[GitHub] [beam] damccorm commented on a diff in pull request #23018: Clarify inference example docs

[GitBox <gi...@apache.org>]

damccorm commented on code in PR #23018:
URL: https://github.com/apache/beam/pull/23018#discussion_r963150978


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sdks/python/apache_beam/examples/inference/README.md:
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@@ -67,37 +67,35 @@ The pipeline reads the images, performs basic preprocessing, passes the images t
 
 To use this transform, you need a dataset and model for image classification.
 
-1. Create a directory named `IMAGES_DIR`. Create or download images and put them in this directory. The directory is not required if image names in the input file `IMAGE_FILE_NAMES` have absolute paths.
+1. Create a directory named `IMAGES_DIR`. Create or download images and put them in this directory. The directory is not required if image names in the input file `IMAGE_FILE_NAMES.txt` you create in step 2 have absolute paths.
 One popular dataset is from [ImageNet](https://www.image-net.org/). Follow their instructions to download the images.
-2. Create a file named `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` that you want to use to run image classification. The path to the file can be different types of URIs such as your local file system, an AWS S3 bucket, or a GCP Cloud Storage bucket. For example:
+2. Create a file named `IMAGE_FILE_NAMES.txt` that contains the absolute paths of each of the images in `IMAGES_DIR` that you want to use to run image classification. The path to the file can be different types of URIs such as your local file system, an AWS S3 bucket, or a GCP Cloud Storage bucket. For example:
 ```
 /absolute/path/to/image1.jpg
 /absolute/path/to/image2.jpg
 ```

Review Comment:
   I actually think that leading with local is a better experience here. Most people are going to want the simplest example path.



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