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zeppelin | Webbased notebook that enables datadriven

 by   apache Java Version: Current License: Apache-2.0

 by   apache Java Version: Current License: Apache-2.0

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kandi X-RAY | zeppelin Summary

zeppelin is a Java library typically used in Big Data, Jupyter, Spark applications. zeppelin has build file available, it has a Permissive License and it has medium support. However zeppelin has 711 bugs and it has 28 vulnerabilities. You can install using 'npm i zeppelin-vis' or download it from GitHub, npm.
Zeppelin, a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. To know more about Zeppelin, visit our web site https://zeppelin.apache.org.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • zeppelin has a medium active ecosystem.
  • It has 5639 star(s) with 2619 fork(s). There are 330 watchers for this library.
  • It had no major release in the last 12 months.
  • zeppelin has no issues reported. There are 102 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of zeppelin is current.
zeppelin Support
Best in #Java
Average in #Java
zeppelin Support
Best in #Java
Average in #Java

quality kandi Quality

  • zeppelin has 711 bugs (28 blocker, 3 critical, 320 major, 360 minor) and 5169 code smells.
zeppelin Quality
Best in #Java
Average in #Java
zeppelin Quality
Best in #Java
Average in #Java

securitySecurity

  • zeppelin has 6 vulnerability issues reported (1 critical, 3 high, 2 medium, 0 low).
  • zeppelin code analysis shows 22 unresolved vulnerabilities (21 blocker, 0 critical, 1 major, 0 minor).
  • There are 90 security hotspots that need review.
zeppelin Security
Best in #Java
Average in #Java
zeppelin Security
Best in #Java
Average in #Java

license License

  • zeppelin 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.
zeppelin License
Best in #Java
Average in #Java
zeppelin License
Best in #Java
Average in #Java

buildReuse

  • zeppelin releases are not available. You will need to build from source code and install.
  • Deployable package is available in npm.
  • Build file is available. You can build the component from source.
  • Installation instructions are available. Examples and code snippets are not available.
  • zeppelin saves you 203711 person hours of effort in developing the same functionality from scratch.
  • It has 203831 lines of code, 14402 functions and 1765 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
zeppelin Reuse
Best in #Java
Average in #Java
zeppelin Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

kandi has reviewed zeppelin and discovered the below as its top functions. This is intended to give you an instant insight into zeppelin implemented functionality, and help decide if they suit your requirements.

  • Convert a query to a query
  • Convert the specified Properties to Jinja parameters .
  • Performs kerberos authentication .
  • stream execute request
  • Splits the given string using the given escape sequence .
  • Sets up the local resources to launch the VM .
  • Runs the interpreter .
  • Copy run files to container .
  • Creates a programmatic config loader
  • Splits the given text .

zeppelin Key Features

Web based notebook style editor.

Built-in Apache Spark support

Cannot find conda info. Please verify your conda installation on EMR

copy iconCopydownload iconDownload
wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"
-----------------------
wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"

Does Zeppelin 0.10.0 try to run interpreter in k8s cluster?

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gofabric8 start

Printing Unique Values in a Column as a Percentage of Total Rows

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import org.apache.spark.sql.functions.{col, countDistinct, count}

import spark.implicits._

// define a dataframe for example
val data = Seq(("1", "1"), ("1", "2"), ("1", "3"), ("1", "4")).toDF("col_a", "col_b")

data.select(data.columns.map(c => (lit(100) * countDistinct(col(c)) / count(col(c))).alias(c)): _*).show()

// output:
+-----+-----+
|col_a|col_b|
+-----+-----+
| 25.0|100.0|
+-----+-----+

User "system:serviceaccount:default:flink" cannot list resource "nodes" in API group "" at the cluster scope

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...
dnsPolicy: ClusterFirst
restartPolicy: Always
schedulerName: default-scheduler
securityContext: {}
terminationGracePeriodSeconds: 30
...

