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added snapshot to release version
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Dockerfile

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@@ -4,17 +4,17 @@ USER 0
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RUN apt-get update && \
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apt install -y curl vim
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ENV SPARK_MASTER local[*]
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ENV ZINGG_HOME /zingg-0.4.1
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ENV ZINGG_HOME /zingg-0.4.1-SNAPSHOT
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ENV PATH $ZINGG_HOME/scripts:$PATH
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ENV LANG C.UTF-8
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WORKDIR /
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USER root
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WORKDIR /zingg-0.4.1
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RUN curl --location https://github.com/zinggAI/zingg/releases/download/v0.4.1/zingg-0.4.1-spark-3.4.0.tar.gz | \
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WORKDIR /zingg-0.4.1-SNAPSHOT
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RUN curl --location https://github.com/zinggAI/zingg/releases/download/v0.4.1-SNAPSHOT/zingg-0.4.1-SNAPSHOT-spark-3.5.0.tar.gz | \
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tar --extract --gzip --strip=1
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RUN pip install -r python/requirements.txt
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RUN pip install zingg
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RUN chmod -R a+rwx /zingg-0.4.1/models
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RUN chown -R 1001 /zingg-0.4.1/models
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RUN chmod -R a+rwx /zingg-0.4.1-SNAPSHOT/models
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RUN chown -R 1001 /zingg-0.4.1-SNAPSHOT/models
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USER 1001
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README.md

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@@ -60,8 +60,8 @@ See Zingg in action [here](https://www.youtube.com/watch?v=zOabyZxN9b0)
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The easiest way to get started with Zingg is through Docker and by running the prebuilt models.
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```
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docker pull zingg/zingg:0.4.1
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docker run -it zingg/zingg:0.4.1 bash
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docker pull zingg/zingg:0.4.1-SNAPSHOT
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docker run -it zingg/zingg:0.4.1-SNAPSHOT bash
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./scripts/zingg.sh --phase match --conf examples/febrl/config.json
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```
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common/client/src/main/java/zingg/common/client/Client.java

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@@ -120,7 +120,7 @@ else if (args.getJobId() != -1) {
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}
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public void printBanner() {
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String versionStr = "0.4.1";
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String versionStr = "0.4.1-SNAPSHOT";
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LOG.info("");
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LOG.info("********************************************************");
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LOG.info("* Zingg AI *");

common/core/src/main/java/zingg/common/core/executor/ZinggBase.java

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@@ -86,7 +86,7 @@ public void postMetrics() {
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Analytics.track(Metric.DATA_FORMAT, getPipeUtil().getPipesAsString(args.getData()), collectMetrics);
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Analytics.track(Metric.OUTPUT_FORMAT, getPipeUtil().getPipesAsString(args.getOutput()), collectMetrics);
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Analytics.track(Metric.MODEL_ID, args.getModelId(), collectMetrics);
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Analytics.track(Metric.ZINGG_VERSION, "0.4.1", collectMetrics);
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Analytics.track(Metric.ZINGG_VERSION, "0.4.1-SNAPSHOT", collectMetrics);
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Analytics.trackEnvProp(Metric.DATABRICKS_RUNTIME_VERSION, collectMetrics);
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Analytics.trackEnvProp(Metric.DB_INSTANCE_TYPE, collectMetrics);
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Analytics.trackEnvProp(Metric.JAVA_HOME, collectMetrics);
@@ -96,7 +96,7 @@ public void postMetrics() {
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//Analytics.trackEnvProp(Metric.USER_NAME, collectMetrics);
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//Analytics.trackEnvProp(Metric.USER_HOME, collectMetrics);
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Analytics.trackDomain(Metric.DOMAIN, collectMetrics);
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Analytics.track(Metric.ZINGG_VERSION, "0.4.1", collectMetrics);
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Analytics.track(Metric.ZINGG_VERSION, "0.4.1-SNAPSHOT", collectMetrics);
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Analytics.postEvent(zinggOptions.getValue(), collectMetrics);
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}
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docs/stepbystep/installation/docker/README.md

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@@ -9,8 +9,8 @@ description: From pre-built Docker image with all dependencies included
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The easiest way to get started is to pull the Docker image with the last release of Zingg.
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```
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docker pull zingg/zingg:0.4.1
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docker run -it zingg/zingg:0.4.1 bash
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docker pull zingg/zingg:0.4.1-SNAPSHOT
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docker run -it zingg/zingg:0.4.1-SNAPSHOT bash
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```
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To know more about Docker, please refer to the official [docker documentation](https://docs.docker.com/).

