https://www.youtube.com/watch?v=dj-Jntz-74g

https://github.com/jeffheaton/t81_558_deep_learning/tree/master/install

Setting up the Python Environment

Conda allows us to set up a defined environment with specific versions of the python version as well as of the packages.

conda create -n jh_class python=3.10

Then activate this environment before installing packages!

conda activate jh_class

Install specific packages:

conda install -c conda-forge tensorflow=2.15.0 -y
conda install -c conda-forge pandas=2.2.0 -y
conda install -c conda-forge scikit-learn=1.4.1 -y
conda install -c conda-forge matplotlib=3.8.3 -y
conda install -c conda-forge ipython=8.21.0 -y
conda install -c conda-forge transformers=4.37.2 -y
conda install -c conda-forge jupyter=1.0.0 -y
conda install -c conda-forge sentencepiece=0.1.99 -y
conda install -c conda-forge click=8.0.4 -y

This command lists the package versions that are installed in the currently active environment:

conda list

Result:
Computer without GPU
Computer with GPU

Install packages (without version number):

conda install -c conda-forge imageio -y
conda install -c conda-forge keras-preprocessing -y
conda install -c conda-forge pydot -y

Installing YOLO

https://github.com/ultralytics/yolov5

mkdir not_on_github
cd not_on_github
git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt
pip install ninja

This will also install PyTorch which will be used in later examples.

Installing StyleGAN

The class module of class 07_2 uses stylegan2, however the environment did not work with the RTX 3090 card.
Instead, I am using the stylegan3 examples from
https://github.com/NVlabs/stylegan3

mkdir not_on_github
cd not_on_github
git clone https://github.com/NVlabs/stylegan3.git
cd stylegan3
conda env create -f environment.yml
conda install ipython

Installing tabgan

Adding tabgan to jh_class will corrupt the jh_class environment such that the class programs will not run any longer,

It is better to create a separate conda environment for the tabgan module 7.5.

Class

To continue the class:
Open terminal

cd ~/github/learn/ai/jheaton/t81_558_deep_learning

conda activate jh_class

jupyter notebook

t81_558_class_01_1_overview.ipynb..........................| 2024-02-12 t81_558_class_01_2_intro_python.ipynb......................| 2024-02-12 t81_558_class_01_3_python_collections.ipynb................| 2024-02-13 t81_558_class_01_4_python_files.ipynb......................| 2024-02-20 t81_558_class_01_5_python_functional.ipynb.................| 2024-02-21 t81_558_class_02_1_python_pandas.ipynb.....................| 2024-02-22 t81_558_class_02_2_pandas_cat.ipynb........................| 2024-02-24 t81_558_class_02_3_pandas_grouping.ipynb...................| 2024-02-25 t81_558_class_02_4_pandas_functional.ipynb.................| 2024-02-25 t81_558_class_02_5_pandas_features.ipynb...................| 2024-02-26 t81_558_class_03_1_neural_net.ipynb........................| 2024-02-26 t81_558_class_03_2_keras.ipynb.............................| 2024-03-06 t81_558_class_03_3_save_load.ipynb.........................| 2024-03-06 t81_558_class_03_4_early_stop.ipynb........................| 2024-03-06 t81_558_class_03_5_weights.ipynb...........................| 2024-03-07 t81_558_class_04_1_feature_encode.ipynb....................| 2024-03-08 t81_558_class_04_2_multi_class.ipynb.......................| 2024-03-12 t81_558_class_04_3_regression.ipynb........................| 2024-03-13 t81_558_class_04_4_backprop.ipynb..........................| 2024-07-12 t81_558_class_04_5_rmse_logloss.ipynb......................| 2024-07-12 t81_558_class_05_1_reg_ridge_lasso.ipynb...................| 2024-09-19 t81_558_class_05_2_kfold.ipynb.............................| 2024-10-27 t81_558_class_05_3_keras_l1_l2.ipynb.......................| 2024-11-18 t81_558_class_05_4_dropout.ipynb...........................| 2024-11-19 t81_558_class_05_5_bootstrap.ipynb.........................| 2024-12-06 t81_558_class_06_1_python_images.ipynb.....................| 2024-12-08 t81_558_class_06_2_cnn.ipynb...............................| 2024-12-27 t81_558_class_06_3_resnet.ipynb............................| 2024-12-31 t81_558_class_06_4_keras_images.ipynb......................| 2025-01-13 t81_558_class_06_5_yolo.ipynb..............................| 2025-01-14 t81_558_class_07_1_gan_intro...............................| 2025-01-18 t81_558_class_07_2_train_gan.ipynb.........................| 2025-01-24 t81_558_class_07_3_latent_vector.ipynb.....................| 2025-01-24 t81_558_class_07_4_deoldify.ipynb..........................| 2025-01-25 t81_558_class_07_5_tabular_synthetic.ipynb.................| 2025-01-28 t81_558_class_08_1_kaggle_intro.ipynb......................| t81_558_class_08_2_keras_ensembles.ipynb...................| t81_558_class_08_3_keras_hyperparameters.ipynb.............| t81_558_class_08_4_bayesian_hyperparameter_opt.ipynb.......| t81_558_class_08_5_kaggle_project.ipynb....................| t81_558_class_09_1_keras_transfer.ipynb....................| t81_558_class_09_2_keras_xfer_cv.ipynb.....................| t81_558_class_09_3_transfer_nlp.ipynb......................| t81_558_class_09_4_facial_points.ipynb.....................| t81_558_class_09_5_style_transfer.ipynb....................| t81_558_class_10_1_timeseries.ipynb........................| t81_558_class_10_2_lstm.ipynb..............................| t81_558_class_10_3_text_generation.ipynb...................| t81_558_class_10_4_intro_transformers.ipynb................| t81_558_class_10_5_keras_transformers.ipynb................| t81_558_class_11_01_huggingface.ipynb......................| t81_558_class_11_02_tokenizers.ipynb.......................| t81_558_class_11_03_hf_datasets.ipynb......................| t81_558_class_11_04_hf_train.ipynb.........................| t81_558_class_11_05_embedding.ipynb........................| t81_558_class_12_01_ai_gym.ipynb...........................| t81_558_class_12_02_qlearningreinforcement.ipynb...........| t81_558_class_12_03_keras_reinforce.ipynb..................| t81_558_class_12_04_atari.ipynb............................| t81_558_class_12_05_apply_rl.ipynb.........................| t81_558_class_13_01_flask.ipynb............................| t81_558_class_13_02_checkpoint.ipynb.......................| t81_558_class_13_03_web.ipynb..............................| t81_558_class_13_04_retrain.ipynb..........................| t81_558_class_13_05_tpu.ipynb..............................| t81_558_class_14_01_automl.ipynb...........................| t81_558_class_14_02_auto_encode.ipynb......................| t81_558_class_14_03_anomaly.ipynb..........................| t81_558_class_14_04_ids_kdd99.ipynb........................| t81_558_class_14_05_new_tech.ipynb.........................|

Software version issues

Installation in February 2024 installs tensorflow 2.15.0.
t81_558_class_01_1_overview.ipynb calls tensorflow.keras.__version
However, this attribute was deprecated after tensorflow 2.13.0,
so leads to an error.
https://stackoverflow.com/questions/46086030/how-to-check-which-version-of-keras-is-installed
This can be resolved by running
conda install tensorflow=2.1.0
in the according environment in which this notebook is run.

Other

The syntax for version numbers in pip is
pip install tensorflow==2.15.0
but we don't want to install anything outside of conda,
so that we know that we are using the intended versions of our packages.

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