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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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Date(UTC) | UnixTimeStamp | Value (Wei) | |
---|---|---|---|
7/30/2015 | 1438214400 | 0 | |
7/31/2015 | 1438300800 | 0 | |
8/1/2015 | 1438387200 | 0 | |
8/2/2015 | 1438473600 | 0 | |
8/3/2015 | 1438560000 | 0 | |
8/4/2015 | 1438646400 | 0 | |
8/5/2015 | 1438732800 | 0 | |
8/6/2015 | 1438819200 | 0 | |
8/7/2015 | 1438905600 | 604684154870 |
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Date(UTC) | UnixTimeStamp | No. of Verified Contracts | |
---|---|---|---|
2015-07-30 | 1438214400 | 0 | |
2015-07-31 | 1438300800 | 0 | |
2015-08-01 | 1438387200 | 0 | |
2015-08-02 | 1438473600 | 0 | |
2015-08-03 | 1438560000 | 0 | |
2015-08-04 | 1438646400 | 0 | |
2015-08-05 | 1438732800 | 0 | |
2015-08-06 | 1438819200 | 0 | |
2015-08-07 | 1438905600 | 0 |
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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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Number of Leading Zeros | Attempts | Time | |
---|---|---|---|
1 | 13 | 0:00:00.000145 | |
2 | 150 | 0:00:00.00609 | |
3 | 978 | 0:00:00.003031 | |
4 | 43715 | 0:00:00.073373 | |
5 | 3635323 | 0:00:02.941581 | |
6 | 12298127 | 0:00:09.910860 | |
7 | 538403472 | 0:07:56.945654 | |
8 | 1542635643 | 0:21:43.033508 |
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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] |
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