Gclabbe

## to get Nvidia's Docker container with Jupyter Notebooks and GPU support
> docker run --gpus all -it --rm -v $(realpath path to your work):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter

## New requirements-gpu.txt
opencv-python
lxml
tqdm

-e .

Gclabbe

clusters = {}

n = len(column_values)

for i in range(n):
    if column_values[i] not in clusters:
        clusters[column_values[i]] = {'count': 0, 'purch': 0}

    clusters[column_values[i]]['count'] += 1

Gclabbe

windoze = True

tb_base = '\\project-tensorboard\\' if windoze else '/project-tensorboard/'
tb_log = 'log-1'

# Path to save the embedding and checkpoints generated
try:
    os.mkdir(PATH + tb_base)
except:
    pass

Gclabbe

Removed all contents from this document in favor of a GitHub repo with updated descriptions and support files to automate setup
https://github.com/Gclabbe/4BAI_setup

Gclabbe

def get_features_and_targets(df, scale_y=None):
    df = df.drop(columns=['MSRP'])
    
    counts = df.pivot_table(columns=list(df.columns), aggfunc='size')

    X = counts.index 
    Y = counts.values[:][:, None] 
    
    if scale_y != None:
        if scale_y == 'by_volume':