docker/requirements-gpu.txt

+# ----------------------------------------------------
+#    ______ _     _ _       __      __             
+#   |  ____(_)   | | |      \ \    / /             
+#   | |__   _  __| | | ___   \ \  / /__ _ ____   __
+#   |  __| | |/ _` | |/ _ \   \ \/ / _ \ '_ \ \ / /
+#   | |    | | (_| | |  __/    \  /  __/ | | \ V / 
+#   |_|    |_|\__,_|_|\___|     \/ \___|_| |_|\_/  
+#                               Fidle pip virtual env
+# ----------------------------------------------------
+#
+# To install your Fidle env, see https://fidle.cnrs.fr/installation
+#
+# Keras 3 / PyTorch version (Python 3.9.2)
+
+ torch
+ torchvision
+ torch-geometric
+ torchtext
+ torchdata
+ lightning
+ tensorboard
+ keras
+ transformers
+ numpy
+ Scikit-image
+ Scikit-learn
+ Matplotlib
+ plotly
+ seaborn
+ einops
+ datasets
+ barviz
+ pyarrow
+ Pandas
+ Pandoc
+ pyyaml
+ Jupyterlab
+ fidle

environment.yml

-name: deeplearning2
-channels:
-  - defaults
-dependencies:
-  - _libgcc_mutex=0.1=main
-  - _tflow_select=2.1.0=gpu
-  - absl-py=0.8.1=py37_0
-  - astor=0.8.0=py37_0
-  - attrs=19.3.0=py_0
-  - backcall=0.1.0=py37_0
-  - blas=1.0=mkl
-  - bleach=3.1.0=py_0
-  - c-ares=1.15.0=h7b6447c_1001
-  - ca-certificates=2019.11.27=0
-  - certifi=2019.11.28=py37_0
-  - cloudpickle=1.2.2=py_0
-  - cudatoolkit=10.0.130=0
-  - cudnn=7.6.4=cuda10.0_0
-  - cupti=10.0.130=0
-  - cycler=0.10.0=py37_0
-  - cytoolz=0.10.1=py37h7b6447c_0
-  - dask-core=2.9.0=py_0
-  - dbus=1.13.12=h746ee38_0
-  - decorator=4.4.1=py_0
-  - defusedxml=0.6.0=py_0
-  - entrypoints=0.3=py37_0
-  - expat=2.2.6=he6710b0_0
-  - fontconfig=2.13.0=h9420a91_0
-  - freetype=2.9.1=h8a8886c_1
-  - gast=0.2.2=py37_0
-  - glib=2.63.1=h5a9c865_0
-  - gmp=6.1.2=h6c8ec71_1
-  - google-pasta=0.1.8=py_0
-  - grpcio=1.16.1=py37hf8bcb03_1
-  - gst-plugins-base=1.14.0=hbbd80ab_1
-  - gstreamer=1.14.0=hb453b48_1
-  - h5py=2.9.0=py37h7918eee_0
-  - hdf5=1.10.4=hb1b8bf9_0
-  - icu=58.2=h9c2bf20_1
-  - imageio=2.6.1=py37_0
-  - importlib_metadata=1.3.0=py37_0
-  - intel-openmp=2019.4=243
-  - ipykernel=5.1.3=py37h39e3cac_0
-  - ipython=7.10.2=py37h39e3cac_0
-  - ipython_genutils=0.2.0=py37_0
-  - jedi=0.15.1=py37_0
-  - jinja2=2.10.3=py_0
-  - joblib=0.14.1=py_0
-  - jpeg=9b=h024ee3a_2
-  - json5=0.8.5=py_0
-  - jsonschema=3.2.0=py37_0
-  - jupyter_client=5.3.4=py37_0
-  - jupyter_core=4.6.1=py37_0
-  - jupyterlab=1.2.4=pyhf63ae98_0
-  - jupyterlab_server=1.0.6=py_0
-  - keras-applications=1.0.8=py_0
-  - keras-preprocessing=1.1.0=py_1
-  - kiwisolver=1.1.0=py37he6710b0_0
-  - libedit=3.1.20181209=hc058e9b_0
-  - libffi=3.2.1=hd88cf55_4
-  - libgcc-ng=9.1.0=hdf63c60_0
