// expand each companies to see full details and all postings
const toggleDetails = document.getElementsByClassName("checkbox-inline")[0]
toggleDetails.click()

// automated scrolling, run this until it gets to the end
const scroll = setInterval(() => {window.scrollTo(0,document.body.scrollHeight);}, 3000)

// when it no longer scrolls, clear the interval
clearInterval(scroll)

// expand each job listing:
const jobs = document.getElementsByClassName("job-name")
for (let job of jobs) {
    job.click()
}

// now Ctrl+S to save the HTML and images

# pseudo code for parsing data
html = open("data.html")
parsed_html = parseHtml(html)

companies = []
for company in parsed_html.find_all("company")
    # company stats, founders and jobs
    company_details = extract_company_details(company)
    companies.append(company_details)

with open("output.json", "wb") as f:
    f.write(json.dumps(companies))

[
    {
        "company_name": "Startup A",
        "logo": "logo.png",
        "jobs": [
            {
                "title": "Software Engineer",
                "skills": ["python", "javascript"],
                "salary": {
                    "min": 90000,
                    "max": 110000,
                    "avg": 100000
                }
            }
        ],
        "founders": [
            {
                "name": "Founder Name",
                "linkedin": "https://linkedin.com/founder",
                "education": "University A",
                "image": "abc.png"
            }
        ]
    }
]

skills = []
for company in company_list:
    if company["jobs"] is not None:
        for job in company["jobs"]:
            if job["job_skills"] is not None:
                for skill in job["job_skills"]:
                    skills.append(skill)

top_skills = Counter(skills).most_common()
print(top_skills)

