Add samples for the Cloud ML Engine by elibixby · Pull Request #824 · GoogleCloudPlatform/python-docs-samples · GitHub

Add samples for triggering online prediction from code.

@nikhilk @brandondutra @JayLoomis since I can't make you reviewers on this repo.

Tests are to follow. This is to solicit initial feedback while I write tests. Note that I don't think we should highlight predict_from_files in the docs, as that's redundant and not as efficient as batch prediction. It's mainly there for testing and to make the file runnable (A repository policy).

why the local imports? This is not a DF job.

This way the necessary inputs will show up in snippets in the docs. The [START foo] and [END foo] blocks indicate a displayable chunk for the docs

Note. On discussing with @jonparrott I'm going to remove predict_from_files and write a short webapp.

why are we making 100 copies of this tuple. This looks wrong.

100 copies of the generator. This is how you batch generators in python (it's weird). But I'm deleting this code anyway in favor of a webapp.

where are the results saved or printed?

so the input data could need batching (be large), but the results don't need batching? I'm ok with this script not doing batching. Depends on what others say.

Add comments on authentication. When should this work, or what needs to be true for the script to work.

Done

in get_ml_engine_service, can you add a link to the doc page describing how I can download a service account file?

does your batching code really work? Hard to tell with just 1 prediction row

@brandondutra Switched to user input stream to avoid problems of batching and file reading which don't really belong in an online prediction sample.

@xlcheng

@brandondutra PTAL

@jonparrott Installing TensorFlow appears to be broken... @jonparrott can you PTAL?

I'm not a fan of raw-input (cannot re-run this quickly, and typing valid json is a pain). But this allows interactive input and "python predict.py < my_data.json". Maybe add file-level comments on these two ways of using this script?

So the main reason I wanted to do it this way, is we already have a solution for batch prediction (via the API) and a 100 request limit seems really bad if the use-case we are highlighting is predicting from files.

I was expecting the file path to be in the census example (in cloudml-samples). Is this file part of the census sample or a more generic 'calling online prediction' sample? If the latter, we need better warnings that this will not work with every model, and we need to describe what the model is expecting.

If this is not part of the census sample, a s/census/json/g is needed.

Sorry if this is a bad question, I not familiar with python-docs-samples

I was thinking we have sort of a hard separation between "things run as part of training" and "code run in your own client to send requests to the prediction service" The former being in cloudml-samples (and in the future tf/garden) and the latter being in python-docs-samples.

I will definitely add some better prose around this in the form of a docstring, and we'll also make it clear in docs.

I'm starting to think you want these files in GoogleCloudPlatform/cloudml-samples/census/something

This license header looks weird, copy it from elsewhere?

s/2016/2017/g

Nit: blank line between license and docstring.

Needs a shebang

We generally show constructing the service in each snippet instead of centralizing it. Every indirection adds cognitive load to the users.

blank newline above here.

We generally encourage snippets to be simple enough not to require this, but I understand if that's not reasonable here. If you're going to go full docstring, follow Napoleon style:

Args:
    project (str): ...
    model (str): ...
    instances (Mapping[ str, dict ]): ...
    version (str): optional ...

Returns:
   Mapping [str, ...] : ...

Don't import here, import at the top.

How do you highlight that this import is only necessary for this snippet?

Is that not important?

Blank new line to separate control statements.

Don't import here.

Where do the users find out what kind of json to send here?

It depends on their model. This snippet will be part of a docs page that is attempting to explain just that. This will be at the end "now that you know what the prediction service does, here's how you call it".

We don't have hand-written readmes in here any more. Please move all of this to the documentation and just link to the docs from here. I can add an auto-generated readme later.

Oh cool.

This isn't necessary.

Period? Also, maybe it's just my unfamiliarity with tensorflow, but this reads like gibberish.

Yeah it doesn't make much sense with context. But could also use some rewording.

Any is capital. What's the key and value here?

What's the key and value here?

blank newline both above and below this.

2017, also, these headers still seem different from the ones in the rest of the repo.

just import predict, please don't import individual members.

Don't put todos in code, just file an issue or bug to track it.

Don't use ranges, pin the version and dpebot will handle updating it.

LGTM after final nits, pending Travis.

this goes in the same section as import googleapiclient.discovery

Why not write a real test and mark it with pytest.mark.xfail('reason')?

