Cherry Pick from Development (#973)

* Update scikit-learn to version 1.5    (#967)

* updated scikit-learn~=1.5
fixes and patches for new scikit-learn version
changes in item.yaml and regenerate function.yaml

* remove filename

* remove numpy import

* revert sklearn.metrics monkey patch
fix _get_dataframe to handle list/dict before accessing artifact_url
added feature name preservation logic in predict function

* revert mlrun version

* revert get_or_create_project

* revert scikit-learn version

* scikit-learn==1.5.2
mlrun v 1.10

* scikit-learn==1.4.2

* revert scikit-learn<1.4.0

* scikit-learn~=1.5

* mlrun 1.10 with scikit-learn<1.4.0

* scikit-learn strict v~=1.5.2
added skip for test_train in test_auto_trainer.py

* revert sklearn_classifier.py changes
change XGBRegressor to LGBMRegressor

* added xgboost.XGBRegressor, xgboost.XGBClassifier and lightgbm.LGBMClassifier models to test

* [module][langchain_mlrun] First version (#963)

* [Build] Fix html links, Add <function>.html as source in documentation

* Update CI temporarily and update index

* [XGB-Custom] Fix test artifact key name

* [XGB-Serving][XGB-Test][XGB-Trainer] Fix tests - artifact key

* [Build] Install python 3.9 when testing (#618)

* [Build] Update python version in CI (#620)

* [Build] Install python 3.9 when testing

* [Build] Update python version in CI

* .

* Revert "[Build] Update python version in CI (#620)" (#621)

This reverts commit 0cd1f15.

* Revert "[Build] Install python 3.9 when testing (#618)" (#619)

This reverts commit 3301415.

* [Build] Build with python 3.9 (#622)

* [Build] Build with python 3.9

* .

* [onnx utils] update onnx utils packages

* [Noise-reduction] Add new function to hub (#765)

* [Noise-reduction] Add new function to hub

* fix test

* added multiprocessing and silence removal to function

* delete `load_dask` (#822)

* [feature selection] update function yaml

* [feature selection] update function yaml

* Revert "[onnx utils] update onnx utils packages"

This reverts commit 8872798.

* [feature selection] update function yaml

* [feature selection] update function yaml

* Delete unsupported functions from the hub (#824)

* delete EOS functions

* bring back validate_great_expectations

* bring back load_dataset

* Update feature_selection/test_feature_selection.py

Co-authored-by: Eyal Danieli <eyald@iguazio.com>

* Update item.yaml

* Align to master branch (#826)

* [Category] Fix and add categories to functions (#808)

* [Category] Fix and add categories to functions

* bump version in structured

* test is not valid in huggingface_serving

* Fix duplicated footer

* Fix duplicated footer

* revert python version change as it will be done in another PR

* comments

* comments

* Bump python:3.6 to python:3.9 (#810)

* [Describe] Align describe to new pandas version (#812)

* [Describe] Align describe to new pandas version

* minor test fix

* update mlrun version

* add dask to requirements

* remove dask

* update numpy version

* debug

* debug

* debug

* remove dask tests

* remove debug code

* [get_offline_features] Updated to mlrun 1.6.3 (#813)

* [Feature-selection] Replace matplotlib with plotly (#815)

* Iguazio-cicd user token updated

Iguazio-cicd user token updated in repo secrets:
https://github.com/mlrun/functions/settings/secrets/actions
MARKETPLACE_ACCESS_TOKEN_V3
new token gh...Zmf was set around April

* forcing iguazio-cicd auth

forcing iguazio-cicd to deal with Author identity unknown

* checkout@v3 to v4 and echo

* [Mlflow_utils] - mlflow model server (#811)

* mlflow server

* small fix to test

* small fixes to ms and nb

* small fixes to mlrun version

* update requirements lightgbm

* added req

* Added xgboost to req

---------

Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com>

* [Mlflow] Remove mlflow tag  (#825)