-  - libgfortran-ng=7.3.0=hdf63c60_0
-  - libpng=1.6.37=hbc83047_0
-  - libprotobuf=3.11.2=hd408876_0
-  - libsodium=1.0.16=h1bed415_0
-  - libstdcxx-ng=9.1.0=hdf63c60_0
-  - libtiff=4.1.0=h2733197_0
-  - libuuid=1.0.3=h1bed415_2
-  - libxcb=1.13=h1bed415_1
-  - libxml2=2.9.9=hea5a465_1
-  - markdown=3.1.1=py37_0
-  - markupsafe=1.1.1=py37h7b6447c_0
-  - matplotlib=3.1.1=py37h5429711_0
-  - mistune=0.8.4=py37h7b6447c_0
-  - mkl=2019.4=243
-  - mkl-service=2.3.0=py37he904b0f_0
-  - mkl_fft=1.0.15=py37ha843d7b_0
-  - mkl_random=1.1.0=py37hd6b4f25_0
-  - more-itertools=8.0.2=py_0
-  - nbconvert=5.6.1=py37_0
-  - nbformat=4.4.0=py37_0
-  - ncurses=6.1=he6710b0_1
-  - networkx=2.4=py_0
-  - notebook=6.0.2=py37_0
-  - numpy=1.17.4=py37hc1035e2_0
-  - numpy-base=1.17.4=py37hde5b4d6_0
-  - olefile=0.46=py_0
-  - openssl=1.1.1d=h7b6447c_3
-  - opt_einsum=3.1.0=py_0
-  - pandas=0.25.3=py37he6710b0_0
-  - pandoc=2.2.3.2=0
-  - pandocfilters=1.4.2=py37_1
-  - parso=0.5.2=py_0
-  - patsy=0.5.1=py37_0
-  - pcre=8.43=he6710b0_0
-  - pexpect=4.7.0=py37_0
-  - pickleshare=0.7.5=py37_0
-  - pillow=6.2.1=py37h34e0f95_0
-  - pip=19.3.1=py37_0
-  - prometheus_client=0.7.1=py_0
-  - prompt_toolkit=3.0.2=py_0
-  - protobuf=3.11.2=py37he6710b0_0
-  - ptyprocess=0.6.0=py37_0
-  - pygments=2.5.2=py_0
-  - pyparsing=2.4.5=py_0
-  - pyqt=5.9.2=py37h05f1152_2
-  - pyrsistent=0.15.6=py37h7b6447c_0
-  - python=3.7.5=h0371630_0
-  - python-dateutil=2.8.1=py_0
-  - pytz=2019.3=py_0
-  - pywavelets=1.1.1=py37h7b6447c_0
-  - pyzmq=18.1.0=py37he6710b0_0
-  - qt=5.9.7=h5867ecd_1
-  - readline=7.0=h7b6447c_5
-  - scikit-image=0.15.0=py37he6710b0_0
-  - scikit-learn=0.22.1=py37hd81dba3_0
-  - scipy=1.3.2=py37h7c811a0_0
-  - seaborn=0.9.0=pyh91ea838_1
-  - send2trash=1.5.0=py37_0
-  - setuptools=42.0.2=py37_0
-  - sip=4.19.8=py37hf484d3e_0
-  - six=1.13.0=py37_0
-  - sqlite=3.30.1=h7b6447c_0
-  - statsmodels=0.10.1=py37hdd07704_0
-  - tensorboard=2.0.0=pyhb38c66f_1
-  - tensorflow=2.0.0=gpu_py37h768510d_0
-  - tensorflow-base=2.0.0=gpu_py37h0ec5d1f_0
-  - tensorflow-estimator=2.0.0=pyh2649769_0
-  - tensorflow-gpu=2.0.0=h0d30ee6_0
-  - termcolor=1.1.0=py37_1
-  - terminado=0.8.3=py37_0
-  - testpath=0.4.4=py_0
-  - tk=8.6.8=hbc83047_0
-  - toolz=0.10.0=py_0
-  - tornado=6.0.3=py37h7b6447c_0
-  - traitlets=4.3.3=py37_0
-  - wcwidth=0.1.7=py37_0
-  - webencodings=0.5.1=py37_1
-  - werkzeug=0.16.0=py_0
-  - wheel=0.33.6=py37_0
-  - wrapt=1.11.2=py37h7b6447c_0
-  - xz=5.2.4=h14c3975_4
-  - zeromq=4.3.1=he6710b0_3
-  - zipp=0.6.0=py_0
-  - zlib=1.2.11=h7b6447c_3
-  - zstd=1.3.7=h0b5b093_0
-prefix: /home/pjluc/anaconda3/envs/deeplearning2
-