[
 ('JAVASCRIPT', 330), # I have been using JS a lot recently with Vue
 ('REACT', 323), # As a prefer to use Vue, I haven't used React in a while
 ('PYTHON', 312), # I'm a big Python fan, it was the first language I touched, happy to see it near the top!
 ('AMAZON WEB SERVICES (AWS)', 200), # I like AWS a lot. I have really been enjoying using CDK to build infrastructure
 ('NODE.JS', 195), # I would to do more with node this year. I generally use Python for web apps
 ('POSTGRESQL', 132), # I have used Postgres ever since I started using Django and like it a lot
 ('TYPESCRIPT', 114), # this is another goal of mine for 2021, it seems like an inevitability
 ('JAVA', 79), # I have never used Java
 ('SQL', 74), # I usually don't write my own SQL queries; I view SQL through the lense of an ORM
 ('RUBY ON RAILS', 72), # I haven't used RoR
 ('CSS', 71), # I like CSS Frameworks. Recently I'm into Tailwind and Material UI. This site uses Tailwind
 ('HTML', 71),
 ('DOCKER', 66), # I am a big container fan! It is my preferred way to run software, locally and in the cloud
 ('KUBERNETES', 58), # I read the Manning book on k8s. I prefer ECS or Swarm, but I might try using it more
 ('GO', 58), # I haven't ever used Go, but it doesn't look too bad coming from Python
 ('REACT NATIVE', 58),
 ('C++', 55), # Also haven't used this, but I have a book on it
 ('GRAPHQL', 48), # I tried GraphQL and built a HN clone in Django. I prefer REST but I get the appeal (for frontend developers)
 ('GOOGLE CLOUD', 46), # I'm not a big GCP as I mostly use AWS and Digital Ocean but I would like to try Cloud Run
 ('RUBY', 44), # I haven't used it
 ('DJANGO', 44), # Django is my go-to tool for building web apps and APIs. I love the admin, ORM and DRF
 ('MACHINE LEARNING', 44), # I am familiar with some ML techniques but not very well practiced
 ('MONGODB', 43), # Have used it before, but I try to use the postgres JSONField for storing NoSQL data when possible
 ('IOS', 38), # I have an iPhone, but haven't used a Mac in a long time, mostly on Linux and Windows
 ('MYSQL', 36), # I tend to use Postgres, I don't think I've ever used this
 ('ANDROID', 35), # Not something I have worked with
 ('DATA ANALYTICS', 32), # I do a lot of this
 ('GIT', 30), # I'm very slowly trying to learn advanced git features. I have many abandoned "rebase-practice" repos
 ('ANGULAR', 29), # I tried it once for about an hour, I know that people love to hate it, I'm just not sure why
 ('SWIFT', 29), # This is apparently a very popular language and has use cases outside of mobile development, but I've never used it
 ('LINUX', 28), # I spend lot of time using Linux machines, mostly Ubuntu.
 ('SOFTWARE ARCHITECTURE', 24), # I like using diagrams.net to draw application infrastructure
 ('KOTLIN', 23), # I think this is a framework for Java/Android?
 ('TENSORFLOW', 22), # Google DL/ML library that I'll try to use later in this article
 ('DISTRIBUTED SYSTEMS', 22), # Using AWS, I guess I have technically designed distributed systems but I wouldn't call it one of my skills
 ('PHP', 22), # I almost learned it to support a WordPress site but opted to use JAMStack instead
 ('DATA WAREHOUSING', 22), # I have used Google BigQuery before which I think counts for this skill
 ('DEEP LEARNING', 20), # I'm going to try to use this later in this article
 ('DATA MODELING', 20), # To me this means writing Django models, or thinking about how to structure an API/json data, etc.
 ('C#', 19), # Micrsoft language used for different things including game dev with Unity
 ('FLASK', 19), # I'm familiar with it but mostly prefer Django's batteries included philosophy
 ('C', 19), # In learning about Linux I have read a bit of C, but never written any
 ('REDIS', 18), # Fast, in memory key-value store with multiple data types. I use it for a few different things
 ('MICROSERVICES', 18), #
 ('COMPUTER VISION', 17), # I once used OpenCV on my Raspberry Pi
 ('EXPRESS', 15), # I'd like to learn how to use this in 2021
 ('BASH/SHELL', 13), # I'm not very fluent in bash but
 ('OBJECTIVE-C', 13), # I haven't used this but I know it is popular for iOS development
 ('FIREBASE', 12), # I have played around with Firebase, but I haven't built anything with it
 ('SCALA', 11), # Functional programming language for JVM, I haven't used it
 ('SOFTWARE SECURITY', 11),
 ('UNITY', 11), # I used this once before to play around with VR development for HTC Vive
 ('R', 11), # I haven't used R before, and I would probably reach for a Python library for doing statistics or ML-related things
 ('KAFKA', 10), # I haven't used it but I'm familiar with the ideas behind Kafka.
 ('SPARK', 10), # I haven't used it and I'm not really sure what it is
 ('ELASTICSEARCH', 10), # I haven't used it before
 ('ETL', 10), # Extract, Transform and Load
 ('NATURAL LANGUAGE PROCESSING', 10),
 ('HEROKU', 10), # I used this when first learning about Django, haven't used it in a while
 ('NGINX', 9), # I use NGINX in most of my web apps as a reverse proxy
 ('JENKINS', 9), # I haven't used it, I am a big GitLab fan and will use that whenever possible
