Now let’s try profiling on a code that calls other functions. In this case, you can pass the call to main() function as a string to cProfile.run() function. # Code containing multiple dunctions def create_array(): arr=[] for i in range(0,400000): arr.append(i) def print_sta...
RuntimeError: Cuda extensions are being compiled with a version of Cuda that does not match the version used to compile Pytorch binaries. so you would need to install the same CUDA toolkit locally as is used in the PyTorch wheels.
the time of this recording, the model.joblib file has to be created with Python 2.7, but the model will run in GCP on Python 3.7. Once the version is created, you can retrieve information about it by using thegcloud ai-platform versions describecommand or looking it up in ...
externals import joblib Next, import the data using read_csv() from pandas. Note that the separator is a colon (not a comma which is what most data sets are stored as in CSV format). The data is stored as a Python object named data. # Python dataset_url = 'http://mlr....
import os import logging import json import numpy import joblib def init(): """ This function is called when the container is initialized/started, typically after create/update of the deployment. You can write the logic here to perform init operations like caching the model in memory """ glo...
importosimportloggingimportjsonimportnumpyimportjoblibdefinit():""" This function is called when the container is initialized/started, typically after create/update of the deployment. You can write the logic here to perform init operations like caching the model in memory ...
('scoring_explainer') automl_model = joblib.load(automl_model_path) scoring_explainer = joblib.load(scoring_explainer_path)defrun(raw_data):data = pd.read_json(raw_data, orient='records')# Make predictionpredictions = automl_model.predict(data)# Setup for inferencing explanationsautoml_explain...
import os import logging import json import numpy import joblib def init(): """ This function is called when the container is initialized/started, typically after create/update of the deployment. You can write the logic here to perform init operations like caching the model in memory """ glo...
import os import logging import json import numpy import joblib def init(): """ This function is called when the container is initialized/started, typically after create/update of the deployment. You can write the logic here to perform init operations like caching the model in memory """ glo...
import os import logging import json import numpy import joblib def init(): """ This function is called when the container is initialized/started, typically after create/update of the deployment. You can write the logic here to perform init operations like caching the model in memory """ glo...