uniovi-simur-wearablepermed-ml 1.8.0

Uniovi Simur WearablePerMed Machine Learning.

Description

Uniovi Simur WearablePerMed Machine Learning.

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• Pasos para lanzar el entrenamiento de un modelo de clasificación: *

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1. Abrir el archivo "train_automatizado.py".

2. Entre las líneas 27 y 37, descomentar el modelo de clasificación que se desea entrenar. Comentamos el resto de modelos. Para una misma clase de modelo tenemos 3 posibilidades, en función de si empleamos datos de muslo, muñeca o muslo+muñeca durante el entrenamiento.

3. Ejecutar el archivo "train_automatizado.py". Este código ya empleará el resto de dependencias auxiliares: Datareader, modelGenerator, etc.

Como resultado se guardará el modelo entrenado en formato ".h5" (para las CNNs) o ".pkl" (para RandomForest y XGBoost). Estos modelos pueden cargarse para estudiar resultados en la fase de test mediante el fichero "scriptResults_v2.ipynb".

Fragmento del pipeline:

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• Stack de datos/características * -------> * Modelos de clasificación *

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• Repositorio Machine Learning (desarrollosPMP_SiMuR) -

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Árbol de directorios:

/Raíz (desarrollosPMP_SiMuR) | … | |-- modelGenerator.py |-- train_automatizado.py (PROGRAMA PRINCIPAL) |-- Models | | | |-- SiMuRModel.py (Implementación de las clases, junto con sus métodos, asociada a cada modelo) | |-- *.h5, *.pkl (Se generan tras entrenar cada modelo) | |-- init.py | |-- scriptResults_v2.ipynb (Resultados en la etapa de test para los modelos entrenados) |-- train_hyperparameter_searching_v2.py (Búsqueda de hiperparámetros óptimos de cada modelo, empleando el algoritmo ASHA).

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•   Contenido de SiMuRModel.py        *
  

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1 clase para cada modelo de clasificación: class SiMuRModel_ESANN (Red Neuronal Convolucional, CNN) class SiMuRModel_CAPTURE24 (CNN de arquitectura más compleja) class SiMuRModel_RandomForest (Balanced Random Forest) class SiMuRModel_XGBoost <---- Tengo que actualizarlo en GitHub, lo tengo local en mi PC.

Cada modelo para datos de: * thigh (muslo). * wrist (muñeca). * thigh + wrist (muslo + muñeca).

En "train_automatizado.py" se crea un objeto de cada clase y se lanza el entrenamiento del modelo de clasificación.

Scaffold your project from scratch

• STEP01: Install PyScaffold and pyscaffoldext-markdown extension

  • You can install PyScaffold and extensions globally in your systems but ins recomendes use a virtual environment:

    Craate a temp folder and use a virtual environment to install PyScaffold tool and scaffold your project. Later will copy the results under the final git folder and remove the last temporal one:

    $ mkdir temp
    $ cd temp
    $ python3 -m venv .venv
    $ source .venv/bin/activate
    $ pip install pyscaffold
    $ pip install pyscaffoldext-markdown
    $ putup --markdown uniovi-simur-wearablepermed-ml -p wearablepermed_ml \
         -d "Uniovi Simur WearablePerMed ML." \
         -u https://github.com/Simur-project/uniovi-simur-wearablepermed-ml.git
    $ deactivate               
    

  • Also you can install pyscaffold and pyscaffoldext-markdown packages in your system and avoid the error from Python 3.11+: "error-externally-managed-environment" this environemnt is externally managed you can execute this command to force instalation:

    $ pip3 install pyscaffold --break-system-packages
    $ pip3 install pyscaffoldext-markdown --break-system-packages
    $ putup --markdown uniovi-simur-wearablepermed-ml -p wearablepermed_ml \
         -d "Uniovi Simur WearablePerMed ML." \
         -u https://github.com/Simur-project/uniovi-simur-wearablepermed-ml.git
    

    or permanent configure pip3 with this command to avoid the previous errors from 3.11+

    $ python3 -m pip config set global.break-system-packages true
    

• STEP02: creare your repo under SIMUR Organization with the name uniovi-simur-wearablepermed-ml and clone the previous scaffoled project

  $ cd git
  $ git clone https://github.com/Simur-project/uniovi-simur-wearablepermed-ml.git
  

• STEP03: copy PyScaffold project to your git folder without .venv folder

• STEP04: install tox project manager used by PyScaffold. Install project dependencies

  $ python3 -m venv .venv
  $ source .venv/bin/activate
  $ pip install tox
  $ pip install pandas
  $ pip install matplotlib
  $ pip install openpyxl
  $ tox list
  

