google-cloud-ml

I'm new to machine learning world and I have built and trained a ml model using ScikitLearn library.It works perfectly well in the Jupyter notebook but when I deployed this model to Google Cloud ML and try to serve it using a Python script, it throws an error. Here's a snippet from my model code: Updated: from sklearn.metrics import classification_report, accuracy_score from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor # define a random state state = 1 classifiers = { "Isolation Forest": IsolationForest(max_samples=len(X), contamination=outlier

I launch a tensorflow task on ML Engine and after about 2 minutes I keep getting an error message " The replica master 0 exited with a non-zero status of 1. " (The task incidentally runs fine with ml-engine local.) Question: Is there any place or log file where can I see further information on what happened? The logs viewer just gives the following: { insertId: "ibal72g1rxhr63" logName: "projects/**-***-ml/logs/ml.googleapis.com%2Fcnn180322_170649" receiveTimestamp: "2018-03-22T17:08:38.344282172Z" resource: { labels: { job_id: "cnn180322_170649" project_id: "**-***-ml" task_name: "service" }

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=(224,224,3), input_tensor=inputs, include_top=False, weights='imagenet') for layer in conv_base.layers[:-200]: layer.trainable = False last_layer = conv_base.output x = GlobalAveragePooling2D()(last_layer) x= keras.layers.GaussianNoise(0.3)(x) x = Dense(1024,name='fc-1')(x) x = keras.layers.BatchNormalization()(x) x = keras.layers.advanced_activations.LeakyReLU(0.3)(x) x = Dropout(0.4)(x) x = Dense(512

I am working on a multiclass classification problem consisting in classifying resumes. I used sklearn and its TfIdfVectorizer to get a big scipy sparse matrix that I feed in a Tensorflow model after pickling it. On my local machine, I load it, convert a small batch to dense numpy arrays and fill a feed dictionnary. Everything works great. Now I would like to do the same thing on ML cloud. My pickle is stored at gs://my-bucket/path/to/pickle but when I run my trainer, the pickle file can't be found at this URI ( IOError: [Errno 2] No such file or directory ). I am using pickle.load(open('gs:/

I'm working on machine learning and I would like to use Google Cloud ml service. At this moment, I have trained my model with retrain.py code of Tensorflow ( https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py#L103 ) and I have exported the results to a cloudml (export and export.meta files). However when I try to make a prediction of new data with command ( https://cloud.google.com/ml/reference/commandline/predict ): gcloud beta ml predict it always returns the same result (I want to predict different data). How is it possible? My data are

I'm trying to train with gcloud ml-engine jobs submit training , and job is getting stuck with the following output on logs: My config.yaml: trainingInput: scaleTier: CUSTOM masterType: standard_gpu workerType: standard_gpu parameterServerType: large_model workerCount: 1 parameterServerCount: 1 Any hints about what "grpc epoll fd: 3" means and how to fix that? My input function is feeding a 16G TFRecord from gs://, but with batch = 4, shuffle buffer_size = 4. Each input sample is a single channel 99 x 161px image: shape (15939,) - not huge. Thanks Maybe this is a bug in the Estimator