I am trying to deploy custom model on AI platform. I have followed the steps as mentioned in the Google Document: https://cloud.google.com/ai-platform/prediction/docs/deploying-models#global-endpoint.
The saved model is stored in Google Cloud Storage and trained with python 3.7.
These are the gcloud commands used to deploy
gcloud ai-platform models create title_topic_custom \
--regions=europe-west1 --enable_logging
MODEL_DIR="gs://ai_platform_custom/SavedModel"
VERSION_NAME="V3"
MODEL_NAME="title_topic_custom"
CUSTOM_CODE_PATH="gs://ai_platform_custom/SavedModel/my_custom_code-0.1.tar.gz"
PREDICTOR_CLASS="predictor.py.MyPredictor"
gcloud beta ai-platform versions create $VERSION_NAME \
--model=$MODEL_NAME \
--origin=$MODEL_DIR \
--runtime-version=2.1 \
--python-version=3.7 \
--machine-type=mls1-c1-m2 \
--package-uris=$CUSTOM_CODE_PATH \
--prediction-class=$PREDICTOR_CLASS
I get the following error after executing those commands:
Using endpoint [https://ml.googleapis.com/]
Creating version (this might take a few minutes)......failed.
ERROR: (gcloud.beta.ai-platform.versions.create) Create Version failed. Bad model detected with error: "There was a problem processing the user code: predictor.py.MyPredictor cannot be found. Please make sure (1) prediction_class is the fully qualified function name, and (2) it uses the correct package name as provided by the package_uris: ['gs://ai_platform_custom/SavedModel/my_custom_code-0.1.tar.gz'] (Error code: 4)"
The predictor code is as below:
%%writefile predictor.py
import os
import spacy
import numpy as np
import joblib
import tensorflow as tf
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
class MyPredictor(object):
def __init__(self, model, topic_encoder):
self._model = model
self._nlp = spacy.load('en_core_web_sm')
self._stopwords = stopwords.words('english')
self._topic_encoder = topic_encoder
def predict(self, instances, **kwargs):
inputs = np.asarray(instances)
inputs_t = [' '.join([i for i in x.split() if i not in self._stopwords]) for x in inputs]
preprocessed_inputs = [' '.join([i.lemma_ for i in self._nlp(x)]) for x in inputs_t]
outputs = self._model.predict(preprocessed_inputs)
return [self._topic_encoder[key] for key in np.argmax(outputs, axis=1)]
@classmethod
def from_path(cls, model_dir):
model_path = os.path.join(model_dir)
model = tf.keras.models.load_model(model_path)
topic_encoder = {0:'topic1',1:'topic2',3:'topic3'}
return cls(model, topic_encoder)
This is the setup file
from setuptools import setup
setup(
name='my-custom-code',
version='0.1',
install_requires=['nltk','spacy','joblib'],
scripts=['predictor.py'])
Any workarounds?
