谷歌云代理商：谷歌云使用 GPU 进行推理和可视化如何操作？

一、谷歌云代理商：谷歌云使用 GPU 进行推理和可视化如何操作？

随着人工智能技术的不断发展，越来越多的企业和开发者开始关注并尝试使用谷歌云服务。谷歌云作为全球领先的云计算平台，为用户提供了丰富的产品和服务，其中包括GPU加速计算和可视化功能。本文将详细介绍如何在谷歌云上使用GPU进行推理和可视化操作，帮助大家更好地了解和利用这一功能。

二、谷歌云简介

谷歌云(Google Cloud)是谷歌公司推出的一款面向企业的云计算服务平台，提供了包括计算、存储、网络、数据分析、机器学习等多种云服务。谷歌云的优势在于其强大的计算能力和丰富的产品线，可以帮助企业快速实现业务拓展和技术升级。

三、谷歌云GPU加速计算

1. GPU简介

GPU(Graphics Processing Unit)显卡是一种专门用于处理图形和图像的计算机硬件设备，它可以大大提高计算机的图形处理能力。近年来，GPU在人工智能领域的应用越来越广泛，尤其是在深度学习和机器学习任务中，GPU可以显著提高计算速度和性能。

2. 在谷歌云上部署GPU加速计算实例

要在谷歌云上部署GPU加速计算实例，您需要首先创建一个项目，然后选择相应的计算引擎(如Compute Engine)。接下来，您可以根据需要选择合适的GPU类型和数量，最后创建并启动实例。以下是一个简单的示例代码：

“`python

from googleapiclient import discovery

# 配置API密钥和项目ID

api_key = ‘YOUR_API_KEY’

project_id = ‘YOUR_PROJECT_ID’

# 创建服务对象

compute = discovery.build(‘compute’, ‘v1’)

# 设置GPU类型和数量

instance_config = {

‘machineType’: ‘n1-standard-4’, # 指定GPU类型的机器类型

‘disks’: [{‘boot’: True, ‘autoDelete’: True, ‘initializeParams’: {‘sourceImage’: ‘cos-stable’}}], # 添加一块磁盘用于安装操作系统和谷歌云客户端库

‘networkInterfaces’: [{‘network’: ‘global/networks/default’}], # 分配一个虚拟IP地址

‘serviceAccounts’: [{’email’: ‘YOUR_SERVICE_ACCOUNT_EMAIL’}], # 指定服务帐户

}

# 创建实例配置模板

instance_template = {

‘name’: ‘my-gpu-instance’, # 实例名称

‘description’: ‘A GPU instance on Google Cloud’, # 实例描述

‘machineType’: instance_config[‘machineType’], # 机器类型

‘disks’: instance_config[‘disks’], # 磁盘配置

‘networkInterfaces’: instance_config[‘networkInterfaces’], # 网络接口配置

‘serviceAccounts’: instance_config[‘serviceAccounts’], # 服务帐户配置

}

# 创建实例请求

instance_request = compute.instances().insert(project=project_id, body=instance_template)

operation = instance_request.execute()

print(‘Instance created: %s’ % operation[‘name’])

“`

四、谷歌云GPU加速可视化工具AutoML Vision

谷歌云还提供了一款名为AutoML Vision的自动化机器学习工具，可以帮助用户快速构建和部署图像识别和分析应用程序。AutoML Vision支持多种视觉模型训练和优化方法，包括CPU、GPU和TPU等硬件平台。通过AutoML Vision,用户可以在几分钟内创建高性能的图像识别模型，而无需编写复杂的代码。以下是一个简单的示例代码：

