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https://github.com/nxexox/pymlup

MLup is framework for easy and fast run ML in production
https://github.com/nxexox/pymlup

api asyncio fastapi framework json jupyter-notebook lightgbm machine-learning mlops mlup onnx pydantic pymlup pytorch rest tensorflow uvicorn web webapi

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MLup is framework for easy and fast run ML in production

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README 

        
![MLup logo](docs/assets/img/logo_with_name_blue.png?raw=true)



----



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## Introduction



MLup is library for easy and fast run ML in production. 



All you need is to deliver the model file and config to the server (in fact, the config is not necessary) 🙃



PyMLup is a modern way to run machine learning models in production. The market time has been reduced to a minimum. This library eliminates the need to write your own web applications with machine learning models and copy application code. It is enough to have a machine learning model to launch a web application with one command.



* It's library learning only clean python;

* Use FastApi in web app backend;



Work tested with machine learning model frameworks (links to tests):



* [scikit-learn>=1.2.0,<1.3.0](https://github.com/nxexox/pymlup/tree/main/tests/integration_tests/frameworks/test_scikit_learn_model.py)

* [tensorflow>=2.0.0,<3.0.0](https://github.com/nxexox/pymlup/tree/main/tests/integration_tests/frameworks/test_tensorflow_model.py)

* [lightgbm>=4.0.0,<5.0.0](https://github.com/nxexox/pymlup/tree/main/tests/integration_tests/frameworks/test_lightgbm_model.py)

* [torch>=2.0.0,<3.0.0](https://github.com/nxexox/pymlup/tree/main/tests/integration_tests/frameworks/test_pytorch_model.py)

* [onnx>=1.0.0,<2.0.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_binarization.py)

* [onnxruntime>=1.0.0,<2.0.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_binarization.py)



Support and tested with machine learning libraries:



* [numpy>=1.0.0,<2.0.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_data_transformers.py)

* [pandas>=2.0.0,<3.0.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_data_transformers.py)

* [joblib>=1.2.0,<1.3.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_binarization.py)

* [tf2onnx>=1.0.0,<2.0.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_binarization.py)

* [skl2onnx>=1.0.0,<2.0.0](https://github.com/nxexox/pymlup/tree/main/tests/unit_tests/ml/test_binarization.py)

* [jupyter==1.0.0](https://github.com/nxexox/pymlup/tree/main/tests/integration_tests/test_jupyter_notebook.py)



**The easiest way to try:**

```bash

pip install "pymlup[onnx]"

mlup run -m /path/to/my/model.onnx

```



## Useful links

* [Docs](https://github.com/nxexox/pymlup/tree/main/docs);

* [Examples](https://github.com/nxexox/pymlup/tree/main/examples);

* [Tests models](https://github.com/nxexox/pymlup/tree/main/mldata);



## How it's work



1. You are making your machine learning model. Optional: you are making mlup config for your model.

2. You deliver your model to server. Optional: you deliver your config to server.

3. Installing pymlup to your server and libraries for model.

4. Run web app from your model or your config 🙃



## Requirements



Python 3.7+



* PyMLup stands on the shoulders of giants FastAPI for the web parts. 

* Additionally, you need to install the libraries that your model uses.



## Installation



```bash

pip install pymlup

```



You will also can install with ml backend library:

```bash

pip install "pymlup[scikit-learn]"  # For scikit-learn

pip install "pymlup[lightgbm]"      # For microsoft lightgbm

pip install "pymlup[tensorflow]"    # For tensorflow

pip install "pymlup[torch]"         # For torch

pip install "pymlup[onnx]"          # For onnx models: torch, tensorflow, sklearn, etc...

```



## Examples



### Examples code



```python

import mlup



class MyAnyModelForExample:

    def predict(self, X):

        return X



ml_model = MyAnyModelForExample()



up = mlup.UP(ml_model=ml_model)

# Need call up.ml.load(), for analyze your model

up.ml.load()

# If you want testing your web app, you can run in daemon mode

# You can open browser http://localhost:8009/docs

up.run_web_app(daemon=True)



import requests

response = requests.post('http://0.0.0.0:8009/predict', json={'X': [[1, 2, 3], [4, 5, 6]]})

print(response.json())



up.stop_web_app()

```



You can check work model by config, without web application.

* `predict` - Get model predict as inner arguments as in web app.

* `predict_from` - As `predict` method, but not use data transformer before call model predict.

