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     new d16b128d8215 [SPARK-54245][INFRA] Install `mlflow` at Python 3.14 
Docker image
d16b128d8215 is described below

commit d16b128d8215a3a6ac75a84d91cf6ef24a55e6e5
Author: Dongjoon Hyun <[email protected]>
AuthorDate: Fri Nov 7 17:50:58 2025 -0800

    [SPARK-54245][INFRA] Install `mlflow` at Python 3.14 Docker image
    
    ### What changes were proposed in this pull request?
    
    This PR aims to install `mlflow` at Python 3.14 Docker image.
    
    ### Why are the changes needed?
    
    MLFlow 3.6.0 is released with Python 3.14 support via PyArrow 22.0.0.
    - https://pypi.org/project/mlflow/3.6.0/
    
    Like the other images, we will add the same condition `mlflow>=2.8.1`, but 
it will install `MLFlow>=3.6.0` effectively.
    
    
https://github.com/apache/spark/blob/a871ba4464e07cb0229b1289b125bb07da7b6265/dev/spark-test-image/python-313/Dockerfile#L71
    
    ### Does this PR introduce _any_ user-facing change?
    
    No behavior change.
    
    ### How was this patch tested?
    
    Manual review.
    
    ### Was this patch authored or co-authored using generative AI tooling?
    
    No.
    
    Closes #52948 from dongjoon-hyun/SPARK-54245.
    
    Authored-by: Dongjoon Hyun <[email protected]>
    Signed-off-by: Dongjoon Hyun <[email protected]>
---
 dev/spark-test-image/python-314/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/spark-test-image/python-314/Dockerfile 
b/dev/spark-test-image/python-314/Dockerfile
index 236e95e3f52c..07916fc35a0d 100644
--- a/dev/spark-test-image/python-314/Dockerfile
+++ b/dev/spark-test-image/python-314/Dockerfile
@@ -68,7 +68,7 @@ RUN apt-get update && apt-get install -y \
     && rm -rf /var/lib/apt/lists/*
 
 
-ARG BASIC_PIP_PKGS="numpy pyarrow>=22.0.0 six==1.16.0 pandas==2.3.3 scipy 
plotly<6.0.0 coverage matplotlib openpyxl memory-profiler>=0.61.0 
scikit-learn>=1.3.2"
+ARG BASIC_PIP_PKGS="numpy pyarrow>=22.0.0 six==1.16.0 pandas==2.3.3 scipy 
plotly<6.0.0 mlflow>=2.8.1 coverage matplotlib openpyxl memory-profiler>=0.61.0 
scikit-learn>=1.3.2"
 # Python deps for Spark Connect
 ARG CONNECT_PIP_PKGS="grpcio==1.76.0 grpcio-status==1.76.0 protobuf==6.33.0 
googleapis-common-protos==1.71.0 graphviz==0.20.3"
 


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