fidle/about.yml

+#--------------------------------------------------------------------
+#  ______ _     _ _         _____            _             _       
+# |  ____(_)   | | |       / ____|          | |           | |      
+# | |__   _  __| | | ___  | |     ___  _ __ | |_ ___ _ __ | |_ ___ 
+# |  __| | |/ _` | |/ _ \ | |    / _ \| '_ \| __/ _ \ '_ \| __/ __|
+# | |    | | (_| | |  __/ | |___| (_) | | | | ||  __/ | | | |_\__ \
+# |_|    |_|\__,_|_|\___|  \_____\___/|_| |_|\__\___|_| |_|\__|___/
+#
+#                      Formation Introduction au Deep Learning - 2024
+#--------------------------------------------------------------------
+# Formation Introduction au Deep Learning       https://fidle.cnrs.fr
+# By MIAI/CNRS/UGA 2023/24
+#
+# This file describes the notebooks used by the Fidle training.
+
+version:                  3.0.15
+content:                  notebooks
+name:                     Notebooks Fidle
+description:              All notebooks used by the Fidle training
+
+
+readme_md:                README.md
+readme_ipynb:             README.ipynb
+default_ci:               fidle/ci/default.yml
+
+toc:
+  LinearReg:              Linear and logistic regression
+  Perceptron:             Perceptron Model 1957
+  BHPD.Keras3:            BHPD regression (DNN), using Keras3/PyTorch
+  BHPD.PyTorch:           BHPD regression (DNN), using PyTorch
+  Wine.Keras3:            Wine Quality prediction (DNN), using Keras3/PyTorch
+  Wine.Lightning:         Wine Quality prediction (DNN), using PyTorch/Lightning
+  MNIST.Keras3:           MNIST classification (DNN,CNN), using Keras3/PyTorch
+  MNIST.PyTorch:          MNIST classification (DNN,CNN), using PyTorch
+  MNIST.Lightning:        MNIST classification (DNN,CNN), using PyTorch/Lightning
+  GTSRB.Keras3:           Images classification GTSRB with Convolutional Neural Networks (CNN), using Keras3/PyTorch
+  Embedding.Keras3:       Sentiment analysis with word embedding, using Keras3/PyTorch
+  RNN.Keras3:             Time series with Recurrent Neural Network (RNN), using Keras3/PyTorch
+  GNN.PyTorch:            Graph Neural Networks
+  AE.Keras3:              Unsupervised learning with an autoencoder neural network (AE), using Keras3
+  VAE.Keras3:             Generative network with Variational Autoencoder (VAE), using Keras3
+  DCGAN.Lightning:        Generative Adversarial Networks (GANs), using Lightning
+  DDPM.PyTorch:           Diffusion Model (DDPM) using PyTorch
+  Optimization.PyTorch:   Training optimization, using PyTorch
+  DRL.PyTorch:            Deep Reinforcement Learning (DRL), using PyTorch
+  Misc:                   Miscellaneous things, but very important!

fidle/ci/batch_gpu.sh

+#!/bin/bash
+# -----------------------------------------------
+#         _           _       _
+#        | |__   __ _| |_ ___| |__
+#        | '_ \ / _` | __/ __| '_ \
+#        | |_) | (_| | || (__| | | |
+#        |_.__/ \__,_|\__\___|_| |_|
+#                              Fidle at IDRIS
+# -----------------------------------------------
+#
+# SLURM batch script
+# Bash script for SLURM batch submission of ci notebooks 
+# by Jean-Luc Parouty (CNRS/SIMaP)
+#
+# Soumission :  sbatch  /(...)/batch_slurm.sh
+# Suivi      :  squeue -u $USER
+
+# ==== Job parameters ==============================================
+
+#SBATCH --job-name="Fidle ci"                      # nom du job
+#SBATCH --ntasks=1                                 # nombre de tâche (un unique processus ici)
+#SBATCH --gres=gpu:1                               # nombre de GPU à réserver (un unique GPU ici)
+#SBATCH --cpus-per-task=10                         # nombre de coeurs à réserver (un quart du noeud)
+#SBATCH --hint=nomultithread                       # on réserve des coeurs physiques et non logiques
+#SBATCH --time=05:00:00                            # temps exécution maximum demande (HH:MM:SS)
+#SBATCH --output="FIDLE_CI_%j.out"   		       # nom du fichier de sortie
+#SBATCH --error="FIDLE_CI_%j.err"    		       # nom du fichier des erreurs
+#SBATCH --mail-user=Someone@somewhere.fr
+#SBATCH --mail-type=END,FAIL
+
+# ==== Parameters ==================================================
+
+MODULE_ENV="pytorch-gpu/py3/2.1.1"
+RUN_DIR="$WORK/fidle-project/fidle"
+CAMPAIN_PROFILE="./fidle/ci/gpu-scale1.yml"
+FILTER=( '.*' )
+
+# ==================================================================
+
+echo '------------------------------------------------------------'
+echo "Start : $0"
+echo '------------------------------------------------------------'
+echo "Job id           : $SLURM_JOB_ID"
+echo "Job name         : $SLURM_JOB_NAME"
+echo "Job node list    : $SLURM_JOB_NODELIST"
+echo '------------------------------------------------------------'
+echo "module loaded    : $MODULE_ENV"
+echo "run dir          : $RUN_DIR"
+echo "campain profile  : $CAMPAIN_PROFILE"
+echo "filter           : ${FILTER[@]}"
+echo '------------------------------------------------------------'
+
+# ---- Module + env.
+
+module purge
+module load "$MODULE_ENV"
+
+export PYTHONUSERBASE=$WORK/local/fidle-k3
+export PATH=$PATH:$PYTHONUSERBASE/bin
+
+# ---- Run it...
+
+cd "$RUN_DIR"
+
+fid run_ci --quiet --campain "$CAMPAIN_PROFILE" --filter ${FILTER[@]}
+
+echo 'Done.'