File-like objects should be opened in binary mode for `blob.upload_from_file()` - cpython standard library accorded with [RFC 2616 Section 3.7.1](https://datatracker.ietf.org/doc/html/rfc2616#section-3.7.1) states the text default charset of iso-8859-1 - add clarifying notes in docstring - update code sample Fixes #818 🦕

elibixby added 2 commits February 24, 2017 10:22

Working commit

5dd9b31

Initial commit of prediction examples

42645c7

elibixby requested review from puneith and theacodes February 24, 2017 22:42

googlebot added the cla: yes This human has signed the Contributor License Agreement. label Feb 24, 2017

brandondutra reviewed Feb 24, 2017

View reviewed changes

Switch to user input stream

52e37a5

elibixby added 5 commits February 24, 2017 15:39

Fix user input loop

71daf1f

Add tests and requirements

11f076d

Add tfrecord stub

125dda0

Add authentication instructions

c43b65d

Small fixes

ad349a5

Fix tests and lint

7df6c4f

brandondutra reviewed Feb 27, 2017

View reviewed changes

theacodes suggested changes Feb 27, 2017

View reviewed changes

elibixby added 3 commits February 27, 2017 12:54

Run census_example_to_bytes in Jenkins only

d5a0e18

Fix some review comments

6bd101a

Add README

a241ff2

Move imports

5ca6773

theacodes suggested changes Feb 28, 2017

View reviewed changes

elibixby added 2 commits February 27, 2017 17:38

Fix review comments

8c3da38

Fix License Headers

90bfa5d

theacodes approved these changes Feb 28, 2017

View reviewed changes

Fix style. Add tfrecords test

2d1726a

elibixby merged commit b0417c9 into master Feb 28, 2017

elibixby deleted the mlengine branch February 28, 2017 02:12

chalmerlowe mentioned this pull request Apr 7, 2026

migrate code from googleapis/python-storage #13989

Merged

9 tasks

+                                     version=None,
+                                     force_tfrecord=False):
+                  import json
+                  import itertools

+                              batch,
+                              version=version
+                          ))
+                  return results

+              # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+              # License for the specific language governing permissions and limitations under
+              # the License.
+              """Examples of using the Cloud ML Engine's online prediction service."""

+                  import json
+                  while True:
+                      try:
+                          user_input = json.loads(raw_input("Valid JSON >>>"))

+              # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+              # License for the specific language governing permissions and limitations under
+              # the License.
+              """Examples of using the Cloud ML Engine's online prediction service."""

+                  # Requests to online prediction
+                  # can have at most 100 instances
+                  args = [instances] * 100

+                  args = [instances] * 100
+                  instance_batches = itertools.izip(*args)
+                  results = []

		@@ -0,0 +1 @@
		{"age": 25, "workclass": " Private", "education": " 11th", "education_num": 7, "marital_status": " Never-married", "occupation": " Machine-op-inspct", "relationship": " Own-child", "race": " Black", "gender": " Male", "capital_gain": 0, "capital_loss": 0, "hours_per_week": 40, "native_country": " United-States"}



		# [START census_to_example_bytes]
		def census_to_example_bytes(json_instance):

		from predict import census_to_example_bytes, predict_json


		MODEL = 'census'

		@@ -0,0 +1,173 @@
		# Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache

+              # [START predict_json]
+              def predict_json(project, model, instances, version=None):
+                  """Send data instances to a deployed model for prediction
+                  Args:

+                      to data.
+                      version: [optional] str, version of the model to target.
+                  Returns:
+                      A dictionary of prediction results defined by the model.

+                          for example_bytes in example_bytes_list
+                      ]}
+                  ).execute()
+                  if 'error' in response:

+              def main(project, model, version=None, force_tfrecord=False):
+                  """Send user input to the prediction service."""
+                  import json

		# [END import_libraries]


		# [START authenticating]

		@@ -0,0 +1,38 @@
		# Online Prediction with the Cloud Machine Learning Engine

		predict_json(PROJECT, MODEL, [{"foo": "bar"}], version=VERSION)


		# TODO(elibixby) Run on Travis when TensorFlow PyPi package supports

		@@ -0,0 +1 @@
		tensorflow>=1.0.0

+                      model (str): model name.
+                      instances ([Mapping[str: any]]): dictionaries from string keys
+                          defined by the model deployment, to data with types that match
+                          expected tensors

		@@ -0,0 +1,68 @@
		# Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache


		import pytest

		from predict import census_to_example_bytes, predict_json

+              import googleapiclient.discovery
+              # [END import_libraries]
+              import six

		assert base64.b64encode(b) is not None


		def test_predict_tfrecord():

Add samples for the Cloud ML Engine by elibixby · Pull Request #824 · GoogleCloudPlatform/python-docs-samples · GitHub

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Add samples for the Cloud ML Engine - #824

Add samples for the Cloud ML Engine#824elibixby merged 16 commits intomasterGoogleCloudPlatform/python-docs-samples:masterfrom mlengineGoogleCloudPlatform/python-docs-samples:mlengineCopy head branch name to clipboard

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elibixby commented Feb 24, 2017

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elibixby Feb 27, 2017 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Add samples for the Cloud ML Engine#824
elibixby merged 16 commits into
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mlengine

elibixby Feb 27, 2017 •
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