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Then, fill in all the relevant details. For example: `kind` (either `nuclio:serving`, `serving` or `job`) and `categories` field (you can browse the [MLRun hub UI](https://www.mlrun.org/hub/functions/) to see existing categories. You can specify more than one category per function).
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Important: Be consistent with the module name across the directory name, all relevant `item.yaml` fields, and the file names.
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#### function.yaml
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The MLRun function definition. Can be generated from `item.yaml` using:
The main code file for your function. (Notice: keep the code well-documented, the docstrings are used in the hub UI as documentation for the function.)
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#### your_function_name.ipynb
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A Jupyter notebook demonstrating the function's usage. (Notice: the notebook must be able to run end-to-end automatically without manual intervention.)
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#### test_your_function_name.py
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Unit tests for your function to cover the function functionality as much as possible. (Will run upon each change to your function).
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#### requirements.txt
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Any additional Python dependencies required by your function's unit tests. (Notice: The function's own dependencies should be specified in the `item.yaml` file, not here.)
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### Modules
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```txt
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modules
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├── src
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│ ├── your_module_name
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│ │ ├── item.yaml
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│ │ ├── your_module_name.py
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│ │ ├── your_module_name.ipynb
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│ │ ├── test_your_module_name.py
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│ │ └── requirements.txt
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```
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#### item.yaml
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Metadata about the module. Can be generated using the following CLI command:
Then, fill in all the relevant details. For example: `kind` (either `generic` or `monitoring_application`) and `categories` (you can browse the [MLRun hub UI](https://www.mlrun.org/hub/functions/) to see existing categories. You can specify more than one category per module).
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Important: Be consistent with the module name across the directory name, all relevant `item.yaml` fields, and the file names.
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#### your_module_name.py
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The main code file for your module. (Notice: keep the code well-documented, the docstrings are used in the hub UI as documentation for the module.)
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For model-monitoring modules, you can see our [guidelines for writing model monitoring applications](https://docs.mlrun.org/en/stable/model-monitoring/applications.html).
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#### your_module_name.ipynb
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A Jupyter notebook demonstrating the module's usage.
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#### test_your_module_name.py
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Unit tests for your module to cover the module functionality as much as possible. (Will run upon each change to your module).
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#### requirements.txt
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Any additional Python dependencies required by your module's unit tests. (Notice: The module's own dependencies should be specified in the `item.yaml` file, not here.)
Copy file name to clipboardExpand all lines: functions/README.md
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<!-- AUTOGEN:START (do not edit below) -->
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| Name | Description | Kind | Categories |
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| --- | --- | --- | --- |
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|[aggregate](https://github.com/mlrun/functions/tree/master/functions/src/aggregate)| Rolling aggregation over Metrics and Lables according to specifications | job | data-preparation |
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|[arc_to_parquet](https://github.com/mlrun/functions/tree/master/functions/src/arc_to_parquet)| retrieve remote archive, open and save as parquet | job | utils |
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|[auto_trainer](https://github.com/mlrun/functions/tree/master/functions/src/auto_trainer)| Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM. | job | machine-learning, model-training |
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|[azureml_serving](https://github.com/mlrun/functions/tree/master/functions/src/azureml_serving)| AzureML serving function | serving | machine-learning, model-serving |
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|[azureml_utils](https://github.com/mlrun/functions/tree/master/functions/src/azureml_utils)| Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom. | job | model-serving, utils |
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|[batch_inference](https://github.com/mlrun/functions/tree/master/functions/src/batch_inference)| Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
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|[batch_inference_v2](https://github.com/mlrun/functions/tree/master/functions/src/batch_inference_v2)| Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
|[feature_selection](https://github.com/mlrun/functions/tree/master/functions/src/feature_selection)| Select features through multiple Statistical and Model filters | job | data-preparation, machine-learning |
