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byronhsu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/submarine.git


The following commit(s) were added to refs/heads/master by this push:
     new 6478ac4  SUBMARINE-864. Support log_params and log_metrics
6478ac4 is described below

commit 6478ac4e581f5cfc908a0f865b605c95247b6fbb
Author: Justin Chang <[email protected]>
AuthorDate: Tue Jun 29 22:26:10 2021 +0800

    SUBMARINE-864. Support log_params and log_metrics
    
    ### What is this PR for?
    Support log_params and log_metrics
    
    ### What type of PR is it?
    [Improvements]
    
    ### Todos
    
    ### What is the Jira issue?
    https://issues.apache.org/jira/projects/SUBMARINE/issues/SUBMARINE-864
    
    ### How should this be tested?
    
    ### Screenshots (if appropriate)
    
    ### Questions:
    * Do the license files need updating? No
    * Are there breaking changes for older versions? No
    * Does this need new documentation? No
    
    Author: Justin Chang <[email protected]>
    
    Signed-off-by: byronhsu <[email protected]>
    
    Closes #623 from yuanchiachang/SUBMARINE-864 and squashes the following 
commits:
    
    445d0f28 [Justin Chang] SUBMARINE-864. Support log_params and log_metrics
---
 submarine-sdk/pysubmarine/submarine/models/client.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/submarine-sdk/pysubmarine/submarine/models/client.py 
b/submarine-sdk/pysubmarine/submarine/models/client.py
index 1bd7f22..0513ad3 100644
--- a/submarine-sdk/pysubmarine/submarine/models/client.py
+++ b/submarine-sdk/pysubmarine/submarine/models/client.py
@@ -57,9 +57,15 @@ class ModelsClient():
     def log_param(self, key, value):
         mlflow.log_param(key, value)
 
+    def log_params(self, params):
+        mlflow.log_params(params)
+
     def log_metric(self, key, value, step=None):
         mlflow.log_metric(key, value, step)
 
+    def log_metrics(self, metrics, step=None):
+        mlflow.log_metrics(metrics, step)
+
     def log_model(self, name, checkpoint):
         mlflow.pytorch.log_model(registered_model_name=name,
                                  pytorch_model=checkpoint,

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