Chapter 2 example error

In attempting to execute the code at the end of chapter 2 i get the following error:

WARNING:google.auth.compute_engine._metadata:Compute Engine Metadata server unavailable on attempt 1 of 3. Reason: timed out
WARNING:google.auth.compute_engine._metadata:Compute Engine Metadata server unavailable on attempt 2 of 3. Reason: timed out
WARNING:google.auth.compute_engine._metadata:Compute Engine Metadata server unavailable on attempt 3 of 3. Reason: timed out
WARNING:google.auth._default:Authentication failed using Compute Engine authentication due to unavailable metadata server.
WARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: Could not automatically determine credentials. Please set GOOGLE_APPLICATION_CREDENTIALS or explicitly create credentials and re-run the application. For more information, please see https://cloud.google.com/docs/authentication/getting-started
Connecting anonymously.

I know its in reference to attempting to pull kinglear.txt from google storage. Any tips on how to resolve this? BTW here is the source code i copied out of the book:

import re
import apache_beam as beam
from apache_beam.io import ReadFromText
from apache_beam.io import WriteToText
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions

input_file = "gs://dataflow-samples/shakespeare/kinglear.txt"
output_file = "~/coding/machine-learning/output.txt"

pipeline_options = PipelineOptions()

with beam.Pipeline(options=pipeline_options) as p:
    lines = p | ReadFromText(input_file)
    counts = (
        lines
        | 'Split' >> beam.FlatMap(lambda x: re.findall(r'[A-Za-z\']+', x))
        | 'PairWithOne' >> beam.Map(lambda x: (x, 1))
        | 'GroupAndSum' >> beam.CombinePerKey(sum)
    )
    def format_result(word_count):
        (word, count) = word_count
        return "{}: {}".format(word, count)

    output = counts | 'Format' >> beam.Map(format_result)

    output | WriteToText(output_file)

Building-ML-Pipelines / building-machine-learning-pipelines

Chapter 2 example error #66