This is an automated email from the ASF dual-hosted git repository.

dongjoon-hyun pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/spark.git


The following commit(s) were added to refs/heads/master by this push:
     new c470f3dce544 [SPARK-58046][DOC] Fix the required NumPy version to 
1.23.2 in MLlib guide
c470f3dce544 is described below

commit c470f3dce544ad46ffb748949b6476d3cbc8d443
Author: Dongjoon Hyun <[email protected]>
AuthorDate: Wed Jul 8 12:40:14 2026 -0700

    [SPARK-58046][DOC] Fix the required NumPy version to 1.23.2 in MLlib guide
    
    ### What changes were proposed in this pull request?
    
    This PR updates the minimum NumPy version in `docs/ml-guide.md` from `1.4` 
to `1.23.2`.
    
    ### Why are the changes needed?
    
    SPARK-56928 raised the minimum NumPy version to `1.23.2`
    - https://github.com/apache/spark/pull/55959
    
    
https://github.com/apache/spark/blob/5ca6b1062887664b16b11f2bfcc015c9e616dc49/python/packaging/classic/setup.py#L162
    
    ### Does this PR introduce _any_ user-facing change?
    
    No.
    
    ### How was this patch tested?
    
    Manual review.
    
    ### Was this patch authored or co-authored using generative AI tooling?
    
    Generated-by: Claude Fable 5
    
    Closes #57132 from dongjoon-hyun/SPARK-58046.
    
    Authored-by: Dongjoon Hyun <[email protected]>
    Signed-off-by: Dongjoon Hyun <[email protected]>
---
 docs/ml-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 132805e7bcd6..64c469d906e3 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -69,7 +69,7 @@ However, native acceleration libraries can't be distributed 
with Spark. See [MLl
 WARNING: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS
 ```
 
-To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 
1.4 or newer.
+To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 
1.23.2 or newer.
 
 [^1]: To learn more about the benefits and background of system optimised 
natives, you may wish to
     watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in 
Scala](http://fommil.github.io/scalax14/).


---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]

Reply via email to