https://github.com/itzalver/basenet-api

A simple way to build ML models with Keras and Tensorflow as base frameworks.
https://github.com/itzalver/basenet-api

api deep-learning keras machine-learning python ray tensorflow

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A simple way to build ML models with Keras and Tensorflow as base frameworks.

• Host: GitHub
• URL: https://github.com/itzalver/basenet-api
• Owner: iTzAlver
• License: apache-2.0
• Created: 2022-10-24T09:43:27.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-07T08:24:47.000Z (over 1 year ago)
• Last Synced: 2024-09-23T07:32:35.180Z (1 day ago)
• Topics: api, deep-learning, keras, machine-learning, python, ray, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 78.1 MB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# BaseNet: A simpler way to build AI models.



    





    

        

    

        

    

        

    

        

    

        





    

        

    

        

    

        



# Basenet API Package - 1.9.5:  (2.0.0 Is coming!!)

This package implements an API over Keras and Tensorflow to build Deep Learning models easily without losing the

framework flexibility. BaseNet API tries to implement almost everything from a few lines of code.

> **Disclaimer**: This API is under development. This means that there isn't a stable release yet.

> However, some features are stable and can be used. Check the tutorials for more info. 

> Please, check the roadmap below to see the future updates.

> Any feature request is wellcome and appreciated; and has a high probability to be implemented.

> Any issue is wellcome and appreciated; and it will often be inspected.

## About ##

    Author: A.Palomo-Alonso ([email protected])

    Universidad de Alcalá.

    Escuela Politécnica Superior.

    Departamento de Teoría De la Señal y Comunicaciones (TDSC).

    ISDEFE Chair of Research.

## Features

#### **MACHINE LEARNING**

* **Feature 01:** Database train, validation and test automatic and random segmentation for DeepLearning.

* **Feature 02:** Solving optimization problems for MetaHeuristic models.

#### **EFFICIENCY**

* **Feature 03:** Real multiprocessing training process (CPU usage optimization).

* **Feature 04:** Automatic and custom GPU usage and assignment.

#### **SIMPLICITY**

* **Feature 05:** Easy-to-use API.

* **Feature 06:** API documentation.

* **Feature 07:** JuPyter Notebooks tutorials included.

* **Feature 08:** Python Packaging and PyPi indexing.

#### **MONITORIZATION**

* **Feature 09:** Real-time logging.

* **Feature 10:** Dashboards included.

* **Feature 11:** Model printing and easy debugging.

#### **CONNECTIVITY**

* **Feature 12:** Model merging and multiple model inputs.

* **Feature 13:** Computational cluster linking.

* **Feature 14:** The different parts of the API are designed to interact.

* **Feature 15:** It allows to create dynamic databases for data ingestion and math problems that require synthetic data.

#### **RELIABILITY**

* **Feature 16:** Depends on huge and reliable frameworks: KERAS, TENSORFLOW, RAY.

* **Feature 17:** Code updating and active support.

### Cons:

#### **FLEXIBILITY**

* An API must look for a balance between simplicity and flexibility. In this case, we bet on simplicity; but the API

is still highly flexible.

* The API is designed for high level research and design. But it is not optimal for low-level research.

#### **DEPENDENCE**

* This API is built over highly reliable frameworks, however, the API depends on those frameworks to run the models.

#### **DEPLOYMENT**

* The API can not deploy models by itself yet in this current version. But I am planning!

### Roadmap:

#### Discipline coverage:

- [x] Database building.

- [x] Supervised learning.

- [x] MetaHeuristic optimization.

- [ ] Unsupervised learning. (Planning in 3.0 release)

- [ ] Reinforcement learning. (Planning in 4.0 release)

- [ ] Computer Vision. (Planning in 5.0 release)

- [ ] Natural Language Processing. (Will be in development from 3.0 to 6.0)

#### Feature roadmap:

- [x] Feeder databases.

- [x] Computational clustering.

- [x] Monitoring.

- [ ] BaseNetDeployment: 

  - Deploy your model in your infrastructure with high connectivity and scalability. 

  - With preprocess, model, postprocess and front-end sector.

  - Automatic workload balance (probably with ``Ray`` or ``Spark`` clusters).

  - Dynamic training after deployment. The model will still be learning from input data if desired.

- [ ] Reinforcement learning:

  - This will be hard to add in deployment.

  - Custom environments.

  - Connectivity with BaseNetCompiler: It will work with RL policy and DL!

  - State-of-the-art RL.

- [ ] Computer Vision:

  - It is already possible to be implemented in DeepLearning API; but can be improved.

  - Database visualization.

  - Database size optimization.

  - Computer vision utils.

- [ ] Natural Language Processing:

  - Hard to tell here, this discipline is very ad-hoc, and it is hard to pack it in an API.

  - Transformers, LSTM and Word Vectorization will be included.

  - Preprocessing will be a must.

  - Probably will use ``HuggingFace`` package.

  - Will be in constant development.