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|[gen_class_data](https://github.com/mlrun/functions/tree/master/functions/src/gen_class_data)| Create a binary classification sample dataset and save. | job | data-generation |
|[hugging_face_serving](https://github.com/mlrun/functions/tree/master/functions/src/hugging_face_serving)| Generic Hugging Face model server. | serving | genai, model-serving |
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|[load_dataset](https://github.com/mlrun/functions/tree/master/functions/src/load_dataset)| load a toy dataset from scikit-learn | job | data-preparation |
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|[mlflow_utils](https://github.com/mlrun/functions/tree/master/functions/src/mlflow_utils)| Mlflow model server, and additional utils. | serving | model-serving, utils |
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|[model_server](https://github.com/mlrun/functions/tree/master/functions/src/model_server)| generic sklearn model server | nuclio:serving| model-serving, machine-learning |
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|[model_server_tester](https://github.com/mlrun/functions/tree/master/functions/src/model_server_tester)| test model servers | job | monitoring, model-serving |
|[onnx_utils](https://github.com/mlrun/functions/tree/master/functions/src/onnx_utils)| ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. | job | utils, deep-learning |
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|[open_archive](https://github.com/mlrun/functions/tree/master/functions/src/open_archive)| Open a file/object archive into a target directory | job | utils |
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|[pii_recognizer](https://github.com/mlrun/functions/tree/master/functions/src/pii_recognizer)| This function is used to recognize PII in a directory of text files | job | data-preparation, NLP |
|[question_answering](https://github.com/mlrun/functions/tree/master/functions/src/question_answering)| GenAI approach of question answering on a given data | job | genai |
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|[send_email](https://github.com/mlrun/functions/tree/master/functions/src/send_email)| Send Email messages through SMTP server | job | utils |
|[sklearn_classifier](https://github.com/mlrun/functions/tree/master/functions/src/sklearn_classifier)| train any classifier using scikit-learn's API | job | machine-learning, model-training |
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|[sklearn_classifier_dask](https://github.com/mlrun/functions/tree/master/functions/src/sklearn_classifier_dask)| train any classifier using scikit-learn's API over Dask | job | machine-learning, model-training |
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|[structured_data_generator](https://github.com/mlrun/functions/tree/master/functions/src/structured_data_generator)| GenAI approach of generating structured data according to a given schema | job | data-generation, genai |
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|[test_classifier](https://github.com/mlrun/functions/tree/master/functions/src/test_classifier)| test a classifier using held-out or new data | job | machine-learning, model-testing |
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|[text_to_audio_generator](https://github.com/mlrun/functions/tree/master/functions/src/text_to_audio_generator)| Generate audio file from text using different speakers | job | data-generation, audio |
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|[tf2_serving](https://github.com/mlrun/functions/tree/master/functions/src/tf2_serving)| tf2 image classification server | nuclio:serving| model-serving, machine-learning |
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|[transcribe](https://github.com/mlrun/functions/tree/master/functions/src/transcribe)| Transcribe audio files into text files | job | audio, genai |
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|[translate](https://github.com/mlrun/functions/tree/master/functions/src/translate)| Translate text files from one language to another | job | genai, NLP |
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|[v2_model_server](https://github.com/mlrun/functions/tree/master/functions/src/v2_model_server)| generic sklearn model server | serving | model-serving, machine-learning |
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|[v2_model_tester](https://github.com/mlrun/functions/tree/master/functions/src/v2_model_tester)| test v2 model servers | job | model-testing, machine-learning |
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|[aggregate](https://github.com/mlrun/functions/tree/development/functions/src/aggregate)| Rolling aggregation over Metrics and Lables according to specifications | job | data-preparation |
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|[arc_to_parquet](https://github.com/mlrun/functions/tree/development/functions/src/arc_to_parquet)| retrieve remote archive, open and save as parquet | job | utils |
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|[auto_trainer](https://github.com/mlrun/functions/tree/development/functions/src/auto_trainer)| Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM. | job | machine-learning, model-training |
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|[azureml_serving](https://github.com/mlrun/functions/tree/development/functions/src/azureml_serving)| AzureML serving function | serving | machine-learning, model-serving |
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|[azureml_utils](https://github.com/mlrun/functions/tree/development/functions/src/azureml_utils)| Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom. | job | model-serving, utils |
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|[batch_inference](https://github.com/mlrun/functions/tree/development/functions/src/batch_inference)| Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
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|[batch_inference_v2](https://github.com/mlrun/functions/tree/development/functions/src/batch_inference_v2)| Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving |
|[feature_selection](https://github.com/mlrun/functions/tree/development/functions/src/feature_selection)| Select features through multiple Statistical and Model filters | job | data-preparation, machine-learning |
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|[gen_class_data](https://github.com/mlrun/functions/tree/development/functions/src/gen_class_data)| Create a binary classification sample dataset and save. | job | data-generation |