* remove mlflow tag

* remove mlflow tag

---------

Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com>

* align feature_selection yaml

---------

Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com>
Co-authored-by: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com>
Co-authored-by: rokatyy <ekaterina_molchanova@mckinsey.com>
Co-authored-by: Katerina Molchanova <35141662+rokatyy@users.noreply.github.com>
Co-authored-by: nashpaz123 <44337075+nashpaz123@users.noreply.github.com>
Co-authored-by: ZeevRispler <73653682+ZeevRispler@users.noreply.github.com>

* set `navigation_with_keys` to False (#829)

* remove xgb and churn functions (#830)

* [Batch Infer V2] Adjust function to 1.7 (#832)

* adjust batch infer v2

* update docs in NB

* bring back deprecated params and add warn (#834)

* fix PyYAML loading (#837)

* [text to audio generator] Replaced bark with openai tts models (#836)

* [Text to audio generator] Add speech engine (#838)

* [text to audio generator] Replaced bark with openai tts models

* [text to audio generator] Fix base url env var

* fix version

* Add speech engine

* after review

* [auto-trainer] update test requirements (#839)

* [Build] Fix html links, Add <function>.html as source in documentation

* Update CI temporarily and update index

* [XGB-Custom] Fix test artifact key name

* [XGB-Serving][XGB-Test][XGB-Trainer] Fix tests - artifact key

* [Build] Install python 3.9 when testing (#618)

* [Build] Update python version in CI (#620)

* [Build] Install python 3.9 when testing

* [Build] Update python version in CI

* .

* Revert "[Build] Update python version in CI (#620)" (#621)

This reverts commit 0cd1f15.

* Revert "[Build] Install python 3.9 when testing (#618)" (#619)

This reverts commit 3301415.

* [Build] Build with python 3.9 (#622)

* [Build] Build with python 3.9

* .

* Update requirements.txt

* [Feature Selection] Fix deprecated `get_offline_features`  (#844)

* fix feature_selection

* fix feature_selection

* fix feature_selection nb

* update yaml name

* fix test

* fix test

* limit torchaudio for unit test (#845)

* Update requirements.txt (#843)

* [Open Archive] Fix arbitrary file vulnerability  (#847)

* fix arbitrary file vulnerability

* fix arbitrary file vulnerability

* fix test

* [open_archive] Add traversal attack test  (#849)

* add traversal test

* add traversal test

* add traversal test

* first version

* run upg to pydantic v2

* added kafka and mlrun-ce code preparation

* Eyal review

* LangChain MLRun Integration with Kafka Support (CE Mode) (#1)

* docstring syntax fixes

* Add initial Kafka support for MLRun CE
Implement _KafkaMLRunEndPointClient with KafkaProducer
Add kafka_broker and kafka_topic to MLRunTracerClientSettings
Add Kafka parameters to setup_langchain_monitoring()
Update notebook to auto-detect CE/Enterprise mode
Add kafka-python, orjson, uuid-utils to requirements.txt

* added KAFKA_BROKER and DatastoreProfileTDEngine for registering kafka and tsdb profiles

* clear output

* use get_kafka_topic for the project name
remove "raises:" docstring
added kafka flush
added s3fs to requirements.txt

* adaptive notebook for local execution
- AWS_ENDPOINT_URL_S3 env variable in deployment
- port forwarding scripts

* Replace kafka_broker/kafka_topic with stream_profile_name
 - Update _KafkaMLRunEndPointClient to use DatastoreProfileKafkaStream
 - Fetch Kafka config (broker, topic, SASL, SSL) from registered profile
 - Auto-retrieve stream_profile_name from model monitoring credentials
 - Update MLRunTracerClientSettings with new stream_profile_name field
 - Update setup_langchain_monitoring() to use profile-based config
 - Update notebook to use simplified API

* Add get_kafka_stream_profile_name() utility to auto-detect Kafka profile

* Move dependencies from requirements.txt to item.yaml

* generalize env variables

* Deploy monitoring app once instead of redeploy
Unify profile variable naming for CE and Enterprise modes

* _KafkaMLRunEndPointClient constructor - keep project handling NONE able with parent class handling in

* revert removal of raise docstring
enforce usage of stream_profile_name

* change stream_profile_name to kafka_stream_profile_name
revert ValueError message

* Revert ValueError message
Added mechanism to flush stream upon root run (instead of each monitor call)

* ensure self._mlrun_client is not None before flush

* revert double deploy

* Add configurable kafka_linger_ms for hybrid flush control

Adds kafka_linger_ms parameter to control message delivery timing:
 - Explicit flush mode (linger_ms=0, default): flush after each root run
 - Kafka-managed mode (linger_ms>0): Kafka controls delivery timing

The flush() method now handles the mode internally - it's a no-op when Kafka-managed mode is enabled, keeping the tracer code simple.