 ('RUST', 9), # I have read the Rust book and have played around with WASM
 ('IMAGE PROCESSING', 8),
 ('SERVERLESS', 8), # I currenty use Fargate and have also used Lambda and SQS and some other serverless AWS tools
 ('BLOCKCHAIN', 8),
 ('OPENCV', 8),
 ('CAD DESIGN', 7), # I am a big fan of SketchUp and I'm familiar with Blender as well, but I'm not sure if these qualify as CAD design
 ('JQUERY', 7), # It was the first library I worked with when I started learning javascript
 ('HADOOP', 6), # It is related to map-reduce, I haven't used it before and I don't really know what it is. I know it is related to HDFS
 ('.NET CORE', 6), # I haven't used it
 ('TCP/IP', 6), # I'm familiary with the basics
 ('ELIXIR', 6), # I don't know, I think it is a framework for Erlang
 ('INTERNET OF THINGS (IOT)', 5),
 ('SASS', 5), # I think it is a framework for CSS. I frequently see node-sass errors from npm
 ('OPENGL', 5),
 ('DYNAMODB', 5), # I am familiar with it but haven't used it
 ('GOOGLE APP ENGINE', 5), # I haven't used it before
 ('UNIX', 4),
 ('SPRING FRAMEWORK', 4), # A Java web framework that I haven't used
 ('CUDA', 4), # I have used it indirectly when I used nvidia-docker to used Tensorflow
 ('DART', 4), # I don't know what this is
 ('ERLANG', 4), # A language that handles concurrency very well
 ('RABBITMQ', 4), # Message queue, I have used it before but tend to use Redis as a message broker
 ('KERAS', 4), # A helper/wrapper library for Tensorflow
 ('SCSS', 4), # I think this is a language that compiles to CSS. CSS frameworks that I use use this
 ('ML', 4), # I've read some books and experimented but I'm not a regular practitioner
 ('MATLAB', 4), # A tool her programming with higher math
 ('SPRING', 4), # A Java web framework. Not sure if different from Spring Boot. Never used it.
 ('CASSANDRA', 4), # FB scalable database (NoSQL I think?) that does sharding really well
 ('HIVE', 3), # Not sure what hive is. I think it related to Hadoop
 ('PUPPET', 3), # Configuration management tool that I haven't ever used
 ('REDSHIFT', 3), # AWS version of Google BigQuery
 ('SQL SERVER', 3), # Not sure what this refers to, specifically.
 ('GROOVY', 3), # I think this is a Java framework?
 ('VERILOG', 3), # I've never heard of this.
 ('TORCH/PYTORCH', 3), # FB python deep learning library.
 ('CLOJURE', 3), # A LISP derivate, functional language
 ('MICROSOFT AZURE', 3), # I've used Azure AD and thats it.
 ('HBASE', 2), # I don't know what this is
 ('RDS/AURORA', 2), # I use RDS and experimented with Aurora but don't know when/why to use it
 ('FIRMWARE', 2), # What's between hardware and software
 ('ABAP', 2), # I don't know
 ('ARDUINO', 2), # I have one, but don't use it
 ('MICROCONTROLLERS', 2), # Arduino might be an example of what this is
 ('SOLIDITY', 2), # Don't know what this is
 ('UNREAL ENGINE', 2), # Unity competitor, used for game development
 ('COFFEESCRIPT', 2), # I think it is a dialect of JS, but I'm not sure
 ('LUA', 2), # I think this is what redis is written in, but I'm not sure how to descbribe what it is
 ('MACOS', 2), # I haven't used MacOS in a long time. I'm tempted to try M1, but I also want to buid a new PC...
 ('NEO4J', 2), # A graph database, I'm not sure what a typical use case is for this
 ('INFORMATION SECURITY', 2), # Unknown unknowns
 ('REINFORCEMENT LEARNING (RL)', 2), # Not sure what this refers to specifically
 ('DEVICE DRIVERS', 2), # Probably involves writing kernel modules
 ('EMBEDDED LINUX', 2), # Not sure if Raspberry Pi is an example of this or not
 ('ELASTIC STACK (ELK)', 1), # Useful for viewing log data (Elastic, Logstash and Kibana), haven't used it
 ('IIS', 1), # I don't know
 ('ORACLE', 1), # A big software company and a proprietary database (I think Django supports it)
 ('F#', 1), # A programming language that I don't know anything about
 ('SQLITE', 1), # A light-weight SQL-compatible file-based database
 ('HASKELL', 1), # A functional programming language that I don't know
 ('SCHEME', 1), # I'm not sure, it may be something related to LISP
 ('MS SQL', 1), # Never used this
 ('MARIADB', 1), # An open source SQL database
 ('MAVEN', 1), # I think it is a Java Framework
 ('SEARCH', 1), # I don't know what this refers to
 ('OCAML', 1), # I think I once read that high-frequency traders like to use this language, but I'm not sure why
 ('JULIA', 1), # A programming language used for math and statistics
 ('GPU PROGRAMMING', 1), # I haven't done this before, probably uses C++
 ('HACK', 1), # FB's version of PHP
 ('XAMARIN', 1), # I dont't know what this is
 ('CORDOVA', 1), # I think it is a tool for generating native apps from JS
 ('SAS', 1), # I don't know what this is
 ('ASSEMBLY', 1), # Low level language that gives instructions to CPU
 ('XML', 1), # A data format, I use it for this site's sitemap and RSS feed
 ('MEMCACHED', 1), # Used for caching. I haven't used it; I typically use redis where this might be an option
 ('LESS', 1), # I think is is related to CSS?
 ('AMAZON ECHO', 1) # I once built an open source Echo on a raspberry pi
]

