BigDL | Fast, distributed, secure AI for Big Data

 by   intel-analytics Jupyter Notebook Version: 2.5.0b20240324 License: Apache-2.0

kandi X-RAY | BigDL Summary

kandi X-RAY | BigDL Summary

BigDL is a Jupyter Notebook library typically used in Big Data, Deep Learning, Pytorch, Tensorflow, Spark, Hadoop applications. BigDL has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

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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              BigDL has a medium active ecosystem.
              It has 4229 star(s) with 1097 fork(s). There are 237 watchers for this library.
              There were 10 major release(s) in the last 12 months.
              There are 553 open issues and 1166 have been closed. On average issues are closed in 71 days. There are 195 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of BigDL is 2.5.0b20240324

            kandi-Quality Quality

              BigDL has no bugs reported.

            kandi-Security Security

              BigDL has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              BigDL is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              BigDL releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            BigDL Key Features

            No Key Features are available at this moment for BigDL.

            BigDL Examples and Code Snippets

            Why is pd.series(cell_list) deleting the last elements of my cell_list?
            Lines of Code : 11dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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
            
            How to backtest a strategy from a specified date
            Lines of Code : 26dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            // 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 +
            How to draw line from condition to current close for all occurences of the condition
            Lines of Code : 34dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            // 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
            Mule 4 dynamic queries in the Database Connector
            Lines of Code : 57dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            #["SELECT * FROM mulesoft 
                     WHERE " ++ vars.SORT_KEY.FILTER_KEY ++ " = '" ++ vars.SORT_KEY.FILTER_VALS ++ "'"]
            
            
            
            
            
                
                    
                
            
                
                    
                
                
                    
                    
                    
                        #["SELEC
            How to find highest and lowest value on substrings?
            Javadot img5Lines of Code : 15dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Comparator numberValueComparator = Comparator.comparing(BigDecimal::new);
            List groupedValue = partitions.stream()
                    .map(p -> String.format("%s  High: %s, Low: %s",
                            String.join(", ", p),
                            Collections
            How import a 3D numpy array?
            Lines of Code : 38dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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
              
            3D gaussian generator/transform
            Lines of Code : 50dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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
            copy iconCopy
            %dw 2.0
            output application/json
            var address = payload.address[0]
            ---
            address ++ (payload - "address")
            
            reading a file in mule and parsing with dataweave
            Lines of Code : 11dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            %dw 2.0
            output application/json
            ---
            read(payload, "applicatin/csv", {"header": false, "separator": "|" }) map (
                {
                    id: $[0],
                    product: $[1],
                    price: $[2]
                }
            )
            
            Transform all DataFrame Columns of a specific Type in Julia
            Lines of Code : 19dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df .= Float64.(df)
            
            transform!(df, All() .=> ByRow(Float64), renamecols=false)
            
            mapcols!(ByRow(Float64), df)
            
            julia> transform!(df, names(df, Int) .=> ByRow(F

            Community Discussions

            QUESTION

            mean() got an unexpected keyword argument 'dtype'!
            Asked 2019-Apr-10 at 09:01

            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 11:25

            The 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,

            Source https://stackoverflow.com/questions/45054810

            QUESTION

            AssertionError: Multiple .dist-info directories on Data Science Experience
            Asked 2019-Apr-10 at 07:57

            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 10:31

            The 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:

            Source https://stackoverflow.com/questions/47179822

            QUESTION

            Install BigDL in Data Science Experience on Cloud
            Asked 2019-Apr-07 at 13:57

            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 05:13

            If 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.

            1. 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.
            2. 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 Python

            You 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:

            1. Install the JAR.

            Source https://stackoverflow.com/questions/47282618

            QUESTION

            Running BigDL Text Classifier fails
            Asked 2019-Apr-07 at 09:14

            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 06:01

            The 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.

            Source https://stackoverflow.com/questions/48936131

            QUESTION

            Output results of spark-submit
            Asked 2018-Apr-26 at 22:47

            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 10:50

            Try 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.

            Source https://stackoverflow.com/questions/50040938

            QUESTION

            Is there a universal method to create a tail recursive function in Scala?
            Asked 2017-Mar-22 at 20:53

            While checking Intel's BigDL repo, I stumbled upon this method:

            ...

            ANSWER

            Answered 2017-Mar-22 at 20:53
            Short answer

            If 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:

            A 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.

            More + References

            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.

            Source https://stackoverflow.com/questions/42911188

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install BigDL

            DLlib is a distributed deep learning library for Apache Spark; with DLlib, users can write distributed deep learning applications as standard Spark programs (using either Scala or Python APIs).
            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.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

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            Install
          • PyPI

            pip install bigdl

          • CLONE
          • HTTPS

            https://github.com/intel-analytics/BigDL.git

          • CLI

            gh repo clone intel-analytics/BigDL

          • sshUrl

            git@github.com:intel-analytics/BigDL.git

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