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from glob import glob
from PIL import Image
import os
  
directory_where_existing_images_are = '/Users/your_username/Projects/Homework'
directory_where_you_want_jpegs = '/Users/your_username/Projects/Homework_but_jpegs'

images_to_convert = glob(os.path.join(directory_where_existing_images_are, '*.png'))

for image in images_to_convert:

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import plotly.graph_objects as go
from plotly.subplots import make_subplots
import datapane as dp 
import numpy as np
import pandas as pd

df = pd.read_csv('../data/sha256_leading_zero_time_to_solve.csv')
df['Time'] = pd.to_timedelta(df['Time']) / np.timedelta64(1, 'm')

fig = make_subplots(specs=[[{"secondary_y": True}]])

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from datetime import datetime
import hashlib

leading_zeros = input("\nHow many leading zeros?: ")
starting_phrase = "I\'m going to share this article!"
must_start_with = '0' * int(leading_zeros)
hash = starting_phrase
attempts = 0
starting_time = datetime.now()
while not hash.startswith(must_start_with):

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from sagemaker.pytorch.model import PyTorchModel

pytorch_model = PyTorchModel(
	model_data=f'{bucket}/models/model.tar.gz',
	role=role,
	source_dir='code',
	entry_point='inference.py',
	py_version='1.6.0',
	framework_version='1.6.0',
)

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AWS_REGION={YOUR AWS REGION} tensorboard --logdir s3://{BUCKET}/tb_logs

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from sagemaker.pytorch import PyTorch

estimator = PyTorch(
	entry_point='train_t5_model.py',
	source_dir='code',
	role=role,
	framework_version='1.6.0',
	instance_count=1,
	instance_type='ml.p3.2xlarge',
	output_path=s3_output_location,

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# in our training loop

	trainer_params = {
		'max_epochs': int(args.epochs),
		'default_root_dir': args.output_data_dir,
		'gpus': int(args.gpus),
		'logger': tb_logger,
		'early_stop_callback': early_stop,
		'checkpoint_callback': model_checkpoint,
		'callbacks': [lr_logger]