Diller Digital - Environment Setup for Deep Learning for Scientists & Engineers

Download and install Python

If you don’t have Python on your system, download and install version 3.11 or later now. Note: in Windows, be sure to check the “Add python.exe to PATH” box in the installer. Otherwise, you’ll need to type the complete path to python.exe when creating the virtual environment.
Create a Python virtual environment in your project folder using the commands below based on your particular machine:

MacOS

In a Terminal window, navigate to the directory where you want to store your environment (this is typically the project folder where your files and code are) type following commands (i.e. the text after the $):

$ python3 -m venv deep_venv
$ source deep_venv/bin/activate
(deep_venv) $ python -m pip install -U pip
(deep_venv) $ pip install tensorflow scikit-learn jupyterlab seaborn ipywidgets ipympl

Linux/Unix:

In a Terminal window, navigate to the directory where you want to store your environment (this is typically the project folder where your files and code are) type following commands (i.e. the text after the $):

$ python -m venv deep_venv
$ source deep_venv/bin/activate
(deep_venv) $ python -m pip install -U pip
(deep_venv) $ pip install tensorflow scikit-learn jupyterlab seaborn ipywidgets ipympl

On Windows

In a PowerShell terminal, navigate to the directory where you want to store your environment (this is typically the project folder where your files and code are) type the following commands (i.e. the text after the >):

C:\Users\you\Enthought_DEEP> python -m venv deep_venv
C:\Users\you\Enthought_DEEP> .\deep_venv\Scripts\Activate.ps1
(deep_venv)C:\Users\you\Enthought_DEEP> python -m pip install -U pip
(deep_venv)C:\Users\you\Enthought_DEEP> pip install tensorflow scikit-learn jupyterlab seaborn ipywidgets ipympl

Test Your Installation

Launch Jupyter lab from a command line

jupyter lab

Open a new Python 3 Notebook (use the blue button with a white +)

launcher button

Paste the following code into a cell and run it with Shift-Enter:

import tensorflow as tf

print(f'TensorFlow version: {tf.__version__}')
gpus = tf.config.list_physical_devices('GPU')
gpus = gpus[0] if gpus else None
print(f'List GPUS: {gpus}')
print('-' * 100)
print()

# Cache the dataset locally (relative to ~/.keras/datasets).
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Rescale from [0, 255] to [0, 1]
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential(
    [
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax'),
    ],
    name='simple_model_1',
)

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['sparse_categorical_accuracy'],
)

model.fit(x_train, y_train, epochs=3, batch_size=32)
print('Done.')