  Installation your python pipeline packages in your virtual environment in development mode:

  $ pip freeze > requirements.txt
  

Start develop your project

• STEP01: Clone your project

  $ git clone https://github.com/Simur-project/uniovi-simur-wearablepermed-ml.git
  

• STEP01: Build and Debug your project

  $ tox list
  default environments:
  default   -> Invoke pytest to run automated tests
  
  additional environments:
  build     -> Build the package in isolation according to PEP517, see https://github.com/pypa/build
  clean     -> Remove old distribution files and temporary build artifacts (./build and ./dist)
  docs      -> Invoke sphinx-build to build the docs
  doctests  -> Invoke sphinx-build to run doctests
  linkcheck -> Check for broken links in the documentation
  publish   -> Publish the package you have been developing to a package index server. By default, it uses testpypi. If you really want to publish your package to be publicly accessible in PyPI, use the `-- --repository pypi` option
  

  $ tox -e clean
  $ tox -e build
  $ tox -e docs
  $ tox -e publish -- --repository pypi
  

• STEP02 Build service

  $ docker build -t uniovi-simur-wearablepermed-ml:1.0.0 .
  

• STEP03: Tag service

  $ docker tag uniovi-simur-wearablepermed-ml:1.0.0 ofertoio/uniovi-simur-wearablepermed-ml:1.0.0
  

• STEP04: Publish service

  $ docker logout
  $ docker login
  $ docker push ofertoio/uniovi-simur-wearablepermed-ml:1.0.0
  

Using GPU

Follow these steps to use GPU from your python script:

• STEP01: Install NVIDIA Drivers Install NVIDIA Drivers for your card, in our case: NVIDIA GeForce RTX 4060 Ti card

• STEP02: Install CUDA Toolkit

  $ sudo apt-get install -y nvidia-cuda-toolkit
  

  Get CUDA version installed:

  $ nvcc --version
  nvcc: NVIDIA (R) Cuda compiler driver
  Copyright (c) 2005-2023 NVIDIA Corporation
  Built on Fri_Jan__6_16:45:21_PST_2023
  Cuda compilation tools, release 12.0, V12.0.140
  Build cuda_12.0.r12.0/compiler.32267302_0
  

• STEP03: Install cuDNN

  Install cuDNN for DeepLearning from python compatible with your CUDA version:

  wget https://developer.download.nvidia.com/compute/cudnn/9.10.2/local_installers/cudnn-local-repo-ubuntu2404-9.10.2_1.0-1_amd64.deb
  sudo dpkg -i cudnn-local-repo-ubuntu2404-9.10.2_1.0-1_amd64.deb
  sudo cp /var/cudnn-local-repo-ubuntu2404-9.10.2/cudnn-*-keyring.gpg /usr/share/keyrings/
  sudo apt-get update
  sudo apt-get -y install cudnn
  

  Install CUDA 12 from aptitude, perform the above configuration but install the CUDA 12 specific package:

  sudo apt-get -y install cudnn-cuda-12
  

  Check cuDNN installed:

  $ dpkg -l | grep libcudnn
  ii  libcudnn9-cuda-12                             9.10.2.21-1                              amd64        cuDNN runtime libraries for CUDA 12.9
  ii  libcudnn9-dev-cuda-12                         9.10.2.21-1                              amd64        cuDNN development libraries for CUDA 12.9
  ii  libcudnn9-headers-cuda-12                     9.10.2.21-1                              amd64        cuDNN header files for CUDA 12.9
  ii  libcudnn9-static-cuda-12                      9.10.2.21-1                              amd64        cuDNN static libraries for CUDA 12.9
  

• STEP04: Configure CUDA environment Create a file called cuda_env.sh with these env variables configurations:

  export CUDA_HOME=/usr/lib/nvidia-cuda-toolkit
  export PATH=$CUDA_HOME/bin:$PATH
  export LD_LIBRARY_PATH=$CUDA_HOME/compute-sanitizer:$LD_LIBRARY_PATH
  export XLA_FLAGS=--xla_gpu_cuda_data_dir=/usr/lib/cuda
  

  make executable the cuda_env.sh file:

  $ chmod +x cuda_env.sh 
  

  Move the file to profile.d folder, to have these env variables globally, for any user server sessions

  $ sudo mv cuda_env.sh /etc/profile.d/
  

• STEP05: Relogin a session You must logout and login again using your accout to have access to CUDA env variables. Check it:

  echo $CUDA_HOME
  echo $PATH
  echo $LD_LIBRARY_PATH
  echo $XLA_FLAGS
  

Note

This project has been set up using PyScaffold 4.6. For details and usage information on PyScaffold see https://pyscaffold.org/.