improve readme

Author: Eyal-Danieli

Makefile

@@ -7,7 +7,7 @@ help: ## Show this help message
 	@awk 'BEGIN {FS = ":.*?## "} /^[a-zA-Z_-]+:.*?## / {printf "  %-15s %s\n", $$1, $$2}' $(MAKEFILE_LIST)
 
 sync: ## Sync dependencies from lockfile
-	uv sync --prerelease=allow
+	uv sync
 
 format: ## Format code with black and isort
 	uv run black .

README.md

@@ -1,4 +1,4 @@
-# MLRun Functions Hub
+# MLRun Hub
 
 A centralized repository for open-source MLRun functions, modules, and steps that can be used as reusable components in ML pipelines.
 
@@ -20,7 +20,7 @@ A centralized repository for open-source MLRun functions, modules, and steps tha
 
 Before you begin, ensure you have the following installed:
 
-- **Python 3.10 or 3.11** - Required
+- **Python 3.10 or 3.11 (recommended) ** - Required
 - **UV** - Fast Python package manager (required)
 - **Git** - For version control
 - **Make** (optional) - For convenient command shortcuts
@@ -317,7 +317,6 @@ We follow **PEP 8** style guidelines with some modifications:
 
 - **Line length**: 88 characters (Black default)
 - **Imports**: Sorted with isort
-- **Docstrings**: Google style or NumPy style
 - **Type hints**: Encouraged for function signatures
 
 ### Formatting Tools
@@ -351,29 +350,78 @@ uv run isort --check-only .
 
 ### Documentation Standards
 
-- **Docstrings are mandatory** for all public functions, classes, and modules
+- **Docstrings are mandatory** for all public hub items
 - Use clear, concise descriptions
 - Include parameter types and return types
 - Provide usage examples when helpful
 
-**Example:**
+**Example (function `auto_trainer`):**
 ```python
-def train_model(data: pd.DataFrame, target_column: str, model_type: str = "sklearn") -> dict:
+def train(
+    context: MLClientCtx,
+    dataset: DataItem,
+    model_class: str,
+    label_columns: Optional[Union[str, List[str]]] = None,
+    drop_columns: List[str] = None,
+    model_name: str = "model",
+    tag: str = "",
+    sample_set: DataItem = None,
+    test_set: DataItem = None,
+    train_test_split_size: float = None,
+    random_state: int = None,
+    labels: dict = None,
+    **kwargs,
+):
     """
-    Train a machine learning model on the provided dataset.
-    
-    Args:
-        data: Input DataFrame containing features and target
-        target_column: Name of the target column
-        model_type: Type of model to train (default: "sklearn")
-    
-    Returns:
-        Dictionary containing the trained model and metrics
-        
-    Example:
-        >>> result = train_model(df, "label", "sklearn")
-        >>> print(result["accuracy"])
-        0.95
+    Training a model with the given dataset.
+
+    example::
+
+        import mlrun
+        project = mlrun.get_or_create_project("my-project")
+        project.set_function("hub://auto_trainer", "train")
+        trainer_run = project.run(
+            name="train",
+            handler="train",
+            inputs={"dataset": "./path/to/dataset.csv"},
+            params={
+                "model_class": "sklearn.linear_model.LogisticRegression",
+                "label_columns": "label",
+                "drop_columns": "id",
+                "model_name": "my-model",
+                "tag": "v1.0.0",
+                "sample_set": "./path/to/sample_set.csv",
+                "test_set": "./path/to/test_set.csv",
+                "CLASS_solver": "liblinear",
+            },
+        )
+
+    :param context:                 MLRun context
+    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
+    :param model_class:             The class of the model, e.g. `sklearn.linear_model.LogisticRegression`
+    :param label_columns:           The target label(s) of the column(s) in the dataset. for Regression or
+                                    Classification tasks. Mandatory when dataset is not a FeatureVector.
+    :param drop_columns:            str or a list of strings that represent the columns to drop
+    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
+    :param tag:                     The model's tag to log with
+    :param sample_set:              A sample set of inputs for the model for logging its stats along the model in favour
+                                    of model monitoring. Can be either a URI or a FeatureVector
+    :param test_set:                The test set to train the model with.
+    :param train_test_split_size:   if test_set was provided then this argument is ignored.
+                                    Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
+                                    in the test split. The size of the Training set is set to the complement of this
+                                    value. Default = 0.2
+    :param random_state:            Relevant only when using train_test_split_size.
+                                    A random state seed to shuffle the data. For more information, see:
+                                    https://scikit-learn.org/stable/glossary.html#term-random_state
+                                    Notice that here we only pass integer values.
+    :param labels:                  Labels to log with the model
+    :param kwargs:                  Here you can pass keyword arguments with prefixes,
+                                    that will be parsed and passed to the relevant function, by the following prefixes:
+                                    - `CLASS_` - for the model class arguments
+                                    - `FIT_` - for the `fit` function arguments
+                                    - `TRAIN_` - for the `train` function (in xgb or lgbm train function - future)
+
     """
     # Implementation here
 ```
@@ -392,10 +440,8 @@ def train_model(data: pd.DataFrame, target_column: str, model_type: str = "sklea
 pwd  # Should be the project root
 