CSS Keyframe Animations

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#SLIDE_BG {
  width: 100%;
  height: 100vh;
  background-position: center center;
  background-size: cover;
  background-repeat: no-repeat;
  backface-visibility: hidden;
  animation: slideBg 8s linear infinite 0s;
  animation-timing-function: ease-in-out;
  background-image: url('https://dummyimage.com/800x300');         
}

@keyframes slideBg {
  0% {
    background-image: url('https://upload.wikimedia.org/wikipedia/commons/2/29/Dscn7471_sunset-sundog_crop_800x300.jpg');
  }
  25% {
    background-image: url('https://www.thegrandsiba.com/wp-content/uploads/2017/06/MG_28423-800x300.jpg');
  }
  50% {
    background-image: url('https://dummyimage.com/800x300');
  }
  75% {
    background-image: url('https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/5e2a19d4-f261-4548-b4a7-1e2f5ad139af/d99najf-e9049704-bfc4-4836-952d-9315403bd60f.gif');
  }
  100% {
    background-image: url('https://www.businessclass.co.uk/wp-content/uploads/sites/11/2016/04/Toronto-800x300-800x300.jpg');
  }
}
-----------------------
#SLIDE_BG {
  width: 100%;
  height: 100vh;
  background-position: center center;
  background-size: cover;
  background-repeat: no-repeat;
  backface-visibility: hidden;
  animation: slideBg 8s linear infinite 0s;
  animation-timing-function: ease-in-out;
  background-image: url('https://jooinn.com/images/dramatic-landscape-7.jpg');
}

@keyframes slideBg {
  0% {
    background-image: url('https://jooinn.com/images/dramatic-landscape-7.jpg');
  }
  25% {
    background-image: url('http://www.thewowstyle.com/wp-content/uploads/2015/01/nature-image.jpg');
  }
  50% {
    background-image: url('https://images.designtrends.com/wp-content/uploads/2016/01/04085621/A-Cold-Sunset-Background.jpg');
  }
  75% {
    background-image: url('https://jooinn.com/images/hdr-landscape-1.jpg');
  }
  100% {
    background-image: url('https://www.shutterstock.com/blog/wp-content/uploads/sites/5/2016/03/fall-trees-road-1.jpg');
  }
}
<div id="SLIDE_BG"></div>
-----------------------
#SLIDE_BG {
  width: 100%;
  height: 100vh;
  background-position: center center;
  background-size: cover;
  background-repeat: no-repeat;
  backface-visibility: hidden;
  animation: slideBg 8s linear infinite 0s;
  animation-timing-function: ease-in-out;
  background-image: url('https://jooinn.com/images/dramatic-landscape-7.jpg');
}

@keyframes slideBg {
  0% {
    background-image: url('https://jooinn.com/images/dramatic-landscape-7.jpg');
  }
  25% {
    background-image: url('http://www.thewowstyle.com/wp-content/uploads/2015/01/nature-image.jpg');
  }
  50% {
    background-image: url('https://images.designtrends.com/wp-content/uploads/2016/01/04085621/A-Cold-Sunset-Background.jpg');
  }
  75% {
    background-image: url('https://jooinn.com/images/hdr-landscape-1.jpg');
  }
  100% {
    background-image: url('https://www.shutterstock.com/blog/wp-content/uploads/sites/5/2016/03/fall-trees-road-1.jpg');
  }
}
<div id="SLIDE_BG"></div>

totalsupply() is not a function openzeppelin contracts

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contract Color is ERC721, IERC721Enumerable { // We must extends IERC721Enumerable 
    string[] public colors;
    mapping(string => bool) _colorExists;

    constructor() ERC721("Color", "COLOR") {}

    function mint(string memory _color) public {
        colors.push(_color);
        uint256 _id = colors.length - 1;

        // _mint(msg.sender,_id);
        _colorExists[_color] = true;
    }
    

    // And must override below three functions

    function tokenOfOwnerByIndex(address owner, uint256 index) public view override returns (uint256) {
        // You need update this logic.
        // ...
        return 3;
    }

    function totalSupply() external  view override returns (uint256) {
      // You need update this logic.
      // ...
      return 1;
    }

    function tokenByIndex(uint256 index) external view  override returns (uint256) {
      // You need update this logic.
      // ...
      return 5;
    }
}
-----------------------
pragma solidity ^0.8.0; // Note that this is using a newer version than in 

import "@openzeppelin/contracts/token/ERC721/ERC721.sol";
import "@openzeppelin/contracts/token/ERC721/extensions/ERC721Enumerable.sol";


contract Color is ERC721, ERC721Enumerable {
    string[] public colors;
    mapping(string => bool) _colorExists;

    constructor() ERC721("Color", "COLOR") public {
    }

    function _beforeTokenTransfer(address from, address to, uint256 tokenId)
    internal
    override(ERC721, ERC721Enumerable)
    {
        super._beforeTokenTransfer(from, to, tokenId);
    }

    function supportsInterface(bytes4 interfaceId)
    public
    view
    override(ERC721, ERC721Enumerable)
    returns (bool)
    {
        return super.supportsInterface(interfaceId);
    }


    function mint(string memory _color) public {
        colors.push(_color);
        uint _id = colors.length - 1;
        _mint(msg.sender, _id);
        _colorExists[_color] = true;
    }
}


How can find the find occurence in a window/group and fetch all unbounded rows prior to that?