|[hugging_face_serving](https://github.com/mlrun/functions/tree/development/functions/src/hugging_face_serving)| Generic Hugging Face model server. | serving | genai, model-serving |
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|[load_dataset](https://github.com/mlrun/functions/tree/development/functions/src/load_dataset)| load a toy dataset from scikit-learn | job | data-preparation |
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|[mlflow_utils](https://github.com/mlrun/functions/tree/development/functions/src/mlflow_utils)| Mlflow model server, and additional utils. | serving | model-serving, utils |
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|[model_server](https://github.com/mlrun/functions/tree/development/functions/src/model_server)| generic sklearn model server | nuclio:serving| model-serving, machine-learning |
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|[model_server_tester](https://github.com/mlrun/functions/tree/development/functions/src/model_server_tester)| test model servers | job | monitoring, model-serving |
|[onnx_utils](https://github.com/mlrun/functions/tree/development/functions/src/onnx_utils)| ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. | job | utils, deep-learning |
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|[open_archive](https://github.com/mlrun/functions/tree/development/functions/src/open_archive)| Open a file/object archive into a target directory | job | utils |
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|[pii_recognizer](https://github.com/mlrun/functions/tree/development/functions/src/pii_recognizer)| This function is used to recognize PII in a directory of text files | job | data-preparation, NLP |
|[question_answering](https://github.com/mlrun/functions/tree/development/functions/src/question_answering)| GenAI approach of question answering on a given data | job | genai |
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|[send_email](https://github.com/mlrun/functions/tree/development/functions/src/send_email)| Send Email messages through SMTP server | job | utils |
|[sklearn_classifier](https://github.com/mlrun/functions/tree/development/functions/src/sklearn_classifier)| train any classifier using scikit-learn's API | job | machine-learning, model-training |
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|[sklearn_classifier_dask](https://github.com/mlrun/functions/tree/development/functions/src/sklearn_classifier_dask)| train any classifier using scikit-learn's API over Dask | job | machine-learning, model-training |
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|[structured_data_generator](https://github.com/mlrun/functions/tree/development/functions/src/structured_data_generator)| GenAI approach of generating structured data according to a given schema | job | data-generation, genai |
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|[test_classifier](https://github.com/mlrun/functions/tree/development/functions/src/test_classifier)| test a classifier using held-out or new data | job | machine-learning, model-testing |
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|[text_to_audio_generator](https://github.com/mlrun/functions/tree/development/functions/src/text_to_audio_generator)| Generate audio file from text using different speakers | job | data-generation, audio |
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|[tf2_serving](https://github.com/mlrun/functions/tree/development/functions/src/tf2_serving)| tf2 image classification server | nuclio:serving| model-serving, machine-learning |
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|[transcribe](https://github.com/mlrun/functions/tree/development/functions/src/transcribe)| Transcribe audio files into text files | job | audio, genai |
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|[translate](https://github.com/mlrun/functions/tree/development/functions/src/translate)| Translate text files from one language to another | job | genai, NLP |
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|[v2_model_server](https://github.com/mlrun/functions/tree/development/functions/src/v2_model_server)| generic sklearn model server | serving | model-serving, machine-learning |
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|[v2_model_tester](https://github.com/mlrun/functions/tree/development/functions/src/v2_model_tester)| test v2 model servers | job | model-testing, machine-learning |
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<!-- AUTOGEN:START (do not edit below) -->
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| Name | Description | Kind | Categories |
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| --- | --- | --- | --- |
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|[count_events](https://github.com/mlrun/functions/tree/master/modules/src/count_events)| Count events in each time window | monitoring_application | model-serving |
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|[evidently_iris](https://github.com/mlrun/functions/tree/master/modules/src/evidently_iris)| Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset | monitoring_application | model-serving, structured-ML |
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|[histogram_data_drift](https://github.com/mlrun/functions/tree/master/modules/src/histogram_data_drift)| Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML |
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|[openai_proxy_app](https://github.com/mlrun/functions/tree/master/modules/src/openai_proxy_app)| OpenAI application runtime based on fastapi | generic | genai |
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|[count_events](https://github.com/mlrun/functions/tree/development/modules/src/count_events)| Count events in each time window | monitoring_application | model-serving |
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|[evidently_iris](https://github.com/mlrun/functions/tree/development/modules/src/evidently_iris)| Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset | monitoring_application | model-serving, structured-ML |
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|[histogram_data_drift](https://github.com/mlrun/functions/tree/development/modules/src/histogram_data_drift)| Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML |
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|[openai_proxy_app](https://github.com/mlrun/functions/tree/development/modules/src/openai_proxy_app)| OpenAI application runtime based on fastapi | generic | genai |
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