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watch -n1 nvidia-smi

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scale_cols = ['Time','Amount']
scaler = StandardScaler()

# fit scaler
scaler.fit(train_df[scale_cols].to_numpy())

# make copies of dataframes
train_df_ = train_df.copy()
val_df_ = val_df.copy()
test_df_ = test_df.copy()

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import qrcode

qr = qrcode.QRCode(version=3, box_size=15, border=15) #box_size is QR dimension and border is thickness

data = "https:~/~/www.amazon.com"

qr.add_data(data)

image = qr.make_image(fill='black', back_color='white')

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# from - https://gitlab.com/juliensimon/aim410/-/blob/master/aim410.ipynb

# Number of GPUs on this machine
%env SM_NUM_GPUS=0
# Where to save the model
%env SM_MODEL_DIR=/tmp/model
# Where the training data is
%env SM_CHANNEL_TRAINING=data
# Where the validation data is
%env SM_CHANNEL_VALIDATION=data

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import os
import shutil
import tarfile
from collections import defaultdict
import logging

# Setup basic logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

def copy_and_compress(src_directory, temp_directory, max_types, max_size):