fidle/ci/cpu-default.yml

+campain:
+  version:            '1.0'
+  description:         Notebook test on CPU, default settings.
+  directory:          ./campains/cpu-default
+  existing_notebook:  'skip'      # remove|skip
+  report_template:    'fidle'     # fidle|default
+  timeout:            6000
+
+  environment_vars:
+    FIDLE_SAVE_FIGS:         true
+
+
+#
+# ------------ LinearReg
+#
+LINR1:
+  notebook: LinearReg/01-Linear-Regression.ipynb
+
+GRAD1:
+  notebook: LinearReg/02-Gradient-descent.ipynb
+
+POLR1:
+  notebook: LinearReg/03-Polynomial-Regression.ipynb
+
+LOGR1:
+  notebook: LinearReg/04-Logistic-Regression.ipynb
+
+#
+# ------------ Perceptron
+#
+PER57:
+  notebook: Perceptron/01-Simple-Perceptron.ipynb
+
+#
+# ------------ BHPD.Keras3
+#
+K3BHPD1:
+  notebook: BHPD.Keras3/01-DNN-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+
+K3BHPD2:
+  notebook: BHPD.Keras3/02-DNN-Regression-Premium.ipynb
+  overrides:
+    fit_verbosity: 2
+
+#
+# ------------ BHPD.PyTorch
+#
+PBHPD1:
+  notebook: BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
+
+#
+# ------------ Wine.Keras3
+#
+K3WINE1:
+  notebook: Wine.Keras3/01-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: default
+
+#
+# ------------ Wine.Lightning
+#
+LWINE1:
+  notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: default
+
+#
+# ------------ MNIST.Keras3
+#
+K3MNIST1:
+  notebook: MNIST.Keras3/01-DNN-MNIST.ipynb
+  overrides:
+    fit_verbosity: 2
+
+K3MNIST2:
+  notebook: MNIST.Keras3/02-CNN-MNIST.ipynb
+  overrides:
+    fit_verbosity: 2
+
+#
+# ------------ MNIST.PyTorch
+#
+#PMNIST1:
+#  notebook: MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
+
+#
+# ------------ MNIST.Lightning
+#
+#LMNIST2:
+#  notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
+
+#
+# ------------ GTSRB.Keras3
+#
+K3GTSRB1:
+  notebook: GTSRB.Keras3/01-Preparation-of-data.ipynb
+  overrides:
+    scale: default
+    output_dir: default
+    progress_verbosity: default
+
+K3GTSRB2:
+  notebook: GTSRB.Keras3/02-First-convolutions.ipynb
+  after:    K3GTSRB1
+  overrides:
+    enhanced_dir: default
+    dataset_name: default
+    batch_size: default
+    epochs: default
+    scale: default
+    fit_verbosity: 2
+
+K3GTSRB3:
+  notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
+  after:    K3GTSRB1
+  overrides:
+    enhanced_dir: default
+    model_name: default
+    dataset_name: default
+    batch_size: default
+    epochs: default
+    scale: default
+    fit_verbosity: 2
+
+#
+# ------------ Embedding.Keras3
+#
+K3IMDB1:
+  notebook: Embedding.Keras3/01-One-hot-encoding.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: 2
+
+K3IMDB2:
+  notebook: Embedding.Keras3/02-Keras-embedding.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    output_dir: default
+    fit_verbosity: 2
+
+K3IMDB3:
+  notebook: Embedding.Keras3/03-Prediction.ipynb
+  after:    K3IMDB2
+  overrides:
+    vocab_size: default
+    review_len: default
+    saved_models: default
+    dictionaries_dir: default
+
+K3IMDB4:
+  notebook: Embedding.Keras3/04-Show-vectors.ipynb
+  after:    K3IMDB2
+  overrides:
+    vocab_size: default
+    review_len: default
+    saved_models: default
+    dictionaries_dir: default
+
+K3IMDB5:
+  notebook: Embedding.Keras3/05-LSTM-Keras.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: 2
+    scale: default
+
+#
+# ------------ RNN.Keras3
+#
+K3LADYB1:
+  notebook: RNN.Keras3/01-Ladybug.ipynb
+  overrides:
+    scale: default
+    train_prop: default
+    sequence_len: default
+    predict_len: default
+    batch_size: default
+    epochs: default
+
+#
+# ------------ AE.Keras3
+#
+K3AE1:
+  notebook: AE.Keras3/01-Prepare-MNIST-dataset.ipynb
+  overrides:
+    prepared_dataset: default
+    scale: 0.1
+    progress_verbosity: default
+
+K3AE2:
+  notebook: AE.Keras3/02-AE-with-MNIST.ipynb
+  after: K3AE1
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    latent_dim: default
+    train_prop: default
+    batch_size: default
+    epochs: 4
+    fit_verbosity: default
+
+K3AE3:
+  notebook: AE.Keras3/03-AE-with-MNIST-post.ipynb
+  after: K3AE2
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    train_prop: default
+    
+K3AE4:
+  notebook: AE.Keras3/04-ExtAE-with-MNIST.ipynb
+  after: K3AE1
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    train_prop: default
+    batch_size: default
+    epochs: 4
+    fit_verbosity: default
+
+K3AE5:
+  notebook: AE.Keras3/05-ExtAE-with-MNIST.ipynb
+  after: K3AE1
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    train_prop: default
+    batch_size: default
+    epochs: 4
+    fit_verbosity: default
+
+#
+# ------------ VAE.Keras3
+#
+K3VAE1:
+  notebook: VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb
+  overrides:
+    latent_dim: default
+    loss_weights: default
+    scale: 0.1
+    seed: default
+    batch_size: default
+    epochs: 4
+    fit_verbosity: default
+
+K3VAE2:
+  notebook: VAE.Keras3/02-VAE-with-MNIST.ipynb
+  overrides:
+    latent_dim: default
+    loss_weights: default
+    scale: 0.1
+    seed: default
+    batch_size: default
+    epochs: 4
+    fit_verbosity: default
+
+K3VAE3:
+  notebook: VAE.Keras3/03-VAE-with-MNIST-post.ipynb
+  after: K3VAE2
+  overrides:
+    scale: 0.1
+    seed: default
+    models_dir: default
+
+#
+# ------------ DCGAN.Lightning
+#
+PLSHEEP3:
+  notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
+  overrides:
+    latent_dim: default
+    gan_name: GAN
+    generator_name: default
+    discriminator_name: default
+    epochs: 4
+    lr: default
+    b1: default
+    b2: default
+    batch_size: default
+    num_img: default
+    fit_verbosity: default
+    dataset_file: default
+    data_shape: default
+    scale: 0.001
+    num_workers: default
+
+PLSHEEP3:
+  notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
+  overrides:
+    latent_dim: default
+    gan_name: WGANGP
+    generator_name: default
+    discriminator_name: default
+    epochs: 4
+    lr: default
+    b1: default
+    b2: default
+    batch_size: default
+    num_img: default
+    fit_verbosity: default
+    dataset_file: default
+    data_shape: default
+    scale: 0.001
+    num_workers: default
+
+
+
+#
+# ------------ Misc
+#
+NP1:
+  notebook: Misc/00-Numpy.ipynb
+
+ACTF1:
+  notebook: Misc/01-Activation-Functions.ipynb
+
+PANDAS1:
+  notebook: Misc/02-Using-pandas.ipynb
+
+FID1:
+  notebook: Misc/07-Fid-Example.ipynb
+  overrides:
+    scale: .1
+    x: 345
+    batch_size: default
\ No newline at end of file