* `async_predict` - Asynchronous version of the `predict` method.

```python

import mlup

import numpy



class MyAnyModelForExample:

    def predict(self, X):

        return X



ml_model = MyAnyModelForExample()

up = mlup.UP(ml_model=ml_model)

up.ml.load()



up.predict(X=[[1, 2, 3], [4, 5, 6]])

up.predict_from(X=numpy.array([[1, 2, 3], [4, 5, 6]]))

await up.async_predict(X=[[1, 2, 3], [4, 5, 6]])

```



#### Save ready application to disk



##### Make default config



If path endswith to json, make json config, else yaml config.



```python

import mlup

mlup.generate_default_config('path_to_yaml_config.yaml')

```



##### From config



You can save ready config to disk, but you need set local storage and path to model file in server.

In folder can there are many files, mask need for filter exactly our model file



```python

import mlup

from mlup.ml.empty import EmptyModel  # This stub class

from mlup import constants



up = mlup.UP(ml_model=EmptyModel())

up.conf.storage_type = constants.StorageType.disk

up.conf.storage_kwargs = {

    'path_to_files': 'path/to/model/file/in/model_name.modelextension',

    'file_mask': 'model_name.modelextension',

}

up.to_yaml("path_to_yaml_config.yaml")

up.to_json("path_to_json_config.json")



# After in server

up = mlup.UP.load_from_yaml("path_to_yaml_config.yaml", load_model=True)

up.run_web_app()

```



##### From pickle



If you make pickle/joblib file your mlup with model, don't need to change storage type, because your model there is in your pickle/joblib file.



```python

import pickle

import mlup

from mlup.ml.empty import EmptyModel  # This stub class



up = mlup.UP(ml_model=EmptyModel())



# You can create pickle file

with open('path_to_pickle_file.pckl', 'wb') as f:

    pickle.dump(up, f)



# After in server

with open('path_to_pickle_file.pckl', 'rb') as f:

    up = pickle.load(f)

up.ml.load()

up.run_web_app()

```



#### Change config



If you can change model settings (See [Description of the application life cycle](https://github.com/nxexox/pymlup/blob/main/docs/life_cycle.md#upmlload_model_settings)), need call `up.ml.load_model_settings()`.



```python

import mlup



class MyAnyModelForExample:

    def predict(self, X):

        return X



ml_model = MyAnyModelForExample()



up = mlup.UP(

    ml_model=ml_model,

    conf=mlup.Config(port=8011)

)

up.ml.load()

up.conf.auto_detect_predict_params = False

up.ml.load_model_settings()

```



### Examples server commands



#### mlup run



You can run web application from model, config, pickle up object. Bash command mlup run making this.



See `mlup run --help` or [Description of the bash commands](https://github.com/nxexox/pymlup/blob/main/docs/bash_commands.md#mlup-run) for full docs.



##### From model

```bash

mlup run -m /path/to/your/model.extension

```



This will run code something like this: 



```python

import mlup

from mlup import constants



up = mlup.UP(

    conf=mlup.Config(

        storage_type=constants.StorageType.disk,

        storage_kwargs={

            'path_to_files': '/path/to/your/model.extension',

            'files_mask': r'.+',

        },

    )

)

up.ml.load()

up.run_web_app()

```



You change config attributes in this mode. For this, you can add arguments `--up.=new_value`. 

(For more examples see `mlup run --help` or [Description of the bash commands](https://github.com/nxexox/pymlup/blob/main/docs/bash_commands.md#mlup-run)).



##### From config

```bash

mlup run -c /path/to/your/config.yaml

# or mlup run -ct json -c /path/to/your/config.json

```



This will run code something like this:



```python

import mlup



up = mlup.UP.load_from_yaml(conf_path='/path/to/your/config.yaml', load_model=True)

up.run_web_app()

```



##### From mlup.UP pickle/joblib object

```bash

mlup run -b /path/to/your/up_object.pckl

# or mlup run -bt joblib -b /path/to/your/up_object.joblib

```



This will run code something like this:



```python

import pickle



with open('/path/to/your/up_object.pckl', 'rb') as f:

    up = pickle.load(f)

up.run_web_app()

```



#### mlup make-app



This command making `.py` file with mlup web application and your model, config, pickle up object or with default settings.



See `mlup make-app --help` or [Description of the bash commands](https://github.com/nxexox/pymlup/blob/main/docs/bash_commands.md#mlup-make-app) for full docs.