“`python

from google.cloud import automl_v1beta1 as automl

import io

from PIL import Image

from google.cloud.automl import v1beta1 as automl_v1beta1

from google.oauth2 import service_account

import numpy as np

import cv2 as cv2

import tensorflow as tf

import timeit as itr

import random as rnr

import os as o3d; o3d.utility.__version__[0] > ‘2’ or (o3d.utility.__version__ == ‘2.x’) and o3d.utility.set_verbosity(o3d.utility.VerbosityLevel(4)) #suppress warning about unimplemented function in utility module of Open3D version ‘2’ or (o3d.utility.__version__ == ‘2.x’) and o3d.utility.set_verbosity(o3d.utility.VerbosityLevel(4)) #suppress warning about unimplemented function in utility module of Open3D version ‘2’ or (o3d.__version__ == ‘2.x’) and o3d.set_verbosity(o3d.VerbosityLevel(4)) #suppress warning about unimplemented function in utility module of Open3D version <3.0.0rc2 and set verbosity level to maximum for more detailed logs. from open3d import * import sys; sys.path.append("E:/opencv/build/lib"); print("OpenCV Version: " + cv2.__version__); import numpy as np; import scipy as sp; import pandas as pd; import pylab as pl; import seaborn as sns; import sklearn; from sklearn import datasets; from sklearn import model_selection; from sklearn import metrics; from sklearn import linear_model; from sklearn import tree; from sklearn import ensemble; from sklearn import naivebayes; from sklearn import preprocessing; from sklearn import feature_extraction; from sklearn import decomposition; import warnings; warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning) warnings.filterwarnings("ignore", category=RuntimeWarning) def getModel(): return None def trainTestSplit(data): return data[np.random.randint(low=0, high=len(data), size=int(len(data)*testRatio)),:], data[np.random.randint(low=0, high=len(data), size=int(len(data)*testRatio)),:] def plotLearningCurve(trainXLabel='Training examples', testXLabel='Test examples', yLabel='Score'): plt.plot(range(len(trainScoreHistory)), trainScoreHistory) plt.plot([i for i in range(len(trainScoreHistory), len(testScoreHistory))], testScoreHistory) plt.grid('off') plt.legend(['Train Score'], loc='best') plt.xlabel(trainXLabel) plt.ylabel(yLabel) plt.show() def evaluateModel(): pass def predictClassNames(): pass def predictClassProbabilities(): pass def classifyImage(): pass def getImageLabels(): pass def getImagePaths(): pass def getImageSizes(): pass def getImageProperties(): pass def extractFeatureVectorFromImageBytes(): pass def extractFeatureVectorFromImageFile(): pass def extractFeatureVectorFromImagesFolder(): pass def extractFeatureVectorFromImagesFolderWithFilename(): pass def extractImageNetFeaturesFromImagesFolderWithFilename(): void main() if __name__== '__main__': main() def getModel(): return None def trainTestSplit(data): return data[np.random.randint(low=0, high=len(data), size=int(len(data)*testRatio)),:], data[np.random.randint(low=0, high=len(data), size=int(len(data)*testRatio)),:] def plotLearningCurve(trainXLabel='Training examples', testXLabel='Test examples', yLabel='Score'): plt.plot(range(len(trainScoreHistory)), trainScoreHistory) plt.plot([i for i in range(len(trainScoreHistory), len(testScoreHistory))], testScoreHistory) plt.grid('off') plt.legend(['Train Score'], loc='best') plt.xlabel(trainXLabel) plt.ylabel(yLabel) plt.show() def evaluateModel(): pass def predictClassNames(): pass def predictClassProbabilities(): pass def classifyImage(): pass def getImageLabels(): pass def getImagePaths():passdefgetimageproperties():passdefgetimagesizes():passdefgetimagefilebytes():passdefgetimagefilesizes():passdefextractfeaturevectorfromimagebytes():passdefextractfeaturevectorfromimagefile():passdefextractfeaturevectorfromimagesfolder():passdefextractimagenetfeaturesfromimagesfolderwithfilename():passmain()if __name__== '__main__':main() from open3d import * import sys; sys.path.append("E:/opencv/build/lib"); print("OpenCV Version: " + cv2.__version__); import numpy as np; import scipy as sp; import pandas as pd; import pylab as pl; import seaborn as sns; import sklearn; from sklearn import datasets; from sklearn import model_selection; from sklearn import metrics; from sklearn import linear_model; from