 # Ensure dependencies are installed
-make install
+make sync
 
-# Try running with full path
-python -m cli.cli --help
 ```
 
 #### Tests Failing
@@ -404,7 +450,7 @@ python -m cli.cli --help
 
 **Solution:**
 ```bash
-# Install test dependencies if the function has a requirements.txt
+# Install test dependencies if the item has a requirements.txt
 cd functions/src/your_function
 uv pip install -r requirements.txt
 

pyproject.toml

@@ -4,20 +4,11 @@ version = "0.1.0"
 description = "MLRun Hub - centralized location for open source contributions of mlrun hub components"
 readme = "README.md"
 requires-python = ">=3.10,<3.12"
-license = { text = "Apache-2.0" }
+license = { file = "LICENSE" }
 authors = [
     { name = "MLRun Team" }
 ]
-keywords = ["mlrun", "machine-learning", "serverless", "ml-ops"]
-classifiers = [
-    "Development Status :: 4 - Beta",
-    "Intended Audience :: Developers",
-    "License :: OSI Approved :: Apache Software License",
-    "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
-    "Topic :: Software Development :: Libraries",
-]
+keywords = ["mlrun", "marketplace"]
 
 dependencies = [
     "wheel",
@@ -39,82 +30,9 @@ dependencies = [
     "sphinxcontrib-qthelp==1.0.7",
 ]
 
-[project.optional-dependencies]
-dev = [
-    "urllib3>=1.26.20",
-    "v3io-frames>=0.10.15",
-    "GitPython~=3.1, >=3.1.41",
-    "aiohttp~=3.11",
-    "aiohttp-retry~=2.9",
-    "click~=8.1",
-    "nest-asyncio~=1.0",
-    "ipython~=8.10",
-    "nuclio-jupyter~=0.11.2",
-    "numpy>=1.26.4, <1.27.0",
-    "pandas>=1.2, <2.2",
-    "pyarrow>=10.0, <18",
-    "pyyaml>=6.0.2, <7",
-    "requests~=2.32",
-    "tabulate~=0.8.6",
-    "v3io~=0.7.0",
-    "pydantic>=1.10.15,<2",
-    "protobuf>=3.13.0,<4",
-    "mergedeep~=1.3",
-    "semver~=3.0",
-    "dependency-injector~=4.41",
-    "fsspec>=2025.5.1, <=2025.7.0",
-    "v3iofs~=0.1.17",
-    "storey~=1.10.13",
-    "inflection~=0.5.0",
-    "python-dotenv~=1.0",
-    "setuptools>=75.2",
-    "deprecated~=1.2",
-    "jinja2~=3.1, >=3.1.6",
-    "orjson>=3.9.15, <4",
-    "mlrun-pipelines-kfp-common~=0.5.8",
-    "mlrun-pipelines-kfp-v1-8~=0.5.7",
-    "docstring_parser~=0.16",
-    "aiosmtplib~=3.0",
-    "deepdiff>=8.6.1,<9.0.0",
-]
-
 [project.scripts]
 mlrun-functions = "cli.cli:cli"
 
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[dependency-groups]
-dev = [
-    "urllib3>=1.26.20",
-    "v3io-frames>=0.10.15",
-    "GitPython~=3.1, >=3.1.41",
-    "aiohttp~=3.11",
-    "aiohttp-retry~=2.9",
-    "click~=8.1",
-    "nest-asyncio~=1.0",
-    "ipython~=8.10",
-    "nuclio-jupyter~=0.11.2",
-    "numpy>=1.26.4, <1.27.0",
-    "pandas>=1.2, <2.2",
-    "pyarrow>=10.0, <18",
-    "requests~=2.32",
-    "tabulate~=0.8.6",
-    "python-dotenv~=1.0",
-    "setuptools>=75.2",
-    "deprecated~=1.2",
-    "orjson>=3.9.15, <4",
-    "docstring_parser~=0.16",
-    "aiosmtplib~=3.0",
-    "deepdiff>=8.6.1,<9.0.0",
-    # The following dependencies will be pulled by mlrun with correct versions
-    # to avoid conflicts, we let mlrun manage them:
-    # pyyaml, semver, v3io, pydantic, protobuf, mergedeep,
-    # fsspec, v3iofs, storey, inflection, jinja2,
-    # mlrun-pipelines-kfp-common, mlrun-pipelines-kfp-v1-8
-]
-
 [tool.black]
 line-length = 88
 target-version = ["py310", "py311"]
@@ -128,7 +46,3 @@ include_trailing_comma = true
 force_grid_wrap = 0
 use_parentheses = true
 ensure_newline_before_comments = true
-
-[tool.hatch.build.targets.wheel]
-packages = ["cli", "functions", "modules", "steps"]
-