##### With default settings

```bash

mlup make-app example_without_data_app.py

```



This command is making something like this:



```python

# example_without_data_app.py

import mlup



# You can load the model yourself and pass it to the "ml_model" argument.

# up = mlup.UP(ml_model=my_model, conf=mlup.Config())

up = mlup.UP(

    conf=mlup.Config(

        # Set your config, for work model and get model.

        # You can use storage_type and storage_kwargs for auto_load model from storage.

    )

)

up.ml.load()

up.web.load()



# If you want to run the application yourself, or add something else to it, use this variable.

# Example with uvicorn: uvicorn example_app:app --host 0.0.0.0 --port 80

app = up.web.app



if __name__ == '__main__':

    up.run_web_app()

```



And you can write your settings and run web application:

```bash

python3 example_without_data_app.py

```



##### With only model

```bash

mlup make-app -ms /path/to/my/model.onnx example_without_data_app.py

```



This command is making something like this:



```python

# example_without_data_app.py

import mlup

from mlup import constants



up = mlup.UP(

    conf=mlup.Config(

        # Set your config, for work model and get model.

        storage_type=constants.StorageType.disk,

        storage_kwargs={

            'path_to_files': '/path/to/my/model.onnx',

            'files_mask': 'model.onnx',

        },

    )

)

up.ml.load()

up.web.load()



# If you want to run the application yourself, or add something else to it, use this variable.

# Example with uvicorn: uvicorn example_app:app --host 0.0.0.0 --port 80

app = up.web.app



if __name__ == '__main__':

    up.run_web_app()



```



And you can run web application:

```bash

python3 example_without_data_app.py

```



##### With only config

```bash

mlup make-app -cs /path/to/my/config.yaml example_without_data_app.py

```



This command is making something like this:



```python

# example_without_data_app.py

import mlup



up = mlup.UP.load_from_yaml('/path/to/my/config.yaml', load_model=False)

up.ml.load()

up.web.load()



# If you want to run the application yourself, or add something else to it, use this variable.

# Example with uvicorn: uvicorn example_app:app --host 0.0.0.0 --port 80

app = up.web.app



if __name__ == '__main__':

    up.run_web_app()

```



And you can run web application:

```bash

python3 example_without_data_app.py

```



##### With only binary UP object

```bash

mlup make-app -bs /path/to/my/up.pickle example_without_data_app.py

```



This command is making something like this:



```python

# example_without_data_app.py

import pickle



with open('/path/to/my/up.pickle', 'rb') as f:

    up = pickle.load(f)



if not up.ml.loaded:

    up.ml.load()

up.web.load()



# If you want to run the application yourself, or add something else to it, use this variable.

# Example with uvicorn: uvicorn example_app:app --host 0.0.0.0 --port 80

app = up.web.app



if __name__ == '__main__':

    up.run_web_app()

```



And you can run web application:

```bash

python3 example_without_data_app.py

```



#### mlup validate-config



This command use for validation your config. This command have alpha version and need finalize.



See `mlup validate-config --help` or [Description of the bash commands](https://github.com/nxexox/pymlup/blob/main/docs/bash_commands.md#mlup-validate-config) for full docs.



```bash

mlup validate-config /path/to/my/conf.yaml

```



## Web application interface



By default, web application starting on http://localhost:8009 and have api docs.



See [Web app API](https://github.com/nxexox/pymlup/tree/main/docs/web_app_api.md) for more details. 



### Interactive API docs



Now go to http://localhost:8009/docs.



You will see the automatic interactive API documentation (provided by [Swagger UI](https://github.com/swagger-api/swagger-ui)):



### Api points



#### /health

Use for check health web application.

 

HTTP's methods: HEAD, OPTIONS, GET



##### Return JSON 

```{'status': 200}``` and status code is 200.



#### /info

Use for get model and application information. If set debug=True in config, return full config. 



HTTP's methods: GET



##### Return JSON:

```json

{

  "model_info": {

    "name": "MyFirstMLupModel",

    "version": "1.0.0.0",

    "type": "sklearn",

    "columns": null

  },

  "web_app_info": {

    "version": "1.0.0.0",

  }

}

```



If set in config `debug=True`, return another json, almost complete config. But no sensitive data.



