google-cloud-ml-engine

I am trying to output some summary scalars in an ML engine experiment at both train and eval time. tf.summary.scalar('loss', loss) is correctly outputting the summary scalars for both training and evaluation on the same plot in tensorboard. However, I am also trying to output other metrics at both train and eval time and they are only outputting at train time. The code immediately follows tf.summary.scalar('loss', loss) but does not appear to work. For example, the code as follows is only outputting for TRAIN, but not EVAL. The only difference is that these are using custom accuracy functions,

My goal is to use training data (format: tfrecords) stored on Google Cloud storage when I run my Tensorflow Training App, locally. (Why locally? : I am testing before I turn it into a training package for Cloud ML) Based on this thread I shouldn't have to do anything since the underlying Tensorflow API's should be able to read a gs://(url) However thats not the case and the errors I see are of the format: 2017-06-06 15:38:55.589068: I tensorflow/core/platform/cloud/retrying_utils.cc:77] The operation failed and will be automatically retried in 1.38118 seconds (attempt 1 out of 10), caused by:

I am solving a text classification problem. I defined my classifier using the Estimator class with my own model_fn . I would like to use Google's pre-trained word2vec embedding as initial values and then further optimise it for the task at hand. I saw this post: Using a pre-trained word embedding (word2vec or Glove) in TensorFlow which explains how to go about it in 'raw' TensorFlow code. However, I would really like to use the Estimator class. As an extension, I would like to then train this code on Cloud ML Engine, is there a good way of passing in the fairly large file with initial values?

I have successfully deployed on model on Cloud ML Engine and verified it is working with gcloud ml-engine models predict by following the instructions , now I want to send predictions to it from my C# app. How do I do that? The online prediction API is a REST API, so you can use any library for sending HTTPS requests, although you will need to use Google's OAuth library to get your credentials. The format of the request is JSON, as described in the docs . To exemplify, consider the Census example. A client for that might look like: using System; using System.Collections.Generic; using System

I am getting an error "Internal error occurred for the current attempt" while submitting a job on google cloud ML. Can anyone help me on this ??? We're experiencing a bit of a capacity crunch making it a little hard to obtain VMs. You can try running in another region such as us-east1 for the time being. We are working on making sure the error message is more informative. Thanks for your patience. 来源： https://stackoverflow.com/questions/45609471/internal-error-on-google-cloud-ml

In the alpha release of CloudML's online prediction service, the format for exporting model was: inputs = {"x": x, "y_bytes": y} g.add_to_collection("inputs", json.dumps(inputs)) outputs = {"a": a, "b_bytes": b} g.add_to_collection("outputs", json.dumps(outputs)) I would like to convert this to a SavedModel without retraining my model. How can I do that? We can convert this to a SavedModel by importing the old model, creating the Signatures, and re-exporting it. This code is untested, but something like this should work: import json import tensorflow as tf from tensorflow.contrib.session

This is a ml-job I previously trained successfully . But when I tried it today it's not working . So after that I tried removing all the things is the bucket and start over . Still it's not working . Giving the following error . Internal error occurred. Please retry in a few minutes. If you still experience errors, contact Cloud ML. 来源： https://stackoverflow.com/questions/45609164/google-cloud-ml-engine-job-failed-due-to-an-internal-error-cant-execute-a-j

I am loading a tensorboard for my ml engine experiment that is running in local mode and got the following warning: "Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events. Overwriting the graph with the newest event. W0825 19:26:12.435613 Reloader event_accumulator.py:311] Found more than one metagraph event per run. Overwriting the metagraph with the newest event." Originally, I suspected that this was because I had not cleared my --logdir=$OUTPUT_PATH (as other posts suggested -- however, even if I performed rm -rf

I've followed the TensorFlow for Poets tutorial and replaced the stock flower_photos with a few classes of my own. Now I've got my labels.txt file and my graph.pb saved on my local machine. Is there a way for me to deploy this pre-trained model to Google Cloud Platform? I've been reading the docs and all I can find are instructions on how to create, train, and deploy models from within their ML Engine. But I don't want to spend money training my model on Google's servers when I only need them to host my model so I can call it for predictions. Anyone else run into the same problem? Deploying a