fidle/ci/default.yml

+campain:
+  version: '1.0'
+  description: Automatically generated ci profile (06/01/25 16:42:30)
+  directory: ./campains/default
+  existing_notebook: 'remove    # remove|skip'
+  report_template: 'fidle     # fidle|default'
+  timeout: 6000
+
+#
+# ------------ LinearReg
+#
+LINR1:
+  notebook: LinearReg/01-Linear-Regression.ipynb
+GRAD1:
+  notebook: LinearReg/02-Gradient-descent.ipynb
+POLR1:
+  notebook: LinearReg/03-Polynomial-Regression.ipynb
+LOGR1:
+  notebook: LinearReg/04-Logistic-Regression.ipynb
+
+#
+# ------------ Perceptron
+#
+PER57:
+  notebook: Perceptron/01-Simple-Perceptron.ipynb
+
+#
+# ------------ BHPD.Keras3
+#
+K3BHPD1:
+  notebook: BHPD.Keras3/01-DNN-Regression.ipynb
+  overrides:
+    fit_verbosity: default
+K3BHPD2:
+  notebook: BHPD.Keras3/02-DNN-Regression-Premium.ipynb
+  overrides:
+    fit_verbosity: default
+
+#
+# ------------ BHPD.PyTorch
+#
+PBHPD1:
+  notebook: BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
+
+#
+# ------------ Wine.Keras3
+#
+K3WINE1:
+  notebook: Wine.Keras3/01-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: default
+    dataset_name: default
+
+#
+# ------------ Wine.Lightning
+#
+LWINE1:
+  notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
+  overrides:
+    fit_verbosity: default
+    dataset_name: default
+
+#
+# ------------ MNIST.Keras3
+#
+K3MNIST1:
+  notebook: MNIST.Keras3/01-DNN-MNIST.ipynb
+  overrides:
+    fit_verbosity: default
+K3MNIST2:
+  notebook: MNIST.Keras3/02-CNN-MNIST.ipynb
+  overrides:
+    fit_verbosity: default
+
+#
+# ------------ MNIST.PyTorch
+#
+PMNIST1:
+  notebook: MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
+
+#
+# ------------ MNIST.Lightning
+#
+LMNIST1:
+  notebook: MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb
+LMNIST2:
+  notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
+
+#
+# ------------ GTSRB.Keras3
+#
+K3GTSRB1:
+  notebook: GTSRB.Keras3/01-Preparation-of-data.ipynb
+  overrides:
+    scale: default
+    output_dir: default
+    progress_verbosity: default
+K3GTSRB2:
+  notebook: GTSRB.Keras3/02-First-convolutions.ipynb
+  overrides:
+    enhanced_dir: default
+    dataset_name: default
+    batch_size: default
+    epochs: default
+    scale: default
+    fit_verbosity: default
+K3GTSRB3:
+  notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
+  overrides:
+    enhanced_dir: default
+    model_name: default
+    dataset_name: default
+    batch_size: default
+    epochs: default
+    scale: default
+    fit_verbosity: default
+
+#
+# ------------ Embedding.Keras3
+#
+K3IMDB1:
+  notebook: Embedding.Keras3/01-One-hot-encoding.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+K3IMDB2:
+  notebook: Embedding.Keras3/02-Keras-embedding.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    output_dir: default
+    fit_verbosity: default
+K3IMDB3:
+  notebook: Embedding.Keras3/03-Prediction.ipynb
+  overrides:
+    vocab_size: default
+    review_len: default
+    saved_models: default
+    dictionaries_dir: default
+K3IMDB4:
+  notebook: Embedding.Keras3/04-Show-vectors.ipynb
+  overrides:
+    vocab_size: default
+    review_len: default
+    saved_models: default
+    dictionaries_dir: default
+K3IMDB5:
+  notebook: Embedding.Keras3/05-LSTM-Keras.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+    scale: default
+
+#
+# ------------ RNN.Keras3
+#
+K3LADYB1:
+  notebook: RNN.Keras3/01-Ladybug.ipynb
+  overrides:
+    scale: default
+    train_prop: default
+    sequence_len: default
+    predict_len: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+
+#
+# ------------ GNN.PyTorch
+#
+{}
+
+#
+# ------------ AE.Keras3
+#
+K3AE1:
+  notebook: AE.Keras3/01-Prepare-MNIST-dataset.ipynb
+  overrides:
+    prepared_dataset: default
+    scale: default
+    progress_verbosity: default
+K3AE2:
+  notebook: AE.Keras3/02-AE-with-MNIST.ipynb
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: default
+    latent_dim: default
+    train_prop: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+K3AE3:
+  notebook: AE.Keras3/03-AE-with-MNIST-post.ipynb
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: default
+    train_prop: default
+K3AE4:
+  notebook: AE.Keras3/04-ExtAE-with-MNIST.ipynb
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: default
+    train_prop: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+K3AE5:
+  notebook: AE.Keras3/05-ExtAE-with-MNIST.ipynb
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: default
+    train_prop: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+
+#
+# ------------ VAE.Keras3
+#
+K3VAE1:
+  notebook: VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb
+  overrides:
+    latent_dim: default
+    loss_weights: default
+    scale: default
+    seed: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+K3VAE2:
+  notebook: VAE.Keras3/02-VAE-with-MNIST.ipynb
+  overrides:
+    latent_dim: default
+    loss_weights: default
+    scale: default
+    seed: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: default
+K3VAE3:
+  notebook: VAE.Keras3/03-VAE-with-MNIST-post.ipynb
+  overrides:
+    scale: default
+    seed: default
+    models_dir: default
+
+#
+# ------------ DCGAN.Lightning
+#
+PLSHEEP3:
+  notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
+  overrides:
+    latent_dim: default
+    gan_name: default
+    generator_name: default
+    discriminator_name: default
+    epochs: default
+    lr: default
+    b1: default
+    b2: default
+    batch_size: default
+    num_img: default
+    fit_verbosity: default
+    dataset_file: default
+    data_shape: default
+    scale: default
+    num_workers: default
+
+#
+# ------------ DDPM.PyTorch
+#
+DDPM1:
+  notebook: DDPM.PyTorch/01-ddpm.ipynb
+
+#
+# ------------ Optimization.PyTorch
+#
+OPT1:
+  notebook: Optimization.PyTorch/01-Apprentissages-rapides-et-Optimisations.ipynb
+
+#
+# ------------ DRL.PyTorch
+#
+DRL1:
+  notebook: DRL.PyTorch/FIDLE_DQNfromScratch.ipynb
+DRL2:
+  notebook: DRL.PyTorch/FIDLE_rl_baselines_zoo.ipynb
+
+#
+# ------------ Misc
+#
+NP1:
+  notebook: Misc/00-Numpy.ipynb
+ACTF1:
+  notebook: Misc/01-Activation-Functions.ipynb
+PANDAS1:
+  notebook: Misc/02-Using-pandas.ipynb
+PYTORCH1:
+  notebook: Misc/03-Using-Pytorch.ipynb
+TSB1:
+  notebook: Misc/04-Using-Tensorboard.ipynb
+  overrides: ??
+K3LSTM1:
+  notebook: Misc/05-RNN.ipynb
+PGRAD1:
+  notebook: Misc/06-Gradients.ipynb
+FID1:
+  notebook: Misc/99-Fid-Example.ipynb
+  overrides:
+    scale: default
+    x: default
+    batch_size: default