skills_frequency = defaultdict(lambda: defaultdict(lambda: 0))

for company in company_list:
    if company["jobs"] is not None:
        for job in company["jobs"]:
            if job["job_skills"] is not None:
                job_skills = job["job_skills"]

                skill_tuples = itertools.permutations(job_skills, 2)

                for skill_tuple in skill_tuples:
                    first = skill_tuple[0]
                    second = skill_tuple[1]

                    skills_frequency[first][second] += 1

pprint.pprint(
    {
        key: sorted(value.items(), key=lambda kv: -kv[1])
        for key, value in skills_frequency.items()
    }
)

import json
import random

from collections import Counter
from os import path

import matplotlib.pyplot as plt
import numpy as np

from PIL import Image
from wordcloud import WordCloud, STOPWORDS

HTML_FILE = "waas_data.json"
with open(HTML_FILE, 'r') as j:
     company_list = json.loads(j.read())

company_names = [company.get("company_name", " ").lower() for company in company_list]
company_description_list = [company.get("company_desc", " ").lower().replace(".", "") for company in company_list]
company_descriptions = " ".join(company_description_list)

wc = WordCloud(background_color="white", width=1920, height=1080, max_words=500, stopwords=STOPWORDS, margin=10,
               random_state=1).generate(company_descriptions)

default_colors = wc.to_array()

plt.figure(figsize=(40, 40))
plt.imshow(wc, interpolation="bilinear")

plt.axis("off")
plt.savefig('company_description_wc.png')
plt.show()

import os
import PIL
from PIL import Image
from IPython.display import display, Image as IPyImage
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline

LOGO_DIR = 'data/waas_full_details_dump_files/'
yc_logos = [LOGO_DIR + x for x in os.listdir(LOGO_DIR) if x.endswith('.png')]

def average_img_hex(img):
    """
    https://www.hackzine.org/getting-average-image-color-from-python.html
    """
    img = Image.open(img)

    # leave out images not in RGB/RGBA mode
    if img.mode in ["LA", "P", "L"]:
        return

    # resize the image to 1 pixel and get the average hex value
    img2 = img.resize((1, 1))
    color = img2.getpixel((0, 0))
    average_hex = '#{:02x}{:02x}{:02x}'.format(*color)

    return average_hex

# sort images by average hex value
sorted_images = sorted(
    [(average_img_hex(img), img) for img in yc_logos if average_img_hex(img) is not None],
    key=lambda x: x[0]
)

images = sorted_images[:600]

fig, axes = plt.subplots(20, 30, figsize=(30, 15), sharex=False, sharey=False)

for img, ax in zip(images, axes.flat):
    ax.imshow(mpimg.imread(img[1]))
    ax.axis('off')

plt.savefig('yc.png')
plt.show()

IMG_DIR = 'data/waas_full_details_dump_files/'
# headshots are all .jpg files, so we can get all headshots like this:
founder_headshots = [x for x in os.listdir(IMG_DIR) if x.endswith('.jpg')]
founder_count = len(founder_headshots)

img_paths = [IMG_DIR + x for x in founder_headshots]
results = {}

for idx, img in enumerate(img_paths):
    print(f"analyzing {idx}/{founder_count}")
    try:
        img_key = img.split("/")[-1]
        obj = DeepFace.analyze(
            img_path=img,
            actions=['age', 'gender', 'race', 'emotion'],
            enforce_detection=False
        )
        obj.update({"img": img_key})
        results[img_key] = obj

    except ValueError as e:
        print(e)

with open("founder_images.json", "w+") as f:
    f.write(json.dumps(results))

race_and_gender_count = defaultdict(lambda: 0)
for result in list(results):
    obj = results[result]
    gender = obj["gender"]
    race = obj["dominant_race"]
    # use (race, gender) tuple as defaultdict key and increment
    race_and_gender_count[(race, gender)] += 1

sorted(race_and_gender_count.items(), key=lambda x: x[1], reverse=True)

