google-cloud-ml

问题 I am trying to train a machine learning model usinf tensorflow library in the google cloud. I am able to train the model in the cloud after creating a bucket. I am facing the issue when I am tring to make predictions using the existing model. The code and the data is available in the following Github directory. https://github.com/terminator172/game-price-predictions The tensorflow version on the cloud is 1.8 and the tensorflow version on my system is also 1.8 I tried to make predictions by

问题 I am trying to train a machine learning model usinf tensorflow library in the google cloud. I am able to train the model in the cloud after creating a bucket. I am facing the issue when I am tring to make predictions using the existing model. The code and the data is available in the following Github directory. https://github.com/terminator172/game-price-predictions The tensorflow version on the cloud is 1.8 and the tensorflow version on my system is also 1.8 I tried to make predictions by

问题 I've been trying to deploy my model to the AI platform for Prediction through the console on my vm instance, but I've gotten the error "(gcloud.beta.ai-platform.versions.create) Create Version failed. Bad model detected with error: "Failed to load model: Unexpected error when loading the model: problem in predictor - ModuleNotFoundError: No module named 'torchvision' (Error code: 0)" I need to include both torch and torchvision . I followed the steps in this question Cannot deploy trained

问题 I'm deploying a Pytorch model in Google Cloud AI Platform, I'm getting the following error: ERROR: (gcloud.beta.ai-platform.versions.create) Create Version failed. Bad model detected with error: Model requires more memory than allowed. Please try to decrease the model size and re-deploy. If you continue to have error, please contact Cloud ML. Configuration: setup.py from setuptools import setup REQUIRED_PACKAGES = ['torch'] setup( name="iris-custom-model", version="0.1", scripts=["model.py"],

问题 In my model evaluation algorithm I would like to get model predictions for validation data and apply an algorithm which models and imitates some real-world scenarios based on validation data and predictions. In my scenario evaluation algorithm depends not only on true target values (y_true) and predictions (y_pred), but also on input validation data (X) to output a final model score. Thus it seems like I can not use an estimator with a custom metric for my use case. It is trivial for me to

问题 In my model evaluation algorithm I would like to get model predictions for validation data and apply an algorithm which models and imitates some real-world scenarios based on validation data and predictions. In my scenario evaluation algorithm depends not only on true target values (y_true) and predictions (y_pred), but also on input validation data (X) to output a final model score. Thus it seems like I can not use an estimator with a custom metric for my use case. It is trivial for me to

问题 What is the easiest way to compare two GCMLE deployed prediction models and identify any differences in their graphs? I have visually inspected both tensorboards and they look identical (as they should be). However, I have code to visualize their activations (basically just loads the weights from the graphs and manually performs all forward steps) and somewhere along the way on one of the graphs my hand-written forward pass calculations diverge from tensorflow's forward pass calculations. The

问题 we got a working exported model in local that is falling to create a new model version in Google Cloud ML as follows: Create Version failed. Model validation failed: Outer dimension for outputs must be unknown, outer dimension of 'Const_2:0' is 1 For more information on how to export Tensorflow SavedModel, seehttps://www.tensorflow.org/api_docs/python/tf/saved_model. Our current exported model response is working in tensorflow-serve and gcloud predict local with this responses: outputs { key:

问题 I'm trying to use Cloud AI Platform for training (gcloud ai-platform jobs submit training). I created my bucket and am sure the training file is there (gsutil ls gs://sat3_0_bucket/data/train_input.csv). However, my job is failing with log messsage: File "/root/.local/lib/python3.7/site-packages/ktrain/text/data.py", line 175, in texts_from_csv with open(train_filepath, 'rb') as f: FileNotFoundError: [Errno 2] No such file or directory: 'gs://sat3_0_bucket/data/train_input.csv' Am I missing

问题 I am trying to build on top of this example. I am trying to deploy the shap explainer as a custom prediction routine on google AI platform. Unfortunately when I create the version I get the following error: Create Version failed. Bad model detected with error: Model requires more memory than allowed. Please try to decrease the model size and re-deploy. If you continue to have error, please contact Cloud ML. Furthermore, instead of text i am working with images, and the shap explainer is a