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SELECT
    accountId,
    customerId,
    sessionDate,
    didPurchase
FROM (
    SELECT 
        *,
        MIN(CASE
            WHEN didPurchase THEN sessionDate
        END) OVER (
            PARTITION BY accountId, customerId
        ) as prior_oldest_purchase
     FROM
        myvisits
) v
WHERE
     v.sessionDate <= v.prior_oldest_purchase
ORDER BY
    accountId DESC, customerId,sessionDate DESC
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window

val customerAccountWindow = Window.partitionBy("accountId","customerId")

val outputDf = df.withColumn(
                    "prior_oldest_purchase",
                    min(
                        when(col("didPurchase"),col("sessionDate"))
                    ).over(customerAccountWindow)
               )
               .where(col("sessionDate") <= col("prior_oldest_purchase"))
               .select(
                   col("accountId"),
                   col("customerId"),
                   col("sessionDate"),
                   col("didPurchase")
               )
               .orderBy(
                   col("accountId").desc(),
                   col("customerId"),
                   col("sessionDate").desc()
               )

from pyspark.sql import functions as F
from pyspark.sql import Window

customerAccountWindow = Window.partitionBy("accountId","customerId")

outputDf = (
    df.withColumn(
        "prior_oldest_purchase",
        F.min(
            F.when(F.col("didPurchase"),F.col("sessionDate"))
        ).over(customerAccountWindow)
    )
    .where(F.col("sessionDate") <= F.col("prior_oldest_purchase"))
    .select(
        F.col("accountId"),
        F.col("customerId"),
        F.col("sessionDate"),
        F.col("didPurchase")
     )
     .orderBy(
        F.col("accountId").desc(),
        F.col("customerId"),
        F.col("sessionDate").desc()
      )
)
-----------------------
SELECT
    accountId,
    customerId,
    sessionDate,
    didPurchase
FROM (
    SELECT 
        *,
        MIN(CASE
            WHEN didPurchase THEN sessionDate
        END) OVER (
            PARTITION BY accountId, customerId
        ) as prior_oldest_purchase
     FROM
        myvisits
) v
WHERE
     v.sessionDate <= v.prior_oldest_purchase
ORDER BY
    accountId DESC, customerId,sessionDate DESC
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window

val customerAccountWindow = Window.partitionBy("accountId","customerId")

val outputDf = df.withColumn(
                    "prior_oldest_purchase",
                    min(
                        when(col("didPurchase"),col("sessionDate"))
                    ).over(customerAccountWindow)
               )
               .where(col("sessionDate") <= col("prior_oldest_purchase"))
               .select(
                   col("accountId"),
                   col("customerId"),
                   col("sessionDate"),
                   col("didPurchase")
               )
               .orderBy(
                   col("accountId").desc(),
                   col("customerId"),
                   col("sessionDate").desc()
               )

from pyspark.sql import functions as F
from pyspark.sql import Window

customerAccountWindow = Window.partitionBy("accountId","customerId")

outputDf = (
    df.withColumn(
        "prior_oldest_purchase",
        F.min(
            F.when(F.col("didPurchase"),F.col("sessionDate"))
        ).over(customerAccountWindow)
    )
    .where(F.col("sessionDate") <= F.col("prior_oldest_purchase"))
    .select(
        F.col("accountId"),
        F.col("customerId"),
        F.col("sessionDate"),
        F.col("didPurchase")
     )
     .orderBy(
        F.col("accountId").desc(),
        F.col("customerId"),
        F.col("sessionDate").desc()
      )
)
-----------------------
SELECT
    accountId,
    customerId,
    sessionDate,
    didPurchase
FROM (
    SELECT 
        *,
        MIN(CASE
            WHEN didPurchase THEN sessionDate
        END) OVER (
            PARTITION BY accountId, customerId
        ) as prior_oldest_purchase
     FROM
        myvisits
) v
WHERE
     v.sessionDate <= v.prior_oldest_purchase
ORDER BY
    accountId DESC, customerId,sessionDate DESC
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window

val customerAccountWindow = Window.partitionBy("accountId","customerId")