* fix double deploy in notebook (set the in-cluster MinIO endpoint before deploying)

* Simplify Kafka flush behavior and enable batching by default
- Always flush at end of root run (removed conditional linger_ms check)
- Set default kafka_linger_ms to 500ms for message batching
- Simplify KafkaProducer initialization (pop bootstrap_servers instead of lambda)

* remove local module import from notebook
remove redundant requirements from item.yaml

* move env variables setup to top of the notebook

* renamed env vars

* fix test

* gilad review

---------

Co-authored-by: yonishelach <yonatanshelach@gmail.com>
Co-authored-by: Yoni Shelach <92271540+yonishelach@users.noreply.github.com>
Co-authored-by: Avi Asulin <avia@iguazio.com>
Co-authored-by: Eyal Danieli <eyald@iguazio.com>
Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com>
Co-authored-by: rokatyy <ekaterina_molchanova@mckinsey.com>
Co-authored-by: Katerina Molchanova <35141662+rokatyy@users.noreply.github.com>
Co-authored-by: nashpaz123 <44337075+nashpaz123@users.noreply.github.com>
Co-authored-by: ZeevRispler <73653682+ZeevRispler@users.noreply.github.com>
Co-authored-by: daniels290813 <78727943+daniels290813@users.noreply.github.com>
Co-authored-by: Tomer Weitzman <81749152+tomerbv@users.noreply.github.com>

* [onnx_utils] updated pytorch due to security vulnerability (#968)

* Update onnx_utils dependencies and improve test robustness

  - Upgrade PyTorch (2.6.0 → 2.8.0) and TorchVision (0.21.0 → 0.23.0) for
    better compatibility and performance
  - Update MLRun version requirement to 1.10.0 in item.yaml
  - Bump function version to 1.4.0

  Test improvements:
  - Add environment variable validation (MLRUN_DBPATH, MLRUN_ARTIFACT_PATH)
  - Add conditional test skipping based on tf2onnx availability
  - Fix cleanup function to properly remove test artifacts (model.pt,
    model_modules_map.json, onnx_model.onnx, etc.)
  - Update deprecated artifact_path parameter to output_path
  - Add explicit project context to all MLRun function calls
  - Fix PyTorch test artifact path construction

* Add conftest fixture for test environment and update notebook to PyTorch demo

- Centralize test setup/cleanup in conftest autouse fixture
- Rewrite notebook demo from Keras to a working PyTorch MobileNetV2 example

* deleted iguazio credentials

* Remove conftest.py and inline fixtures into test_onnx_utils.py

Move onnx_project and test_environment fixtures directly into the test
file to reduce unnecessary indirection for a single test module.

---------

Co-authored-by: Tomer Weitzman <81749152+tomerbv@users.noreply.github.com>
Co-authored-by: guy1992l <83535508+guy1992l@users.noreply.github.com>
Co-authored-by: yonishelach <yonatanshelach@gmail.com>
Co-authored-by: Yoni Shelach <92271540+yonishelach@users.noreply.github.com>
Co-authored-by: Avi Asulin <avia@iguazio.com>
Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com>
Co-authored-by: rokatyy <ekaterina_molchanova@mckinsey.com>
Co-authored-by: Katerina Molchanova <35141662+rokatyy@users.noreply.github.com>
Co-authored-by: nashpaz123 <44337075+nashpaz123@users.noreply.github.com>
Co-authored-by: ZeevRispler <73653682+ZeevRispler@users.noreply.github.com>
Co-authored-by: daniels290813 <78727943+daniels290813@users.noreply.github.com>
Co-authored-by: Omer Mimon <81911093+omermaim@users.noreply.github.com>

