google-cloud-ml

问题 The pre-processing page in the cloud ML How to guide (https://cloud.google.com/ml/docs/how-tos/preprocessing-data) says that you should see the SDK reference documentation for details about each type of feature and the Can anyone point me to this documentation or a list of feature types and their methods? I'm trying to setup a discrete target but keep getting "data type int64 expected type: float" errors whenever I set my target to .discrete() rather than .continuous() 回答1: You need to

问题 I'm using Google Cloud Machine Learning to predict images with labels. I've trained my model, named flower and I see the API end point at Google API Exporer but, when I call the API at API Explorer, I get the following error: Image Error I can't understanding why. Thanks Ibere 回答1: I guess you followed the tutorial from https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/flowers? I had the exact same problem but with some trial and errors I succeeded with the payload: {

问题 I am new to the Google ML Cloud engine. I would like to post the Keras model to the cloud to train, but I always get this error: I master-replica-0 Running module trainer.bot. master-replica-0 I master-replica-0 Downloading the package: gs://zadravecm-bot/jobs/test_job4/packages/84f3c60920e885020405e1eb7afa5f509313d2a5406a1f1551a81b81993ac66c/trainer-1.0.tar.gz master-replica-0 I master-replica-0 Running command: gsutil -q cp gs://zadravecm-bot/jobs/test_job4/packages

问题 I'm getting the following error when trying to use gcloud ml-engine predict ERROR: (gcloud.ml-engine.predict) HTTP request failed. Response: { "error": { "code": 429, "message": "Prediction server is out of memory, possibly because model size is too big.", "status": "RESOURCE_EXHAUSTED" } } My model size is 151 mb, I'm also using Tensorflow version 1.4 that does not requiere variables folder. When performing prediction it uses over 2gb. I'm using a modified version of inception. 回答1:

问题 I am reading my data file using the following commands: data_dir = arguments['data_dir'] data = pd.read_csv(data_dir + "/train.csv") I am using this data to train my model on Google Cloud ML, I am successfully able to schedule the job but getting the following IO error while fetching the file: IOError: File gs://cloud-bucket/data/train.csv does not exist The address of the file is proper as I have uploaded it using the console in the above mentioned bucket. Also the Cloud ML is working in the

问题 I am new to training on Google Cloud. When I am running the training job, I get the following error: (gcloud.ml-engine.jobs.submit.training) Could not copy [research/dist/object_detection-0.1.tar.gz] to [training/packages/c5292b23e57f357dc2d63baab473c04337dbadd2deeb10965e743cd8422b964f/object_detection-0.1.tar.gz]. Please retry: HTTPError 404: Not Found I am using this to run the training job gcloud ml-engine jobs submit training job1 \ --job-dir=gs://${ml-project-neu}/training \ --packages

问题 I followed the 'Getting Started' tutorial for Cloud Machine Learning Engine and deployed it. I can pass an input file containing JSON instances to Batch Prediction service and it returns a file containing the predictions. How can I pass an instance key (index number) through the application graph unaltered so that the prediction contain the key and I know which JSON prediction belongs to which JSON input? It probably can be done by adding/changing a few lines in the original tutorial code

问题 I am following the flowers tutorial for re-training inception on google cloud ml. I can run the tutorial, train, predict, just fine. I then substituted the flowers dataset for a test dataset of my own. Optical character recognition of image digits. When training the model I receive the error: Invalid argument: Received a label value of 13 which is outside the valid range of [0, 6). Label values: 6 3 2 7 3 7 6 6 12 6 5 2 3 6 8 8 8 8 4 6 5 13 7 4 8 12 5 2 4 12 12 8 8 8 12 6 4 2 12 4 3 8 2 6 8

问题 I am trying to use an existing TensorFlow model, which I have so far run locally, with Google Cloud ML Engine. The model currently obtains its training data by passing filesnames such as my_model.train and my_model.eval into tf.data.TextLineDataset. These filenames are now hardcoded in the model's trainer, but I plan to refactor it such that it obtains them as training application parameters (along with --job-dir ) on the command line instead; e.g. like so: my_trainer.pl --job-dir job \ -

问题 When running in Cloud Shell the sample code from this posting by Google's Slaven Bilac, an error occurs. chuck@wordone-wordtwo-1234567:~/google-cloud-ml/samples/flowers$ ./sample.sh Your active configuration is: [cloudshell-270] Using job id: flowers_chuck_20170130_212715 # Takes about 30 mins to preprocess everything. We serialize the two # preprocess.py synchronous calls just for shell scripting ease; you could use # --runner DataflowPipelineRunner to run them asynchronously. Typically, #