val outputDf = df.withColumn(
                    "prior_oldest_purchase",
                    min(
                        when(col("didPurchase"),col("sessionDate"))
                    ).over(customerAccountWindow)
               )
               .where(col("sessionDate") <= col("prior_oldest_purchase"))
               .select(
                   col("accountId"),
                   col("customerId"),
                   col("sessionDate"),
                   col("didPurchase")
               )
               .orderBy(
                   col("accountId").desc(),
                   col("customerId"),
                   col("sessionDate").desc()
               )

from pyspark.sql import functions as F
from pyspark.sql import Window

customerAccountWindow = Window.partitionBy("accountId","customerId")

outputDf = (
    df.withColumn(
        "prior_oldest_purchase",
        F.min(
            F.when(F.col("didPurchase"),F.col("sessionDate"))
        ).over(customerAccountWindow)
    )
    .where(F.col("sessionDate") <= F.col("prior_oldest_purchase"))
    .select(
        F.col("accountId"),
        F.col("customerId"),
        F.col("sessionDate"),
        F.col("didPurchase")
     )
     .orderBy(
        F.col("accountId").desc(),
        F.col("customerId"),
        F.col("sessionDate").desc()
      )
)

1st Sept coming before 31st Aug in bar chart ordering by date in Zeppelin, how to fix please?

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SELECT FROM_TIMESTAMP(DATE_TRUNC('HOUR', concat(replace(my_timestamp,'"',''), "Z")), 'd MMM HH:mm') AS hours, 
       FROM_TIMESTAMP(DATE_TRUNC('HOUR', concat(replace(my_timestamp,'"',''), "Z")), 'yyyy-MM-dd HH:mm') as dt,
       COUNT(my_number) AS "number per hour"
FROM my_table
WHERE unix_timestamp(my_timestamp) --also it seems Z should be removed, etc 
      > (unix_timestamp(now()) - 86400)
GROUP BY dt, hours
ORDER BY dt
LIMIT 24;

AWS EMR: Zeppelin taking numpy version from python 2.7 instead of higher version

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sudo python3 -m pip install h5py==2.10.0
sudo python3 -m pip install keras==2.3.1
sudo python3 -m pip install keras_applications==1.0.8 --no-deps
sudo python3 -m pip install keras_preprocessing==1.1.0 --no-deps

sudo python3 -m pip install tqdm==4.40.0

sudo python3 -m pip install s3fs 
sudo python3 -m pip install ipaddress==1.0.23
sudo python3 -m pip install netaddr==0.7.19 

sudo python3 -m pip install matplotlib

sudo python3 -m pip install pyarrow==0.12.1
sudo python3 -m pip install boto3
sudo python3 -m pip install torch==1.6.0
sudo python3 -m pip install --upgrade scipy==1.4.1
sudo python3 -m pip install torchvision
sudo python3 -m pip install pydot==1.4.1
sudo python3 -m pip install xlrd
sudo python3 -m pip install xlwt
sudo python3 -m pip install pandas==1.2.0
sudo python3 -m pip install scikit-learn
sudo python3 -m pip install scikit-multilearn 
sudo python3 -m pip install wrapt==1.12.0
sudo python3 -m pip install tensorboard==2.1.0
sudo python3 -m pip install tensorflow==2.1.0
sudo python3 -m pip install tensorflow-estimator==2.1.0


Apache Zeppelin - Jquery datepicker in angular cell doesn't work

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%angular
<link rel="stylesheet" href="//code.jquery.com/ui/1.12.0/themes/base/jquery-ui.css">
<script src="https://code.jquery.com/jquery-1.12.4.js"></script>
<script src="https://code.jquery.com/ui/1.12.0/jquery-ui.js"></script>

<script>
    angular.element( function() {
        angular.element( "#todatepicker" ).datepicker({ dateFormat: 'yy-mm-dd',changeMonth: true,changeYear: true, minDate: new Date(1900, 1, 1), yearRange: '1900:+00' });
        angular.element( "#fromdatepicker" ).datepicker({ dateFormat: 'yy-mm-dd',changeMonth: true,changeYear: true,  minDate: new Date(1900, 1, 1), yearRange: '1900:+00' });
    } );
    
    function changeMaxDate(val){
         angular.element('#fromdatepicker').datepicker('option', 'maxDate', new Date(val));
    }
    
    function changeMinDate(val){
        angular.element('#todatepicker').datepicker('option', 'minDate', new Date(val));
    } 
        
</script>

<form class="form-inline">

    <div style="text-align:center; margin-bottom:20px">
    <button type="submit" class="btn btn-primary"  ng-click="z.runParagraph('20210728-173149_661735685')" > Load data </button>
    </div>