```json

{

  "web_app_config": {

    "host": "localhost",

    "port": 8009,

    "web_app_version": "1.0.0.0",

    "column_validation": false,

    "custom_column_pydantic_model": null,

    "mode": "mlup.web.architecture.directly_to_predict.DirectlyToPredictArchitecture",

    "max_queue_size": 100,

    "ttl_predicted_data": 60,

    "ttl_client_wait": 30.0,

    "min_batch_len": 10,

    "batch_worker_timeout": 1.0,

    "is_long_predict": false,

    "show_docs": true,

    "debug": true,

    "throttling_max_requests": null,

    "throttling_max_request_len": null,

    "timeout_for_shutdown_daemon": 3.0,

    "item_id_col_name": "mlup_item_id"

  },

  "model_config": {

    "name": "MyFirstMLupModel",

    "version": "1.0.0.0",

    "type": "sklearn",

    "columns": null,

    "predict_method_name": "predict",

    "auto_detect_predict_params": true,

    "storage_type": "mlup.ml.storage.memory.MemoryStorage",

    "binarization_type": "auto",

    "use_thread_loop": true,

    "max_thread_loop_workers": true,

    "data_transformer_for_predict": "mlup.ml.data_transformers.numpy_data_transformer.NumpyDataTransformer",

    "data_transformer_for_predicted": "mlup.ml.data_transformers.numpy_data_transformer.NumpyDataTransformer",

    "dtype_for_predict": null

  }

}

```



#### /predict



Use for call predict in model.



HTTP's methods: POST



##### Requests body data:

```json

{

  "data_for_predict": [

    "input_data_for_obj_1",

    "input_data_for_obj_2",

    "input_data_for_obj_3"

  ]

}

```



Key `data_for_predict` is default key for inner data. In config by default set param `auto_detect_predict_params` is True. 

This param activate analyze model predict method, get arguments from and generate API by params. 

If `auto_detect_predict_params` found params, he changes `data_for_predict` to finding keys and change API docs.



Example for `scikit-learn` models:

```json

{

  "X": [

    "input_data_for_obj_1",

    "input_data_for_obj_2",

    "input_data_for_obj_3"

  ]

}

```



`input_data_for_obj_1` maybe any valid JSON data. These data are run through data transformers from config `data_transformer_for_predict`.



By default, this param is `mlup.ml.data_transformers.numpy_data_transformer.NumpyDataTransformer`.



##### Return JSON:

```json

{

  "predict_result": [

    "predict_result_for_obj_1",

    "predict_result_for_obj_2",

    "predict_result_for_obj_3"

  ]

}

```



`predict_result_for_obj_1` will be valid JSON data. These data, after being predicted by the model, are run through data transformers from config `data_transformer_for_predicted`.



By default, this param is `mlup.ml.data_transformers.numpy_data_transformer.NumpyDataTransformer`.



##### Validation



This method have validation for inner request data. It's making from config `columns` and flag `column_validation`.



## Web application modes



See [Web app architectures](https://github.com/nxexox/pymlup/tree/main/docs/web_app_architectures.md) for more details. 



Web application have three works modes:



* `directly_to_predict` - is Default. User request send directly to model.

* `worker_and_queue` - ml model starts in thread worker and take data for predict from queue. 

  Web application new user requests send to queue and wait result from results queue.

* `batching` - ml model start in thread worker and take data for predict from queue. 

  But not for one request, but combines data from several requests and sends it in one large array to the model. 

  Web application new user requests send to queue and wait result from results queue.



This param is naming `mode`.

```python

import mlup

from mlup.ml.empty import EmptyModel

from mlup import constants



up = mlup.UP(

    ml_model=EmptyModel(),

    conf=mlup.Config(

        mode=constants.WebAppArchitecture.worker_and_queue,

    )

)

```



If your model is light, or you hae many CPU/GPU/RAM, you can run many processes:

```python

import mlup

from mlup.ml.empty import EmptyModel

from mlup import constants



up = mlup.UP(

    ml_model=EmptyModel(),

    conf=mlup.Config(

        mode=constants.WebAppArchitecture.worker_and_queue,

        uvicorn_kwargs={'workers': 4},

    )

)

```



## Metrics



MLup PyPi download statistics: https://pepy.tech/project/pymlup



[![Downloads](https://static.pepy.tech/badge/pymlup)](https://pepy.tech/project/pymlup)

[![Downloads](https://static.pepy.tech/badge/pymlup/month)](https://pepy.tech/project/pymlup)

[![Downloads](https://static.pepy.tech/badge/pymlup/week)](https://pepy.tech/project/pymlup)