won't be able to implement a tail recursive implementation.
Trampolines try to solve this very problem by providing a way to evaluate a recursive computation without overflowing the stack. The general idea is to create a stream of pairs of (result, computation). So at each step you'll have to return the computed result up to that point and a function to create the next result (aka thunk
).
From Rich Dougherty’s blog:
More + ReferencesA trampoline is a loop that repeatedly runs functions. Each function, called a thunk, returns the next function for the loop to run. The trampoline never runs more than one thunk at a time, so if you break up your program into small enough thunks and bounce each one off the trampoline, then you can be sure the stack won't grow too big.
In the categorical sense, the theory behind such data types is closely related to Cofree Monads
and fold
and unfold
functions, and in general to Fixed point types
.
See this fantastic talk: Fun and Games with Fix Cofree and Doobie by Rob Norris which discusses a use case very similar to your question.
This article about Free monads and Trampolines is also related to your first question: Stackless Scala With Free Monads.
See also this part of the Matryoshka docs. Matryoshka is a Scala library implementing monads around the concept of FixedPoint types.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install BigDL
Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger data set in a distributed fashion. The Orca library seamlessly scales out your single node TensorFlow or PyTorch notebook across large clusters (so as to process distributed Big Data).
Ray is an open source distributed framework for emerging AI applications. RayOnSpark allows users to directly run Ray programs on existing Big Data clusters, and directly write Ray code inline with their Spark code (so as to process the in-memory Spark RDDs or DataFrames). See the RayOnSpark user guide and quickstart for more details.
Time series prediction takes observations from previous time steps as input and predicts the values at future time steps. The Chronos library makes it easy to build end-to-end time series analysis by applying AutoML to extremely large-scale time series prediction.
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