[(('white', 'Man'), 771),
 (('asian', 'Man'), 217),
 (('latino hispanic', 'Man'), 134),
 (('indian', 'Man'), 117),
 (('middle eastern', 'Man'), 111),
 (('black', 'Man'), 84),
 (('white', 'Woman'), 52),
 (('asian', 'Woman'), 17),
 (('latino hispanic', 'Woman'), 13),
 (('indian', 'Woman'), 4),
 (('black', 'Woman'), 1)]

%tensorflow_version 1.x
!pip install -q gpt-2-simple
import gpt_2_simple as gpt2
from datetime import datetime
from google.colab import files

!nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   51C    P8    10W /  70W |      0MiB / 15079MiB |      0%      Default |
|                               |                      |                 ERR! |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   51C    P8    10W /  70W |      0MiB / 15079MiB |      0%      Default |
|                               |                      |                 ERR! |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

gpt2.download_gpt2(model_name="124M")

gpt2.mount_gdrive()

file_name = "waas.txt"
gpt2.copy_file_from_gdrive(file_name)

sess = gpt2.start_tf_sess()

gpt2.finetune(
    sess,
    dataset=file_name,
    model_name='124M',
    steps=1000,
    restore_from='fresh',
    run_name='run1',
    print_every=10,
    sample_every=200,
    save_every=500
)

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/gpt_2_simple/src/sample.py:17: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Loading checkpoint models/124M/model.ckpt
INFO:tensorflow:Restoring parameters from models/124M/model.ckpt
  0%|          | 0/1 [00:00<?, ?it/s]Loading dataset...
100%|██████████| 1/1 [00:00<00:00,  1.00it/s]
dataset has 128333 tokens
Training...
[10 | 28.79] loss=3.60 avg=3.60
[20 | 50.75] loss=3.30 avg=3.45
[30 | 73.39] loss=3.25 avg=3.38
[40 | 96.80] loss=3.18 avg=3.33
[50 | 121.40] loss=3.12 avg=3.29
[60 | 145.52] loss=3.10 avg=3.26
[70 | 169.15] loss=2.56 avg=3.16
[80 | 193.06] loss=2.46 avg=3.07
[90 | 217.28] loss=2.59 avg=3.01
[100 | 241.38] loss=2.49 avg=2.96
[110 | 265.33] loss=2.34 avg=2.90
[120 | 289.30] loss=2.52 avg=2.86
[130 | 313.46] loss=2.37 avg=2.82
[140 | 337.69] loss=2.38 avg=2.79
[150 | 361.84] loss=1.92 avg=2.73
[160 | 385.94] loss=2.25 avg=2.70
[170 | 410.01] loss=1.73 avg=2.63
[180 | 434.10] loss=1.66 avg=2.58
[190 | 458.18] loss=1.25 avg=2.50
[200 | 482.22] loss=1.47 avg=2.44
======== SAMPLE 1 ========

