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Run python3 dev/update_mlflow_versions.py post-release... (mlflow#14587)
Signed-off-by: mlflow-automation <[email protected]> Co-authored-by: mlflow-automation <[email protected]>
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-15
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mlflow/R/mlflow/DESCRIPTION

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Type: Package
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Package: mlflow
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Title: Interface to 'MLflow'
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Version: 2.20.2
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Version: 2.20.3
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Authors@R:
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c(person(given = "Matei",
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family = "Zaharia",

mlflow/java/client/pom.xml

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<parent>
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<groupId>org.mlflow</groupId>
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<artifactId>mlflow-parent</artifactId>
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<version>2.20.2-SNAPSHOT</version>
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<version>2.20.3-SNAPSHOT</version>
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<relativePath>../pom.xml</relativePath>
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</parent>
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mlflow/java/pom.xml

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<modelVersion>4.0.0</modelVersion>
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<groupId>org.mlflow</groupId>
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<artifactId>mlflow-parent</artifactId>
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<version>2.20.2-SNAPSHOT</version>
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<version>2.20.3-SNAPSHOT</version>
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<packaging>pom</packaging>
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<name>MLflow Parent POM</name>
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<url>http://mlflow.org</url>
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</distributionManagement>
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<properties>
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<mlflow-version>2.20.2-SNAPSHOT</mlflow-version>
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<mlflow-version>2.20.3-SNAPSHOT</mlflow-version>
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<maven.compiler.source>1.8</maven.compiler.source>
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<maven.compiler.target>1.8</maven.compiler.target>
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<scala.version>2.11.12</scala.version>

mlflow/java/scoring/pom.xml

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<parent>
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<groupId>org.mlflow</groupId>
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<artifactId>mlflow-parent</artifactId>
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<version>2.20.2-SNAPSHOT</version>
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<version>2.20.3-SNAPSHOT</version>
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<relativePath>../pom.xml</relativePath>
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</parent>
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mlflow/java/scoring/src/main/java/org/mlflow/sagemaker/ScoringServer.java

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@@ -172,7 +172,7 @@ static class VersionServlet extends HttpServlet {
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@Override
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public void doGet(HttpServletRequest request, HttpServletResponse response) throws IOException {
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response.setStatus(HttpServletResponse.SC_OK);
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response.getWriter().print("2.20.2-SNAPSHOT");
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response.getWriter().print("2.20.3-SNAPSHOT");
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response.getWriter().close();
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}
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}

mlflow/java/scoring/src/test/java/org/mlflow/ScoringServerTest.java

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HttpResponse response = httpClient.execute(getRequest);
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Assert.assertEquals(HttpServletResponse.SC_OK, response.getStatusLine().getStatusCode());
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String responseBody = getHttpResponseBody(response);
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Assert.assertEquals("2.20.2-SNAPSHOT", responseBody);
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Assert.assertEquals("2.20.3-SNAPSHOT", responseBody);
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server.stop();
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}
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mlflow/java/spark/pom.xml

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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<artifactId>mlflow-spark_${scala.compat.version}</artifactId>
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<version>2.20.2-SNAPSHOT</version>
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<version>2.20.3-SNAPSHOT</version>
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<name>${project.artifactId}</name>
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<properties>
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<maven.compiler.source>1.8</maven.compiler.source>
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<parent>
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<groupId>org.mlflow</groupId>
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<artifactId>mlflow-parent</artifactId>
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<version>2.20.2-SNAPSHOT</version>
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<version>2.20.3-SNAPSHOT</version>
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<relativePath>../pom.xml</relativePath>
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</parent>
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mlflow/server/js/src/common/constants.tsx

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RESOURCE_CONFLICT: 'RESOURCE_CONFLICT',
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};
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export const Version = '2.20.2.dev0';
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export const Version = '2.20.3.dev0';
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const DOCS_VERSION = 'latest';
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mlflow/version.py

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# Copyright 2018 Databricks, Inc.
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import re
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VERSION = "2.20.2.dev0"
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VERSION = "2.20.3.dev0"
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def is_release_version():

pyproject.release.toml

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[project]
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name = "mlflow"
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version = "2.20.2.dev0"
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version = "2.20.3.dev0"
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description = "MLflow is an open source platform for the complete machine learning lifecycle"
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readme = "README.md"
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keywords = ["mlflow", "ai", "databricks"]
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]
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requires-python = ">=3.9"
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dependencies = [
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"mlflow-skinny==2.20.2.dev0",
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"mlflow-skinny==2.20.3.dev0",
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"Flask<4",
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"Jinja2<4,>=2.11; platform_system != 'Windows'",
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"Jinja2<4,>=3.0; platform_system == 'Windows'",

pyproject.skinny.toml

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[project]
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name = "mlflow-skinny"
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version = "2.20.2.dev0"
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version = "2.20.3.dev0"
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description = "MLflow is an open source platform for the complete machine learning lifecycle"
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readme = "README.md"
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keywords = ["mlflow", "ai", "databricks"]

pyproject.toml

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[project]
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name = "mlflow"
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version = "2.20.2.dev0"
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version = "2.20.3.dev0"
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description = "MLflow is an open source platform for the complete machine learning lifecycle"
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readme = "README.md"
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keywords = ["mlflow", "ai", "databricks"]

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