## What's new?

### < 0.1.0

1. BaseNetModel included.

2. BaseNetDatabase included.

3. BaseNetCompiler included.

4. Inheritance from CorNetAPI project.

5. Multi-processing fitting.

6. Tensorboard launching.

### 0.2.0

1. BaseNetResults included (working).

2. Now the model is callable.

3. Switched print to logging.

4. Project documentation.

### 1.0.0 - 1.0.3

1. Python packaging

3. 1.0.x: Upload bug solving.

### 1.1.0

1. Functional package.

2. PyPi indexing.

### 1.2.0:

1. Loss results included in the BaseNetResults while multiprocessing.

2. GPU auto set up to avoid TensorFlow memory errors.

3. Method ``BaseNetCompiler.set_up_devices()`` configures the GPUs according to the free RAM to be used in the API.

### 1.3.0

1. Included WindowDiff to the project scope.

### 1.4.0

1. Solved python packaging problems.

2. Included force stop callback in the ```BaseNetModel.fit_stop()``` method.

### 1.5.0

1. BaseNetDatabase now has the attributes ``BaseNetDatabase.size`` and ``BaseNetDatabase.distribution``.

2. Solved forced stopping bugs with multiprocessing in the method ``BaseNetDatabase.fit_stop()``.

3. ```BaseNetModel._threshold()``` private method now takes a set of outputs instead only one. 

This was only for optimization.

4. Solved wrong ```BaseNetModel.recover()```.

5. **Auto recover implemented**, now ```BaseNetModel.recover()``` is a private method: ```BaseNetModel._recover()```.

Now the used does not need to recover it. *The model recovers by itself. -- Hans Niemann 2022.* 

**NOTE**: RECOVER IS NECESARY WHEN THE MODEL IS EARLY STOPPED; CONSIDER RECOVERING ALWAYS THE MODEL.

### 1.5.1 - 1.5.3

1. Solved a bug where ``BaseNetDatabase`` modified the incoming list of instances in the database; avoiding checkpoints

for large database generators.

2. Exception handler for ``nvml`` library if NVIDIA Drivers are not installed`in the machine.

### 1.5.4

1. Added some ``BaseNetDatabase`` utils: merge and split databases.

2. Added ``BaseNetDatabase`` equality check.

3. Added a ``BaseNetDatabase._reversion()``, ``BaseNetCompiler._reversion()`` and ``BaseNetModel.__version__``. Which

rebuilds the Classes to the current version of the API.

### 1.6.0

1. Start to develop the second branch of the API: ``BaseNetHeuristic``

2. Start to create JuPyter Notebook tutorials of the API.

3. Included ``BaseNetDatabase`` binarization and normalization of databases.

4. Included in ``BaseNetDatabase`` to read ``TensorFlow`` and ``Pandas`` databases.

5. Some rework was done for bug-fixing and providing more logging information.

### 1.7.0 - 1.9.1

1. Reworked ``BaseNetDatabase`` for minor bug fixing. (1.7.0)

2. Added the ``BaseNetFeeder`` Class to generate dynamic ``BaseNetDatabase``s. (1.7.0)

3. Jupyter tutorials. (1.8.0)

4. Added the ``BaseNetDeployment`` Class to deploy scalable Machine Learning models (in alpha, do not use until 3.0).

(1.9.0)

5. Added ``BaseNetHeuristic`` algorithms that implements MetaHeuristic methods such as PSO. (1.8.0)

### 1.9.2

1. Added ``BaseNetLMSE``, solving linear problems with matrix multiplication.

### 1.9.3 - 1.9.5

1. Added ``CassandraDatabase``.

2. Added ``categorical`` entry to ``compile_options`` in the ``BaseNetCompiler``. 

This allows the user to build a classifier in the DeepLearning module. By default, it is a regression model.

3. Added a minimum of 1 GPU in ``BaseNetCompiler.set_up_devices()``

4. Added the output mapping function to the ``BaseNetDatabase`` as ``.define_map()`` and the mapping method ``map()``.

5. Added the attribute ``shape`` to the ``BaseNetDatabase`` telling the input-output data shapes.

6. Added Fuzzy-Logic feature in ``BaseNetDatabase`` for Fuzzy-Logic mapping.

7. Added `BaseNetCVVisualizer` and ``basenet_cv_gui`` for computer vision dataset and results visualization.

8. Added `BaseNetStackRoom`, which is a Database manager for big databases.

### 1.9.6

1. Added multi-block to ``BaseNetCompiler`` so it can be built from several YAML files.

## Basic and fast usage

There are Jupyter Notebooks with usage tutorials! Refer to them 

[HERE](https://github.com/iTzAlver/BaseNet-API/tree/main/jupyter). You should run your notebook creating a 

virtual environment in this README path.

### Cite as

Please, cite this library as:

    @misc{basenetapi,

      title={BaseNet: A simpler way to build AI models.},

      author={A. Palomo-Alonso},

      booktitle={PhD in TIC: Machine Learning and NLP.},

      year={2022}

    }