I have a google-cloud-ml model that I can run prediction by passing a 3 dimensional array of float32... { 'instances' [ { 'input' : '[ [ [ 0.0 ], [ 0.5 ], [ 0.8 ] ] ... ] ]' } ] } However this is...

Is it possible to tune hyperparameters using ML Engine to train the model locally? The documentation only mentions training with hyperparameter tuning in the cloud (submitting a job), and has no me...

I would like to feed TFRecords into my model at a super fast rate. However, currently, my GPU(Single K80 on GCP) is at 0% load which is super slow on CloudML. I have TFRecords in GCS: train_direc...

I'm using Tensorflow Object Detection API to train an object detection model using transfer learning. Specifically, I'm using ssd_mobilenet_v1_fpn_coco from the model zoo, and using the sample pipe...

When writing checkpoint files using a tf.train.MonitoredTrainingSession it somehow writes multiple metagraphs. What am I doing wrong? I stripped it down to the following code: import tensorflow a...

I am trying to run a distributed GCMLE training job and I keep getting the following error: An error was raised. This may be due to a preemption in a connected worker or parameter server. The curr...

I followed the codelab TensorFlow For Poets for transfer learning using inception_v3. It generates retrained_graph.pb and retrained_labels.txt files, which can used to make predictions locally (run...

I'm working on Google cloud ML, and I want to get prediction on jpeg image. To do this, I would like to use: gcloud beta ml predict --instances=INSTANCES --model=MODEL [--version=VERSION] (https:...

There are multiple questions (examples: 1, 2, 3, 4, 5, 6, etc.) trying to address the question of how to handle image data when serving predictions for TensorFlow/Keras models in Cloud ML Engine. ...

I'm trying Google Cloud for the first time for training a DNN model with using Keras in R. The use of the cloudml_train()-function try to install a lot of libraries on the Google Cloud server, and ...

Google Cloud ML-engine supports the ability to deploy scikit-learn Pipeline objects. For example a text classification Pipeline could look like the following, classifier = Pipeline([ ('vect', Coun...

I am looking to use Google Cloud ML to host my Keras models so that I can call the API and make some predictions. I am running into some issues from the Keras side of things. So far I have been a...

The complete code for exporting the model: (I've already trained it and now loading from weights file) def cnn_layers(inputs): conv_base= keras.applications.mobilenetv2.MobileNetV2(input_shape=(22...

I have difficulties exporting a custom VGG-Net (not exactly the one from Keras), that was trained with Keras, so that it can be used for the Google Cloud Predict API. I am loading my model with Ker...

So I am trying to switch to an input_fn() using tf.datasets as described in this question. While I have been able to get superior steps/sec using tf.datasets with the input_fn() below, I appear to ...

The google cloud documentation (see Binary data in prediction input) states: Your encoded string must be formatted as a JSON object with a single key named b64. The following Python example en...

I've been trying to adapt the reddit_tft example from the cloud-ml github samples repo to my needs. I've been able to get it running as per the tutorial readme. However what i want to use it fo...

I am trying to convert an encoded byte string back into the original array in the tensorflow graph (using tensorflow operations) in order to make a prediction in a tensorflow model. The array to by...

I have a GCMLE experiment and I am trying to upgrade my input_fn to use the new tf.data functionality. I have created the following input_fn based off of this sample def input_fn(...): dataset = ...

So, I've been struggling to understand what the main task of a serving_input_fn() is when a trained model is exported in Tensorflow for serving purposes. There are some examples online that explain...

I have a Keras .h5 model which I've been training locally, however now wish to automate the full process via the Google Cloud ML-Engine. I have all the GCloud Storage buckets set up to be accessed...

I have a working uploaded ML-model on Goggle Cloud platform (Tested via python and gcloud ml-engine predict). I am currently trying to get predictions from Android using this library: Client Libra...

I am trying to convert a keras model to use it for predictions on google cloud's ml-engine. I have a pre-trained classifier that takes in a numpy array as input. The normal working data I send to m...

I am using Tensorflow r0.12. I use google-cloud-ml locally to run 2 different training jobs. In the first job, I find good initial values for my variables. I store them in a V2-checkpoint. When I...

I am surveying Google Dataflow and Apache Spark to decide which one is more suitable solution for our bigdata analysis business needs. I found there are Spark SQL and MLlib in the spark platform t...