cloud-machine | Go Project that should be used to create a cloud | Continuous Deployment library

I am trying to figure out how to do batch prediction using Google Cloud. Specifically, I'm looking to do object detection, getting from a faster-RCNN tensorflow ckpt to a graph/saved model.

My issue is that I need to be able to recover some kind of ID for my input images, perhaps an index or a filename. I'm not entirely sure how to do this in my situation, since this link mentions using instance keys, and the only relevant examples I've found regarding instance keys use JSON as the input format. As I am supposed to use TFRecords for input to my saved model, this would seem to be an issue. I also consulted the prediction guide, but was still confused.

In short, does anybody have any tips as to what file(s) I should edit (export_inference_graph.py?) to preserve some sort of indices/ordering of my input images for batch prediction? I am using the Object Detection API for reference. Thanks!

I am using a custom tensorflow Estimator and am trying to use

According to https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Algorithm there are only grid search and random search algorithms available.

According to this blog post https://cloud.google.com/blog/products/gcp/hyperparameter-tuning-cloud-machine-learning-engine-using-bayesian-optimization Bayesian is supported.

So, if supported, how can I tune hyperparameters using Bayesian optimization on Google Cloud ML Engine?

I trained an object detector using Cloud Machine Learning Engine according to the recent post by Google’s Derek Chow on the Google Cloud Big Data And Machine Learning Blog and now want to predict using Cloud Machine Learning Engine.

The instructions include code to export the Tensorflow graph as output_inference_graph.pb but not how to convert protobuf format (pb) into the SavedModel format required for gcloud ml-engine predict.

I reviewed the answer by Google’s @rhaertel80 for how to convert a “Tensorflow For Poets” image classification model and the answer provided by Google’s @MarkMcDonald for how to convert a “Tensorflow For Poets 2” image classification model but neither appears to work for the object detector graph (pb) described in the blog post.

How does one convert that object detector graph (pb) so it can be used or gcloud ml-engine predict, please?

I am deploying a model to Google Cloud ML for the first time. I have trained and tested the model locally and it still needs work but it works ok.

I have uploaded it to Cloud ML and tested with the same example images I test locally that I know get detections. (using this tutorial)

When I do this, I get no detections. At first I thought I had uploaded the wrong checkpoint but I tested and the same checkpoint works with these images offline, I don't know how to debug further.

When I look at the results the file

prediction.results-00000-of-00001

is just empty

and the file

prediction.errors_stats-00000-of-00001

contains the following text: ('No JSON object could be decoded', 1)

Is this a sign the detection has run and detected nothing, or is there some problem while running?

Maybe the problem is I am preparing the images wrong for uploading?

The logs show no errors at all

Thank you

EDIT:

I was doing more tests and tried to run the model locally using the command "gcloud ml-engine local predict" instead of the usual local code. I get the same result as online, no answer at all, but also no error message

EDIT 2: I am using a TF_Record file, so I don't understand the JSON response. Here is a copy of my command:

gcloud ml-engine jobs submit prediction ${JOB_ID} --data- format=tf_record \ --input-paths=gs://MY_BUCKET/data_dir/inputs.tfr \ --output-path=gs://MY_BUCKET/data_dir/version4 \ --region us-central1 \ --model="gcp_detector" \ --version="Version4"

I trained a tensorflow model that i'd like to run predictions on from numpy arrays. This is for image processing within videos. I will pass the images to the model as they happen. Not every frame is passed.

I reload my SavedModel within a session like so

Followed

OAuth example

successfully getting bearer token, but response is:

I’m working on an object detector using Cloud Machine Learning Engine via a Cloud-VM instance. Following the tutorial (https://cloud.google.com/blog/big-data/2017/06/training-an-object-detector-using-cloud-machine-learning-engine).

I get a module import error on Google Cloud Platform when I submit the training job below:

I’m working on an object detector using Cloud Machine Learning Engine via a Cloud-VM instance. Following the tutorial (https://cloud.google.com/blog/big-data/2017/06/training-an-object-detector-using-cloud-machine-learning-engine).

I get a module import error on Google Cloud Platform when I submit the training job below: