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Table of contents
1.
Introduction
2.
TensorFlow
3.
Cloud 
4.
TensorFlow Cloud 
5.
Difference Between Cloud And TensorFlow Cloud
6.
Advantages of TensorFlow Cloud
7.
Implementing a model using Tensorflow
7.1.
Code
7.2.
Code
7.3.
Code
7.4.
Code
7.5.
Code
8.
Model deployment on Tensorflow Cloud
9.
Frequently Asked Questions
9.1.
Why some of the operations of TensorFlow are not implemented in TensorFlow Lite?
9.2.
What is the use of a .tflite file?
9.3.
Is Google Cloud an Open Source Service?
9.4.
Which one is more useful, TensorFlow or Keras?
9.5.
Is it possible to Run TensorFlow in Parallel?
10.
Conclusion 
Last Updated: Mar 27, 2024
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Introduction to Tensorflow Cloud

Author Lali Sharma
3 upvotes
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Have you ever tried Tensorflow on the cloud or just imported and worked with TensorFlow on the cloud platforms like google colab? Giving it a try won't be wrong?

intro image

This Article contains all the information regarding Tensorflow and cloud computing, through which you will make you learn about Tensorflow Cloud and the advantages of using TensorFlow cloud, and the model deployment on Tensorflow Cloud. Let's jump straight into it, starting with Tensorflow.

TensorFlow

Google's TensorFlow is an Open-Source library in python developed by google researchers for deep learning, machine learning, and statistical and Natural language processing. Tensorflow is used to help the developer and user with data automation, model tracking, model retraining, and performance monitoring. Also, it is free to use and thus very helpful in implementing and deploying models related to automation, deep learning, and machine learning.

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Cloud 

A cloud or cloud computing is a delivery service provider over the networks. In simple words, it provides the resources of storage, databases, networking, Software, analytics, and intelligence over the internet, and these services or resources are fast, flexible, and economical. The services cloud includes infrastructure, platforms, or  Software. Cloud is also very beneficial to overcome the drawbacks of limited memory storage in a computer system, whereas cloud computing platforms have massive memory storage.

TensorFlow Cloud 

tensorflow cloud

Tensorflow cloud is a library or repository to connect the local environment to Google Cloud and provides APIs that make the transition from the local model building easy and debugging to distributed training and hyperparameter tuning on Google Cloud. The Tensorflow cloud is developed and thus associated with Google services. The training and training of the model can be done by directly connecting or uploading the Colab or Kaggle Notebook or a local script without needing to use Cloud Console.

  • TensorFlow cloud is a client-side library for training your TensorFlow model on Vertex AI.
     
  • It provides API for a seamless transition from debugging to distributed training and hyperparameter tuning in Google cloud.
     
  • TensorFlow cloud handles cloud-specific tasks such as automatically creating virtual machine instances and setting distribution strategies for machine learning models.
     
  • TensorFlow cloud handles cloud-specific tasks such as automatically creating virtual machine instances and setting distribution strategies for machine learning models.
     
  • In the case of distributed tuning jobs, the TensorFlow cloud automatically sets up model callbacks to capture model checkpoints and tensorboard logs.
     

TensorFlow Cloud is the step-by-step model implementation using the machine learning algorithm, which has a massive amount of data and requires more than one GPU for model building. The Colab and Kaggle Notebooks and the Platform they use for model building and implementation often need more accuracy with which massive data is required to be trained. To overcome this drawback, Google researchers developed the TensorFlow cloud, which ensures that the assets are configured with data size that can significantly be compared to Notebooks with one free GPU and allows the model to train and test with the highest accuracy obtained.

Difference Between Cloud And TensorFlow Cloud

Cloud 

TensorFlow Cloud 

Cloud is a delivery service provider over the internet. The services include servers, storage, database, networking, Software, analytics, and intelligence over the internet. The TensorFlow Cloud provides APIs that allow going quickly from debugging and training TensorFlow code in the local environment to distributed training in the cloud.
Different companies own their clouds to maintain data at a personal level. TensorFlow Cloud is developed and owned by Google.
There are three main types of services provided by the cloud -Infrastructure as service(IaaS), Platform as a service(PaaS), and Software as a service(SaaS). The types of TensorFlow is the services or function it provides. It allows the running of API for GCP training/tuning.

Advantages of TensorFlow Cloud

image

TensorFlow and its cloud services are convenient when working with machine learning, cloud computing, automation, statistical analysis, and Deep learning. The model can be trained using massive amounts of data, less time-consuming, and high-speed processing using more than one GPU owned by the cloud service.

  • Scalability: TensorFlow cloud is just like a prominent online workplace; it is not limited to the computer or PC of the owner, and it is not related to one specific device.
     
  • Open Source Platform: TensorFlow Cloud is also a free and open source where one can simply load the simple Colab or Kaggle NoteBook and able to build the model with a massive amount of data, larger storage capacity, and less time for computation, and the model build is highly accurate. 
     
  • Graphs: TensorFlow Cloud has a better GPU and processors to process fast; thus, the more accurate model can produce compelling visualizations. Also, the cloud owns some fantastic visualization tools.
     
  • Debugging: TensorFlow Cloud helps execute the subpart of a graph, which gives it an upper hand and can be introduced and retrieve discrete data.
     
  • Pipelining: TensorFlow is manufactured in such a way that it allows to use of various backend software such as GPU, ASIC, etc.
     
  • Parallelism: TensorFlow Cloud uses different distribution computing or distributed computer strategies like parallel processing or Virtual machine the concept for the training and testing of models effective also faster than other single GPU processors and CPU systems. It is possible to run the code on an architecture-based model.
     
  • Compatible: The compatibility of TensorFlow Cloud is with all the object-oriented languages such as C++, JavaScript, Python, C#, Ruby, and Swift.
     
  • Architectural support: TensorFlow Cloud works on the architecture of TensorFlow is embedded with more functionality and storage. It supports faster computations than GPU and CPU. A model built on the TensorFlow TPU architecture can be easily implemented and deployed over the cloud at a cheaper rate, and execution is faster.

Implementing a model using Tensorflow

TensorFlow in machine learning is the most prominent method leading to an accurate result of the training model. Tensorflow can optimize the performance of the machine learning models before and after training.

Below is the implementation of the machine learning supervised learning

  1. Model of the diabetes datasethttps://drive.google.com/file/d/1thSxj95Tfgz6I1KOFsmXz4OHv8FcLHk4/view?usp=share_link

The diabetes dataset is a 768x9 column described below:

Pregnancies — Number of times pregnant

GlucosePlasma — glucose concentration 2 hours in an oral glucose tolerance

test

Blood Pressure — Diastolic blood pressure (mm Hg)

SkinThickness — Triceps skin-fold thickness (mm)

Insulin — Two hours of serum insulin (mu U/ml)

BMI — Body mass index (weight in kg/(height in m)2)

Diabetes Pedigree Function — Diabetes pedigree function

Age — Age in years

Outcome — Class variable (0 or 1)

This model implementation is focused on finding the model accuracy using the Binary Crossentropy model, which is used to calculate the cross entropy loss between the predicted and actual classes. As the data is categorical, this method is best suitable for finding the accuracy of the trained model.

The model implementation is:

https://colab.research.google.com/drive/1XkpEXsyOSMjXXdWnuM5DviTrkFS42GVV?usp=share_link

Step 1: The environment setup is the first requirement before implementing

The Installation of the imbalanced-learn Package: It is a python package for handling imbalanced datasets in the building of machine learning models. There can be many data samples that are not equally distributed and are managed by an imbalanced-learning algorithm.

!pip install -U imbalanced-learn


Step 2: Importing all the required libraries for the model building

import numpy as np
import pandas as pd
import matplotlib. pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from imblearn.over_sampling import RandomOverSampler


Step 3: Dataset representation 

df = pd.read_csv("/diabetes (1).csv")
df

Output:

output

Step 4: Data description and information 

df.head()
df.info()
df.describe()
df.columns


Output:

output
output

Step 5: Histogram plotting between "Diabetes" and “No Diabetes"  VS "Probability"

Code

for i in range(len(df. columns[:-1])):
  label = df. columns[i]
  plt. hist(df[df['Outcome']==1][label], color='blue', label="Diabetes", alpha=0.7, density=True, bins=15)
  plt. hist(df[df['Outcome']==0][label], color='red', label="No diabetes", alpha=0.7, density=True, bins=15)
  plt. title(label)
  plt.ylabel("Probability")
  plt.xlabel(label)
  plt.legend()
  plt.show()
output
output

Step 6: Create the dependent and independent variables and apply the StandarScaler to scale the dataset in a smaller range. 

Code

X = df[df.columns[:-1]].values
y = df[df.columns[-1]].values

Output:

output

Code

The below code is a python sklearn library for standard values of data sets.

scaler = StandardScaler()
X = scaler.fit_transform(X)
data = np.hstack((X, np.reshape(y, (-1, 1))))
transformed_df = pd.DataFrame(data, columns=df.columns


Step 7:  Splitting data into Training testing and validation and data transformation  

Code

over = RandomOverSampler()
X, y = over.fit_resample(X, y)
data = np.hstack((X, np. reshape(y, (-1, 1))))
transformed_df = pd.DataFrame(data, columns=df.columns)


X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=0)
X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=0)


Output:

output

Step 8:  Model building and results of training, testing, and validation 

Code

model = tf. Keras.Sequential([
                             tf.keras.layers.Dense(16, activation='relu'), # if x <= 0 --> 0, x > 0 --> x
                             tf.keras.layers.Dense(16, activation='relu'),
                             tf.keras.layers.Dense(1, activation="sigmoid")
])


model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=['accuracy'])


model. evaluate(X_train, y_train)
model. evaluate(X_valid, y_valid)
model. evaluate(X_test, y_test)


Output:

output
output
output

CrossEntropy 

Training 

76%

42%

Validating 

77%

37%

Testing 

75%

40%

Model deployment on Tensorflow Cloud

To deploy Tensorow, machine learning, or any other models and algorithm, the very first and essential requirement is to learn about the cloud and the working environment of the cloud. Although the cloud is open-source, its services are generally paid, so with proper knowledge, there may be a gain of money. The steps required for deploying a model in the cloud platform are as follows.

Step 1:  Setting up the Google Cloud Account or any other cloud platform.

Account setup generally requires basic personal information such as name, email, and mobile number. Also, these platform requirements are to accept the terms and conditions and usually require Account details or bank details as the services on the cloud are paid after one or two months of a free trial.

step1

Step 2: Create a Project 

The project is based on a TensorFlow project as new project, also connecting the Colab Notebook. The model is implemented in colab.

step2

Step 3: Deploy Deep Learning Virtual Machine 

Step 4: Access the Colab Notebook GUI 

To create a new project in Google Cloud Platform (GCP), 

  • Login to your Google account 
  • Go to https://console.cloud.google.com 
  • Click on the Project dropdown and click on the 'NEW PROJECT.' 

Command to connect to the colab Notebook 

from google. colab import auth
auth.authenticate_user()


Then after replacing “put_your_project_id”:

CLOUD_PROJECT_NEW = “put_your_project_id”
BUCKET = “gs :// + CLOUD_PROJECT + ‘-tf2-models’


Step 5: Adding GPSs to the created Virtual Machines 

Step 6: Change Virtual Machine Configuration.

Frequently Asked Questions

Why some of the operations of TensorFlow are not implemented in TensorFlow Lite?

The TensorFlow lite is the lighter version of TensorFlow. The Lite version has certain limitations; thus, it is only able to support a few basic operations.

What is the use of a .tflite file?

The file called .tflite is the essential file and easiest file for visualizing a TensorFlow Model.

Is Google Cloud an Open Source Service?

The Google Cloud is a partially open Source Service as it provides cloud services for free for a limited time, and then you only have to pay for the services you want to use.

Which one is more useful, TensorFlow or Keras?

TensorFlow is more useful than Keras because Keras is the API within TensorFlow.

Is it possible to Run TensorFlow in Parallel?

TensorFlow can run as a thread; thus, it is possible to run it in parallel. Small processes TensorFlow can be used for creating Thread.

Conclusion 

This Article contains an Introduction to Tensorflow Cloud, the working and implementation of Tensorflow Cloud, and Also covers both comparisons between Cloud and Tensorflow Cloud; this Article also includes a machine learning-based tensorflow implementation. 

To learn more about TensorFlowCloud, and TensorFlowCloudyou can check coding ninjas blogs and provide the best coding and computer science content.
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