Author: 10 people

functions/src/auto_trainer/auto_trainer.py

@@ -67,30 +67,14 @@ def _get_dataframe(
                             Classification tasks.
     :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
     """
-    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)
-
-    # Getting the dataset:
-    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
-        label_columns = label_columns or dataset.meta.status.label_column
-        context.logger.info(f"label columns: {label_columns}")
-        # FeatureVector case:
-        try:
-            fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
-            dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
-        except AttributeError:
-            # Leave here for backwards compatibility
-            dataset = fs.get_offline_features(
-                dataset.meta.uri, drop_columns=drop_columns
-            ).to_dataframe()
-
-    elif not label_columns:
-        context.logger.info(
-            "label_columns not provided, mandatory when dataset is not a FeatureVector"
-        )
-        raise ValueError
-
-    elif isinstance(dataset, (list, dict)):
+    # Check if dataset is list/dict first (before trying to access artifact_url)
+    if isinstance(dataset, (list, dict)):
         # list/dict case:
+        if not label_columns:
+            context.logger.info(
+                "label_columns not provided, mandatory when dataset is not a FeatureVector"
+            )
+            raise ValueError
         dataset = pd.DataFrame(dataset)
         # Checking if drop_columns provided by integer type:
         if drop_columns:
@@ -103,17 +87,38 @@ def _get_dataframe(
                 )
                 raise ValueError
             dataset.drop(drop_columns, axis=1, inplace=True)
-
     else:
-        # simple URL case:
-        dataset = dataset.as_df()
-        if drop_columns:
-            if all(col in dataset for col in drop_columns):
-                dataset = dataset.drop(drop_columns, axis=1)
-            else:
+        # Dataset is a DataItem with artifact_url (URI or FeatureVector)
+        store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)
+
+        # Getting the dataset:
+        if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
+            label_columns = label_columns or dataset.meta.status.label_column
+            context.logger.info(f"label columns: {label_columns}")
+            # FeatureVector case:
+            try:
+                fv = mlrun.datastore.get_store_resource(dataset.artifact_url)
+                dataset = fv.get_offline_features(drop_columns=drop_columns).to_dataframe()
+            except AttributeError:
+                # Leave here for backwards compatibility
+                dataset = fs.get_offline_features(
+                    dataset.meta.uri, drop_columns=drop_columns
+                ).to_dataframe()
+        else:
+            # simple URL case:
+            if not label_columns:
                 context.logger.info(
-                    "not all of the columns to drop in the dataset, drop columns process skipped"
+                    "label_columns not provided, mandatory when dataset is not a FeatureVector"
                 )
+                raise ValueError
+            dataset = dataset.as_df()
+            if drop_columns:
+                if all(col in dataset for col in drop_columns):
+                    dataset = dataset.drop(drop_columns, axis=1)
+                else:
+                    context.logger.info(
+                        "not all of the columns to drop in the dataset, drop columns process skipped"
+                    )
 
     return dataset, label_columns
 

functions/src/auto_trainer/function.yaml

@@ -13,7 +13,7 @@ labels:
   author: Iguazio
 maintainers: []
 marketplaceType: ''
-mlrunVersion: 1.7.0
+mlrunVersion: 1.10.0
 name: auto_trainer
 platformVersion: 3.5.0
 spec:
@@ -23,4 +23,4 @@ spec:
   kind: job
   requirements: []
 url: ''
-version: 1.8.0
+version: 1.9.0

functions/src/auto_trainer/item.yaml

@@ -1,4 +1,5 @@
 pandas
-scikit-learn<1.4.0
+scikit-learn~=1.5.2
+lightgbm
 xgboost<2.0.0
 plotly

functions/src/auto_trainer/requirements.txt

@@ -29,6 +29,9 @@
     ("sklearn.linear_model.LinearRegression", "regression"),
     ("sklearn.ensemble.RandomForestClassifier", "classification"),
     ("xgboost.XGBRegressor", "regression"),
+    ("xgboost.XGBClassifier", "classification"),
+    ("lightgbm.LGBMRegressor", "regression"),
+    ("lightgbm.LGBMClassifier", "classification")
 ]
 