fidle/ci/gpu-scale1.yml

+campain:
+  version:            '1.0'
+  description:         Notebook test on GPU, with scale=1
+  directory:          ./campains/gpu-scale1
+  existing_notebook:  'remove'    # remove|skip
+  report_template:    'fidle'     # fidle|default
+  timeout:            6000
+
+  environment_vars:
+    FIDLE_SAVE_FIGS:         true
+
+
+#
+# ------------ LinearReg
+#
+LINR1:
+  notebook: LinearReg/01-Linear-Regression.ipynb
+
+GRAD1:
+  notebook: LinearReg/02-Gradient-descent.ipynb
+
+POLR1:
+  notebook: LinearReg/03-Polynomial-Regression.ipynb
+
+LOGR1:
+  notebook: LinearReg/04-Logistic-Regression.ipynb
+
+#
+# ------------ Perceptron
+#
+PER57:
+  notebook: Perceptron/01-Simple-Perceptron.ipynb
+
+#
+# ------------ BHPD.Keras3
+#
+K3BHPD1:
+  notebook: BHPD.Keras3/01-DNN-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+
+K3BHPD2:
+  notebook: BHPD.Keras3/02-DNN-Regression-Premium.ipynb
+  overrides:
+    fit_verbosity: 2
+
+#
+# ------------ BHPD.PyTorch
+#
+PBHPD1:
+  notebook: BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
+
+#
+# ------------ Wine.Keras3
+#
+K3WINE1:
+  notebook: Wine.Keras3/01-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: default
+
+#
+# ------------ Wine.Lightning
+#
+LWINE1:
+  notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: default
+
+#
+# ------------ MNIST.Keras3
+#
+K3MNIST1:
+  notebook: MNIST.Keras3/01-DNN-MNIST.ipynb
+  overrides:
+    fit_verbosity: 2
+
+K3MNIST2:
+  notebook: MNIST.Keras3/02-CNN-MNIST.ipynb
+  overrides:
+    fit_verbosity: 2
+
+#
+# ------------ MNIST.PyTorch
+#
+#PMNIST1:
+#  notebook: MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
+
+#
+# ------------ MNIST.Lightning
+#
+#LMNIST2:
+#  notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
+
+#
+# ------------ GTSRB.Keras3
+#
+K3GTSRB1:
+  notebook: GTSRB.Keras3/01-Preparation-of-data.ipynb
+  overrides:
+    scale: 1
+    output_dir: "{datasets_dir}/GTSRB/enhanced"
+    progress_verbosity: default
+
+K3GTSRB2:
+  notebook: GTSRB.Keras3/02-First-convolutions.ipynb
+  after: K3GTSRB1
+  overrides:
+    enhanced_dir: "{datasets_dir}/GTSRB/enhanced"
+    dataset_name: set-24x24-L
+    batch_size: 64
+    epochs: 5
+    scale: 1
+    fit_verbosity: 2
+
+K3GTSRB3_1:
+  notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
+  after:    K3GTSRB1
+  overrides:
+    enhanced_dir: "{datasets_dir}/GTSRB/enhanced"
+    model_name: model_01
+    dataset_name: set-48x48-RGB
+    batch_size: 64
+    epochs: 5
+    scale: 1
+    fit_verbosity: 2
+
+K3GTSRB3_2:
+  notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
+  after:    K3GTSRB1
+  overrides:
+    enhanced_dir: "{datasets_dir}/GTSRB/enhanced"
+    model_name: model_02
+    dataset_name: set-48x48-RGB
+    batch_size: 64
+    epochs: 5
+    scale: 1
+    fit_verbosity: 2
+
+#
+# ------------ Embedding.Keras3
+#
+K3IMDB1:
+  notebook: Embedding.Keras3/01-One-hot-encoding.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: 2
+
+K3IMDB2:
+  notebook: Embedding.Keras3/02-Keras-embedding.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    output_dir: default
+    fit_verbosity: 2
+
+K3IMDB3:
+  notebook: Embedding.Keras3/03-Prediction.ipynb
+  after:    K3IMDB2
+  overrides:
+    vocab_size: default
+    review_len: default
+    saved_models: default
+    dictionaries_dir: default
+
+K3IMDB4:
+  notebook: Embedding.Keras3/04-Show-vectors.ipynb
+  after:    K3IMDB2