 to improve the usability of our Services and to provide better
 offline features through offline messaging. Our Platform
 connects users from across the country to collect data on
 non-US citizens abroad, and then gives US citizens a secure and
 authentic online presence while keeping their identities
 private. Our mission is to enable Americans to be more secure
 in online world.  We do this by putting a damper on online
 threats while preventing online attacks and slowing down the
 spread of cyberthreats. Backed by a world class Series A and B
 investments from leading technology firms, including Shas
 Ventures and Y Combinator, we\'re an organization that believes
 in doing what is right. We\'re built to last and provide an
 experience that\'s built to last.\xa0We\'ve partnered with some
 of the world\'s top law enforcement institutions including the
 Washington DC Metropolitan Detention Center, National Domestic
 Relations Task Force, Lambda Legal, Lambda Legal National Labs,
 NIMBYs Angels of San Francisco and Rally Labs provide legal
 technology & community education, as well as legal assistance/
 cure treatments. We\'re a tech firm that\'s built to last ‘lots
 of years 🙏. We\'ve solved some really really hard problems in
 the past, and haven\'t forgotten.We\'re a team of passionate
 engineers, machine learning researchers, and business
 executives from Y Combinator and Naval Institute. We\'ve just
 closed our Series B and C funding, and\'s back at it with two
 more large-scale brothels and a bona fide law firm dedicated to
 bringing brothels to the people. Join us on this next chapter
 in our journey, and help us build the next Clark County jail.
 We\'re looking for guys who can quickly learn leadership roles
 and fill in for leaders, not executives. We like to take what
 we learn and apply it to real problems. ReactJS, Node, Django,
 Postgres, and a sprinkle of AWS. ReactInfrastructure serves
 Express-like functions in the cloud. Our customers are law
 firms and other third-party service providers, largely
 comprised of tech startups. Our customers are generating
 billions of dollars in revenue through our platform every
 year.  Come join us and help us transform this dynamic into a
 platform-as-a-works. Rippling uses the following
 technologies:React Native JavaScriptFast, clean,
 nativeCSS3Deterministic executionReact NativeScriptWeb appLync,
 typescriptArchitecture-as-a-systemResponsible for maintaining
 and expanding the Ethereum and Bitcoin infrastructure. We\'re
 based in New York City, and are backed by the VC-leading
 venture firms in the space. Our flagship product, Rippling, is
 an intuitive phone-in-a-person phone banking solution that
 integrates an RFID reader and password manager, as well as
 password managers dedicated to managing assets and managing
 cards. Users can create portfolios and trade on the Rippling
 marketplace, or access the technology to create their own
 portfolios on the Bitcoin and Ethereum markets. Simply put,
 Rippling is 21+ year old inventors. We\'ve reinvented banking
 by making money available today through an open API. Banks now
 have an easy path to secure assets, including a digital wallet
 and code backed by an AllinAir’s digital asset portfolio. We
 are a small team of computer scientists building tools that
 improve customer experience, reduce wait times for businesses,
 and increase transparency in banking. Our platform runs on very
 little electricity, minimal mechanical power, and is 100x more
 robust 20 than traditional DPG/PGD systems. The system stores 1
 watt of energy and 8 watts of power per watt signal in a
 standard APS-C (Advanced Plasmon Super Capacitor Stacker).
 Cells in our cell-resistant cell-glide-based sprays secrete a
 protein that is targeted to trigger cell death, called a T
 cell. This triggered death is what causes breast cancer.
 Stacking together our diverse array of biological and chemical
 biology and chemical pathologies, and building in new energy
 efficiency technology to power our smart consumer products,
 we\'re taking our users on an exciting journey toward modern
 energy efficiency. We are a team of computer scientists located
 in Barcelona, Spain, and we are focusing on the extremely first
 and foremost research and development applications in renewable
 energy. Current RCP is the largest R&D and R&E net for the 21st
 century, automating substantial paperwork and providing
 essential modern services. With the exception of emergency
 services and work, we are unaffected by the most common types
 of CO2 emissions including man-made nitrous oxide, human-made
 CO2, and volatile organic compounds. Current RCP delivers the
 highest quality, highest efficiency possible, including
 equipment, software, waste management, and notification
 systems, all in a modern, integrated and convenient way. By
 using RCP, consumers, businesses, civil society, researchers
 and others around the world can save money on power bills,
 reduce emissions and waste, receive timely assistance from the
 energy sector and be

[210 | 517.61] loss=1.62 avg=2.40

[...]

[1000 | 2448.17] loss=0.07 avg=0.47
Saving checkpoint/run1/model-1000
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.

gpt2.generate_to_file(sess,
                      destination_path=gen_file,
                      length=150,
                      prefix="we are building the world's first",
                      temperature=0.7,
                      nsamples=10000,
                      batch_size=20,
                      )