<jupyter_start><jupyter_text>Running Jupyter and the [lazyqt_demo_notebooks](https://github.com/CamHD-Analysis/lazyqt_demo_notebooks) on a Google Compute Engine instance.   Machine is _n1-highcpu-4 (4 vCPUs, 3.6 GB memory)_ running Ubuntu 16.04LTS.   Manually installed Miniconda, and from there installed Jupyter and dependencies.Ran the last block from [Lazycache Benchmarking](https://github.com/CamHD-Analysis/lazyqt_demo_notebooks/blob/master/Lazycache%20Benchmarking.ipynb) repeatedly to warm the cache.  Got variable results as I continued to hit the cache.  Unfortunately, no amazing results even "across the datacenter" between the two instances.As with other data sets, results are the mean wall clock time to retrieve a random frame from one video.   The randomizer is seeded consistently so results are repeated.   These tests used a different random seed from previous testing.<jupyter_code>%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(9, 9))
data = pd.Series([2684.393597,503.570867,246.178055,237.999845,193.861699,178.219604,160.611200,151.524568,222.419190,185.088372,162.946749,147.112799,221.965265,140.543318])
plt = data.plot()
plt.set_xlabel( 'iteration' )
plt.set_ylabel( 'mean time per frame (ms)' )<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08987915407854985, 'fraction_numerical': 0.10574018126888217, 'mean_word_length': 5.829896907216495, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '573942', 'license': 'MIT', 'n_tokens_mistral': 522, 'n_tokens_neox': 432, 'n_words': 132, 'path': 'analysis/Lazycache GAE to GCE Testing.ipynb', 'repo_name': 'CamHD-Analysis/jupyter-lazyqt-benchmarking', 'size': '1324'}