+  overrides:
+    vocab_size: default
+    review_len: default
+    saved_models: default
+    dictionaries_dir: default
+
+K3IMDB5:
+  notebook: Embedding.Keras3/05-LSTM-Keras.ipynb
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    fit_verbosity: 2
+    scale: 1
+
+#
+# ------------ RNN.Keras3
+#
+K3LADYB1:
+  notebook: RNN.Keras3/01-Ladybug.ipynb
+  overrides:
+    scale: 1
+    train_prop: 0.8
+    sequence_len: 20
+    predict_len: 5
+    batch_size: 32
+    epochs: 10
+    fit_verbosity: 2
+
+#
+# ------------ AE.Keras3
+#
+K3AE1:
+  notebook: AE.Keras3/01-Prepare-MNIST-dataset.ipynb
+  overrides:
+    prepared_dataset: default
+    scale: 1
+    progress_verbosity: 2
+
+K3AE2:
+  notebook: AE.Keras3/02-AE-with-MNIST.ipynb
+  after: K3AE1
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    latent_dim: 10
+    train_prop: default
+    batch_size: default
+    epochs: 20
+    fit_verbosity: 2
+    
+K3AE3:
+  notebook: AE.Keras3/03-AE-with-MNIST-post.ipynb
+  after: K3AE2
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: default
+    train_prop: default
+
+K3AE4:
+  notebook: AE.Keras3/04-ExtAE-with-MNIST.ipynb
+  after: K3AE1
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    train_prop: default
+    batch_size: default
+    epochs: 20
+    fit_verbosity: 2
+
+K3AE5:
+  notebook: AE.Keras3/05-ExtAE-with-MNIST.ipynb
+  after: K3AE1
+  overrides:
+    prepared_dataset: default
+    dataset_seed: default
+    scale: 1
+    train_prop: default
+    batch_size: default
+    epochs: 30
+    fit_verbosity: 2
+
+#
+# ------------ VAE.Keras3
+#
+K3VAE1:
+  notebook: VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb
+  overrides:
+    latent_dim: default
+    loss_weights: default
+    scale: 1
+    seed: default
+    batch_size: default
+    epochs: 20
+    fit_verbosity: 2
+
+K3VAE2:
+  notebook: VAE.Keras3/02-VAE-with-MNIST.ipynb
+  overrides:
+    latent_dim: default
+    loss_weights: default
+    scale: 1
+    seed: default
+    batch_size: default
+    epochs: 20
+    fit_verbosity: 2
+
+K3VAE3:
+  notebook: VAE.Keras3/03-VAE-with-MNIST-post.ipynb
+  after: K3VAE2
+  overrides:
+    scale: 1
+    seed: default
+    models_dir: default
+
+#
+# ------------ DCGAN.Lightning
+#
+PLSHEEP3_1:
+  notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
+  overrides:
+    latent_dim: default
+    gan_name:           GAN
+    generator_name:     Generator_2
+    discriminator_name: Discriminator_3
+    epochs: 30
+    lr: default
+    b1: default
+    b2: default
+    batch_size: 64
+    num_img: default
+    fit_verbosity: default
+    dataset_file: default
+    data_shape: default
+    scale: 1
+    num_workers: 2
+
+PLSHEEP3_2:
+  notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
+  overrides:
+    latent_dim: default
+    gan_name:           WGANGP
+    generator_name:     Generator_2
+    discriminator_name: Discriminator_3
+    epochs: 30
+    lr: default
+    b1: default
+    b2: default
+    batch_size: 64
+    num_img: default
+    fit_verbosity: default
+    dataset_file: default
+    data_shape: default
+    scale: 1
+    num_workers: 2
+
+#
+# ------------ Misc
+#
+NP1:
+  notebook: Misc/00-Numpy.ipynb
+
+ACTF1:
+  notebook: Misc/01-Activation-Functions.ipynb
+
+PANDAS1:
+  notebook: Misc/02-Using-pandas.ipynb
+
+FID1:
+  notebook: Misc/99-Fid-Example.ipynb
+  overrides:
+    scale: 1
+    x: 345
+    batch_size: default

