google-cloud-ml-engine | 易学教程

Deploy retrained inception SavedModel to google cloud ml engine

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I am trying to deploy a retrained version of the inception model on google cloud ml-engine. Gathering informations from the SavedModel documentation , this reference , and this post of rhaertel80, I exported successfully my retrained model to a SavedModel, uploaded it to a bucket and tried to deploy it to a ml-engine version. This last task actually creates a version, but it outputs this error: Create Version failed. Bad model detected with error: "Error loading the model: Unexpected error when loading the model" And when I try to get predictions from the model via commandline I get this error

Using gcloud ml serving for large images

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I have a trained net in tensorflow that i wish to use in gcloud ml-engine serving for prediction. Predict gcloud ml serving should accept numpy array float32 type images with size of 320x240x3 and return 2 tiny matrices as an output. Does anyone knows how should i create the input layers that would accept this kind of input type? I have tried multiple ways, for example using base64 encoded json files, but casting the string into float type produces an error in which it's not supported: "error": "Prediction failed: Exception during model execution: LocalError(code=StatusCode.UNIMPLEMENTED,

How to deploy locally trained TensorFlow graph file to Google Cloud Platform?

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问题 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

Training and Predicting with instance keys

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问题 I am able to train my model and use ML Engine for prediction but my results don't include any identifying information. This works fine when submitting one row at a time for prediction but when submitting multiple rows I have no way of connecting the prediction back to the original input data. The GCP documentation discusses using instance keys but I can't find any example code that trains and predicts using an instance key. Taking the GCP census example how would I update the input functions

Using gcloud ml serving for large images

阅读更多关于 Using gcloud ml serving for large images

问题 I have a trained net in tensorflow that i wish to use in gcloud ml-engine serving for prediction. Predict gcloud ml serving should accept numpy array float32 type images with size of 320x240x3 and return 2 tiny matrices as an output. Does anyone knows how should i create the input layers that would accept this kind of input type? I have tried multiple ways, for example using base64 encoded json files, but casting the string into float type produces an error in which it's not supported: "error

Deploy retrained inception SavedModel to google cloud ml engine

阅读更多关于 Deploy retrained inception SavedModel to google cloud ml engine

问题 I am trying to deploy a retrained version of the inception model on google cloud ml-engine. Gathering informations from the SavedModel documentation, this reference, and this post of rhaertel80, I exported successfully my retrained model to a SavedModel, uploaded it to a bucket and tried to deploy it to a ml-engine version. This last task actually creates a version, but it outputs this error: Create Version failed. Bad model detected with error: "Error loading the model: Unexpected error when