    <div style="text-align:center">

            <label for="fromDateId" >From: </label>
            <input type="text"  id="fromdatepicker" ng-model="fromDate" onChange="changeMinDate(this.value);" autocomplete="off"> </input>
            <label for="toDateId"style="margin-left:5px"> to: </label>
            <input type="text" id="todatepicker" ng-model="toDate" onChange="changeMaxDate(this.value);" autocomplete="off"> </input>

            <label style="margin-left:30px"> City: </label>
            <input type="text" ng-model="city"> </input>

            <label for="genders" style="margin-left:30px">Gender:</label>
            <select name="genders" id="genders" ng-model="gender">
                <option value="both">Both</option>
                <option value="F">Female</option>
                <option value="M">Male</option>
            </select>

    </div>
    <div style="text-align:center; margin-top:20px">
    <button type="submit" class="btn btn-primary" ng-click="z.angularBind('toDate',toDate,'20210727-110725_1586668489');z.angularBind('fromDate',fromDate,'20210727-110725_1586668489');z.angularBind('city',city,'20210727-110725_1586668489');z.angularBind('gender',gender,'20210727-110725_1586668489');z.runParagraph('20210727-110725_1586668489');z.runParagraph('20210727-111144_1584153174')">Search</button>
    </div>
</form>

Community Discussions

Trending Discussions on zeppelin
  • Cannot find conda info. Please verify your conda installation on EMR
  • Dataproc Cluster creation is failing with PIP error &quot;Could not build wheels&quot;
  • Does Zeppelin 0.10.0 try to run interpreter in k8s cluster?
  • Apache Zeppelin Error When Importing Pandas
  • Printing Unique Values in a Column as a Percentage of Total Rows
  • User &quot;system:serviceaccount:default:flink&quot; cannot list resource &quot;nodes&quot; in API group &quot;&quot; at the cluster scope
  • CSS Keyframe Animations
  • totalsupply() is not a function openzeppelin contracts
  • Spark - Scala: &quot;error: not found: value transform&quot;
  • How can find the find occurence in a window/group and fetch all unbounded rows prior to that?
Trending Discussions on zeppelin

QUESTION

Cannot find conda info. Please verify your conda installation on EMR

Asked 2022-Feb-05 at 00:17

I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH variable on EMR master node, it can identify conda. I want to use conda on Zeppelin.

I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.

    "classification": "spark-env",
    "properties": {
        "conda": "/home/hadoop/conda/bin"
    }
[hadoop@ip-172-30-5-150 ~]$ PATH=/home/hadoop/conda/bin:$PATH
[hadoop@ip-172-30-5-150 ~]$ conda
usage: conda [-h] [-V] command ...

conda is a tool for managing and deploying applications, environments and packages.
#!/usr/bin/env bash


# Install conda
wget https://repo.continuum.io/miniconda/Miniconda3-4.2.12-Linux-x86_64.sh -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda


conda config --set always_yes yes --set changeps1 no
conda install conda=4.2.13
conda config -f --add channels conda-forge
rm ~/miniconda.sh
echo bootstrap_conda.sh completed. PATH now: $PATH
export PYSPARK_PYTHON="/home/hadoop/conda/bin/python3.5"

echo -e '\nexport PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like.
conda activate zoo
sudo pip3 install tensorflow
sudo pip3 install boto3
sudo pip3 install botocore
sudo pip3 install numpy
sudo pip3 install pandas
sudo pip3 install scipy
sudo pip3 install s3fs
sudo pip3 install matplotlib
sudo pip3 install -U tqdm
sudo pip3 install -U scikit-learn
sudo pip3 install -U scikit-multilearn
sudo pip3 install xlutils
sudo pip3 install natsort
sudo pip3 install pydot
sudo pip3 install python-pydot
sudo pip3 install python-pydot-ng
sudo pip3 install pydotplus
sudo pip3 install h5py
sudo pip3 install graphviz
sudo pip3 install recmetrics
sudo pip3 install openpyxl
sudo pip3 install xlrd
sudo pip3 install xlwt
sudo pip3 install tensorflow.io
sudo pip3 install Cython
sudo pip3 install ray
sudo pip3 install zoo
sudo pip3 install analytics-zoo
sudo pip3 install analytics-zoo[ray]
#sudo /usr/bin/pip-3.6 install -U imbalanced-learn


enter image description here

ANSWER

Answered 2022-Feb-05 at 00:17

I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:

wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv

and setting zeppelin python and pyspark parameters to:

“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"

Orca only support TF upto 1.5 hence it was not working as I am using TF2.

Source https://stackoverflow.com/questions/70901724

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