fidle/css/custom.css

+<style>
+
+div.warn {    
+    background-color: #fcf2f2;
+    border-color: #dFb5b4;
+    border-left: 5px solid #dfb5b4;
+    padding: 0.5em;
+    font-weight: bold;
+    font-size: 1.1em;;
+    }
+
+
+
+div.nota {    
+    background-color: #DAFFDE;
+    border-left: 5px solid #92CC99;
+    padding: 0.5em;
+    }
+
+div.todo:before { content:url(data:image/svg+xml;base64,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);
+    float:left;
+    margin-right:20px;
+    margin-top:-20px;
+    margin-bottom:20px;
+}
+div.todo{
+    font-weight: bold;
+    font-size: 1.1em;
+    margin-top:40px;
+}
+div.todo ul{
+    margin: 0.2em;
+}
+div.todo li{
+    margin-left:60px;
+    margin-top:0;
+    margin-bottom:0;
+}
+
+div .comment{
+    font-size:0.8em;
+    color:#696969;
+}
+
+
+
+</style>
+

fidle/css/readme.css

+<style>
+
+.fid_line{
+    padding-top: 10px
+}
+
+.fid_id {    
+    font-size:1.em;
+    color:black;
+    font-weight: bold; 
+    padding:0px;
+    margin-left: 20px;
+    display: inline-block;
+    width: 60px;
+    }
+
+.fid_desc {    
+    font-size:1.em;
+    padding:0px;
+    margin-left: 85px;
+    display: inline-block;
+    width: 600px;
+    }
+
+
+
+div.fid_section {    
+    font-size:1.2em;
+    color:black;
+    margin-left: 0px;
+    margin-top: 12px;
+    margin-bottom:8px;
+    border-bottom: solid;
+    border-block-width: 1px;
+    border-block-color: #dadada;
+    width: 700px;
+    }
+
+</style>
\ No newline at end of file

fidle/img/00-Datasets-tar.svg

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\ No newline at end of file

fidle/img/00-Fidle-pdf.svg

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\ No newline at end of file

fidle/img/00-Mail_contact.svg

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fidle/img/00-Notebooks.svg

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