More copyediting of examples (#126)

02_building_containers/screenshot.py

@@ -2,25 +2,24 @@
 # integration-test: false
 # output-directory: "/tmp/screenshots"
 # ---
-# # Screenshot with headless Chromium
+# # Screenshot with Chromium
 
-# In this example, we use Modal functions and the `playwright` package to screenshot websites from a list of urls in parallel.
-# Please also see our [introductory guide](/docs/guide/web-scraper) for another example of a web-scraper, with more in-depth examples.
+# In this example, we use Modal functions and the `playwright` package to take screenshots
+# of websites from a list of URLs in parallel.
 #
-# You can run this example on the command line, like this:
+# You can run this example on the command line with
 #
 # ```
 # python 02_building_containers/screenshot.py 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'
 # ```
 #
-# This should take a few seconds then write a `/tmp/screenshots/screenshot.png` file.
-# This is what the file should look like:
+# This should take a few seconds then create a `/tmp/screenshots/screenshot.png` file, shown below.
 #
 # ![screenshot](./screenshot.png)
 #
 # ## Setup
 #
-# First we import the `modal` client library:
+# First we import the Modal client library.
 
 import pathlib
 import sys
@@ -29,7 +28,7 @@ import modal
 
 stub = modal.Stub("example-screenshot")
 
-# ## Defining a custom image
+# ## Define a custom image
 #
 # We need an image with the `playwright` Python package as well as its `chromium` plugin pre-installed.
 # This requires intalling a few Debian packages, as well as setting up a new Debian repository.
@@ -48,7 +47,7 @@ image = modal.Image.debian_slim().run_commands(
     ],
 )
 
-# ## Defining the screenshot function
+# ## The screenshot function
 #
 # Next, the scraping function which runs headless Chromium, goes to a website, and takes a screenshot.
 # This is a Modal function which runs inside the remote container.
@@ -82,3 +81,6 @@ if __name__ == "__main__":
         with open(filename, "wb") as f:
             f.write(data)
         print(f"wrote {len(data)} bytes to {filename}")
+
+# And we're done! Please also see our [introductory guide](/docs/guide/web-scraper) for another
+# example of a web scraper, with more in-depth logic.

03_scaling_out/basic_grid_search.py

@@ -1,20 +1,23 @@
-# # Grid search
+# # Hyperparameter search
+#
+# This example showcases a simple grid search in one dimension, where we try different
+# parameters for a model and pick the one with the best results on a holdout set.
 #
-# This example showcases a very simplistic grid search in 1-D where we try different parameters for a model
-# and pick the one with the best results on a holdout set.
-
-import modal
-
 # ## Defining the image
 #
 # First, let's build a custom image and install scikit-learn in it.
 
-stub = modal.Stub("example-basic-grid-search", image=modal.Image.debian_slim().pip_install(["scikit-learn"]))
+import modal
+
+stub = modal.Stub(
+    "example-basic-grid-search",
+    image=modal.Image.debian_slim().pip_install(["scikit-learn"]),
+)
 
 # ## The Modal function
 #
 # Next, define the function. Note that we use the custom image with scikit-learn in it.
-# We also take the hyperparameter `k` which is how many nearest neighbors we use.
+# We also take the hyperparameter `k`, which is how many nearest neighbors we use.
 
 
 @stub.function
@@ -33,10 +36,10 @@ def fit_knn(k):
     return score, k
 
 
-# ## Hyperparameter search
+# ## Parallel search
 #
-# To do a hyperparameter search, let's map over this function with lots of different values
-# for `k`, and then pick whichever `k` has the best score on the holdout set:
+# To do a hyperparameter search, let's map over this function with different values
+# for `k`, and then select for the best score on the holdout set:
 
 if __name__ == "__main__":
     with stub.run():

03_scaling_out/fetch_stock_prices.py

@@ -33,7 +33,9 @@ import modal
 
 stub = modal.Stub(
     "example-fetch-stock-prices",
-    image=modal.Image.debian_slim().pip_install(["requests", "yfinance", "beautifulsoup4", "matplotlib"]),
+    image=modal.Image.debian_slim().pip_install(
+        ["requests", "yfinance", "beautifulsoup4", "matplotlib"],
+    ),
 )
 
 # ## Fetch a list of tickers
@@ -48,7 +50,10 @@ def get_stocks():
     import bs4
     import requests
 
-    headers = {"user-agent": "curl/7.55.1", "referer": "https://finance.yahoo.com/"}
+    headers = {
+        "user-agent": "curl/7.55.1",
+        "referer": "https://finance.yahoo.com/",
+    }
     url = "https://finance.yahoo.com/etfs/?count=100&offset=0"
     res = requests.get(url, headers=headers)
     soup = bs4.BeautifulSoup(res.text, "html.parser")

03_scaling_out/youtube_face_detection.py

@@ -2,7 +2,7 @@
 # deploy: true
 # output-directory: "/tmp"
 # ---
-# # Face detection on Youtube videos
+# # Face detection on YouTube videos
 #
 # This is an example that uses
 # [OpenCV](https://github.com/opencv/opencv-python)
@@ -44,13 +44,16 @@ FACE_CASCADE_FN = "haarcascade_frontalface_default.xml"
 
 image = (
     modal.Image.debian_slim()
-    .run_commands(["apt-get install -y libgl1-mesa-glx libglib2.0-0 wget"])
     .run_commands(
-        [f"wget https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/{FACE_CASCADE_FN} -P /root"]
+        [
+            "apt-get install -y libgl1-mesa-glx libglib2.0-0 wget",
+            f"wget https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/{FACE_CASCADE_FN} -P /root",
+        ]
     )
     .pip_install(["pytube", "opencv-python", "moviepy"])
 )
 stub = modal.Stub("example-youtube-face-detection", image=image)
+
 if stub.is_inside():
     import cv2
     import moviepy.editor
@@ -100,7 +103,7 @@ def detect_faces(fn, start, stop):
 #
 # The "entrypoint" into Modal controls the main flow of the program:
 #
-# 1. Download the video from Youtube
+# 1. Download the video from YouTube
 # 2. Fan-out face detection of individual 1s clips
 # 3. Stitch the results back into a new video
 
@@ -138,7 +141,7 @@ def run(url):
 #
 # The code we run locally to fire up the Modal job is quite simple
 #
-# * Take a Youtube URL on the command line
+# * Take a YouTube URL on the command line
 # * Run the Modal function
 # * Store the output data
 

data/covid_datasette.py

@@ -58,7 +58,7 @@ DB_PATH = pathlib.Path(CACHE_DIR, "covid-19.db")
 
 # ## Getting a dataset
 #
-# Johns Hopkins has been publishing up-to-date COVID-19 pandemic data on Github since early February 2020, and
+# Johns Hopkins has been publishing up-to-date COVID-19 pandemic data on GitHub since early February 2020, and
 # as of late September 2022 daily reporting is still rolling in. Their dataset is what this example will use to
 # show off Modal and Datasette's capabilities.
 #

data/meltano.py

@@ -32,7 +32,7 @@ stub = modal.Stub(
 )
 
 
-# For this example to work, the secret provides a valid Github personal access token
+# For this example to work, the secret provides a valid GitHub personal access token
 # under the key `TAP_GITHUB_ACCESS_TOKEN`. You may provide any other plugin configs that you wish via modal's Secrets.
 @stub.function(
     secrets=[