docs/stepbystep/installation/docker/file-read-write-permissions.md

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@@ -9,5 +9,5 @@ A docker image is preferred to run with a non-root user. By default, the Zingg c
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```
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$ id
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uid=1000(abc) gid=1000(abc) groups=1000(abc)
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$ docker run -u <uid> -it zingg/zingg:0.4.1 bash
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$ docker run -u <uid> -it zingg/zingg:0.4.1-SNAPSHOT bash
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```

docs/stepbystep/installation/docker/sharing-custom-data-and-config-files.md

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@@ -7,7 +7,7 @@ description: Using custom data to save data files on host machine
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However, note that once the docker container is stopped, all the work done in that session is lost. If we want to use custom data or persist the generated model or data files, we have to use **Volumes** or **Bind mount** to share files between the two.
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```
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docker run -v <local-location>:<container-location> -it zingg/zingg:0.4.1 bash
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docker run -v <local-location>:<container-location> -it zingg/zingg:0.4.1-SNAPSHOT bash
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```
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The **\<local-location>** directory from host will get mounted inside container at **\<container-location>**. Any file written inside this directory will persist on the host machine and can be reused in a new container instance later.

docs/stepbystep/installation/installing-from-release/installing-zingg.md

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@@ -6,13 +6,13 @@ description: Downloading and setting things up
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Download the tar zingg-version.tar.gz from the [Zingg releases page](https://github.com/zinggAI/zingg/releases) to a folder of your choice and run the following:
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> gzip -d zingg-0.4.1.tar.gz ; tar xvf zingg-0.4.1.tar
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> gzip -d zingg-0.4.1-SNAPSHOT.tar.gz ; tar xvf zingg-0.4.1-SNAPSHOT.tar
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This will create a folder zingg-0.4.1 under the chosen folder.
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This will create a folder zingg-0.4.1-SNAPSHOT under the chosen folder.
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Move the above folder to zingg.
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> mv zingg-0.4.1 \~/zingg
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> mv zingg-0.4.1-SNAPSHOT \~/zingg
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> export ZINGG\_HOME=path to zingg
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pom.xml

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</profile>
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</profiles>
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<properties>
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<zingg.version>0.4.1</zingg.version>
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<zingg.version>0.4.1-SNAPSHOT</zingg.version>
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<skipTests>false</skipTests>
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<failIfNoTests>false</failIfNoTests>
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<maven.compiler.source>8</maven.compiler.source>

python/docs/conf.py

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author = 'Zingg.AI'
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# The full version, including alpha/beta/rc tags
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release = '0.4.1'
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release = '0.4.1-SNAPSHOT'
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# -- General configuration ---------------------------------------------------

python/zingg/client.py

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_spark_ctxt = None
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_sqlContext = None
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_spark = None
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_zingg_jar = 'zingg-0.4.1.jar'
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_zingg_jar = 'zingg-0.4.1-SNAPSHOT.jar'
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def initSparkClient():
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global _spark_ctxt

python/zingg/databricks.py

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}
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},
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{
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"jar": "dbfs:/FileStore/jars/zingg_0_4_0.jar"
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"jar": "dbfs:/FileStore/jars/zingg_0_4_1_SNAPSHOT.jar"
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}
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],
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"timeout_seconds": 0,

scripts/zingg.sh

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#!/bin/bash
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#ZINGG_HOME=./assembly/target
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ZINGG_JARS=$ZINGG_HOME/zingg-0.4.1.jar
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ZINGG_JARS=$ZINGG_HOME/zingg-0.4.1-SNAPSHOT.jar
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LICENSE=zinggLicense.txt
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log4j_setting="-Dlog4j2.configurationFile=file:log4j2.properties"

test/run_tests.sh

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#!/bin/bash
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# Set the paths to your JAR files and Spark binaries
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SPARK_HOME="/opt/spark-3.2.4-bin-hadoop3.2"
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PY4J_JAR="../common/client/target/zingg-common-client-0.4.1.jar"
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SPARK_HOME="/opt/spark-3.5.0-bin-hadoop3"
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PY4J_JAR="../common/client/target/zingg-common-client-0.4.1-SNAPSHOT.jar"
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# Run Spark with the required JAR files and your test script
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$SPARK_HOME/bin/spark-submit --jars $PY4J_JAR testInfra.py

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