 REQUIRED_ENV_VARS = [
@@ -78,11 +81,15 @@ def _assert_train_handler(train_run):
 
 
 @pytest.mark.parametrize("model", MODELS)
+@pytest.mark.skipif(
+    condition=not _validate_environment_variables(),
+    reason="Project's environment variables are not set",
+)
 def test_train(model: Tuple[str, str]):
     dataset, label_columns = _get_dataset(model[1])
     is_test_passed = True
 
-    project = mlrun.new_project("auto-trainer-test", context="./")
+    project = mlrun.get_or_create_project("auto-trainer-test", context="./")
     fn = project.set_function("function.yaml", "train", kind="job", image="mlrun/mlrun")
 
     train_run = None
@@ -119,7 +126,7 @@ def test_train_evaluate(model: Tuple[str, str]):
     dataset, label_columns = _get_dataset(model[1])
     is_test_passed = True
     # Importing function:
-    project = mlrun.new_project("auto-trainer-test", context="./")
+    project = mlrun.get_or_create_project("auto-trainer-test", context="./")
     fn = project.set_function("function.yaml", "train", kind="job", image="mlrun/mlrun")
     temp_dir = tempfile.mkdtemp()
 
@@ -172,7 +179,7 @@ def test_train_predict(model: Tuple[str, str]):
     df = pd.read_csv(dataset)
     sample = df.head().drop("labels", axis=1).values.tolist()
     # Importing function:
-    project = mlrun.new_project("auto-trainer-test", context="./")
+    project = mlrun.get_or_create_project("auto-trainer-test", context="./")
     fn = project.set_function("function.yaml", "train", kind="job", image="mlrun/mlrun")
     temp_dir = tempfile.mkdtemp()
 

functions/src/auto_trainer/test_auto_trainer.py

@@ -11,7 +11,7 @@ labels:
   author: Iguazio
 maintainers: []
 marketplaceType: ''
-mlrunVersion: 1.7.0
+mlrunVersion: 1.10.0
 name: describe
 platformVersion: 3.5.3
 spec:
@@ -21,4 +21,4 @@ spec:
   kind: job
   requirements: []
 url: ''
-version: 1.4.0
+version: 1.5.0

functions/src/describe/function.yaml

@@ -1,4 +1,4 @@
-scikit-learn~=1.0.2
+scikit-learn~=1.5.2
 plotly~=5.23
 pytest~=7.0.1
 matplotlib~=3.5.1

functions/src/describe/item.yaml

@@ -1,13 +1,15 @@
 metadata:
-  categories:
-  - data-generation
   tag: ''
   name: gen-class-data
+  categories:
+  - data-generation
+verbose: false
 spec:
   description: Create a binary classification sample dataset and save.
-  default_handler: gen_class_data
   entry_points:
     gen_class_data:
+      lineno: 22
+      has_varargs: false
       has_kwargs: false
       parameters:
       - name: context
@@ -48,7 +50,6 @@ spec:
       - name: sk_params
         doc: additional parameters for `sklearn.datasets.make_classification`
         default: {}
-      lineno: 22
       doc: 'Create a binary classification sample dataset and save.
 
         If no filename is given it will default to:
@@ -59,14 +60,13 @@ spec:
         Additional scikit-learn parameters can be set using **sk_params, please see
         https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html
         for more details.'
-      has_varargs: false
       name: gen_class_data
-  command: ''
-  disable_auto_mount: false
-  image: mlrun/mlrun
   build:
     origin_filename: ''
     functionSourceCode: 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
     code_origin: ''
+  command: ''
+  image: mlrun/mlrun
+  default_handler: gen_class_data
+  disable_auto_mount: false
 kind: job
-verbose: false

functions/src/describe/requirements.txt

@@ -11,7 +11,7 @@ labels:
   author: Iguazio
 maintainers: []
 marketplaceType: ''
-mlrunVersion: 1.7.0
+mlrunVersion: 1.10.0
 name: gen_class_data
 platformVersion: 3.5.3
 spec:
@@ -21,4 +21,4 @@ spec:
   kind: job
   requirements: []
 url: ''
-version: 1.3.0
+version: 1.4.0