Installing TensorFlow: The first step in AI development

With the rise of artificial intelligence and deep learning, many people are wondering how to install TensorFlow. TensorFlow is an open-source library for deep learning development that makes it easy to implement and train complex models. From installation to use cases, this article will teach you how to get started with TensorFlow. We'll walk you through it step-by-step so you don't miss any important information, so follow along!

How to install TensorFlow

There are several ways to install TensorFlow, but the simplest is to use the pip TensorFlow is available in CPU and GPU versions, so you'll need to install it for your system.

1. basic installation (CPU version)

The default version of TensorFlow is the one that runs on the CPU, which is sufficient for small-scale experiments with deep learning models or for environments with only a CPU. You can install it with the command below.

pip install tensorflow

# installation result (below is the result of reinstalling from the existing installation)
(myenv) C:\projects\Project_Python>pip install tensorflow
Requirement already satisfied: tensorflow in c:\users\user\.conda\envs\myenv\lib\site-packages (2.16.1)
Requirement already satisfied: tensorflow-intel==2.16.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow) (2.16.1)
Requirement already satisfied: absl-py>=1.0.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)
Requirement already satisfied: astunparse>=1.6.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)
Requirement already satisfied: flatbuffers>=23.5.26 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (24.3.25)
Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)
Requirement already satisfied: google-pasta>=0.1.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)
Requirement already satisfied: h5py>=3.10.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.11.0)
Requirement already satisfied: libclang>=13.0.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (18.1.1)
Requirement already satisfied: ml-dtypes~=0.3.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)
Requirement already satisfied: opt-einsum>=2.3.2 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)
Requirement already satisfied: packaging in c:\users\user\appdata\roaming\python\python311\site-packages (from tensorflow-intel==2.16.1->tensorflow) (23.0)
Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,=3.20.3 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)
Requirement already satisfied: requests=2.21.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.31.0)
Requirement already satisfied: setuptools in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (70.1.1)
Requirement already satisfied: six>=1.12.0 in c:\users\user\appdata\roaming\python\python311\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)
Requirement already satisfied: termcolor>=1.1.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.4.0)
Requirement already satisfied: typing-extensions>=3.6.6 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.10.0)
Requirement already satisfied: wrapt>=1.11.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)
Requirement already satisfied: grpcio=1.24.3 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.64.1)
Requirement already satisfied: tensorboard=2.16 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)
Requirement already satisfied: keras>=3.0.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.4.0)
Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.31.0)
Requirement already satisfied: numpy=1.23.5 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.26.4)
Requirement already satisfied: wheel=0.23.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.41.2)
Requirement already satisfied: rich in c:\users\user\.conda\envs\myenv\lib\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)
Requirement already satisfied: namex in c:\users\user\.conda\envs\myenv\lib\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.8)
Requirement already satisfied: optree in c:\users\user\.conda\envs\myenv\lib\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)
Requirement already satisfied: charset-normalizer=2 in c:\users\user\.conda\envs\myenv\lib\site-packages (from requests=2.21.0->tensorflow-intel==2.16.1->tensorflow) (3.3.2)
Requirement already satisfied: idna=2.5 in c:\users\user\.conda\envs\myenv\lib\site-packages (from requests=2.21.0->tensorflow-intel==2.16.1->tensorflow) (3.6)
Requirement already satisfied: urllib3=1.21.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from requests=2.21.0->tensorflow-intel==2.16.1->tensorflow) (2.1.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\user\.conda\envs\myenv\lib\site-packages (from requests=2.21.0->tensorflow-intel==2.16.1->tensorflow) (2023.11.17)
Requirement already satisfied: markdown>=2.6.8 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorboard=2.16->tensorflow-intel==2.16.1->tensorflow) (3.6)
Requirement already satisfied: tensorboard-data-server=0.7.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorboard=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)
Requirement already satisfied: werkzeug>=1.0.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from tensorboard=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.3)
Requirement already satisfied: MarkupSafe>=2.1.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from werkzeug>=1.0.1->tensorboard=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.3)
Requirement already satisfied: markdown-it-py>=2.2.0 in c:\users\user\.conda\envs\myenv\lib\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)
Requirement already satisfied: pygments=2.13.0 in c:\users\user\appdata\roaming\python\python311\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.14.0)
Requirement already satisfied: mdurl~=0.1 in c:\users\user\.conda\envs\myenv\lib\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)

2. Install the GPU version

If GPUthen installing the GPU version of TensorFlow will significantly improve performance. However, installing the GPU version requires CUDAand cuDNN Make sure your GPU is supported before installing.

pip install tensorflow-gpu

Checking your TensorFlow version

Once you're done installing, it's also a good idea to know how to check the version of TensorFlow you have installed, as you may have projects or libraries that require a specific version of TensorFlow.

Checking the version is easy with the command below.

import tensorflow as tf
print(tf.__version__)

Running this code will output the installed version of TensorFlow as shown below.

Messages starting with oneDNN custom operations are on are logs generated by TensorFlow that inform you that TensorFlow is using custom operations from the Deep Neural Network Library (oneDNN). This is done to optimize CPU performance by using the oneDNNis used to help speed up the computation, but in doing so, the Order of floating-point operationsDepending on the Subtle differencesmay occur. This is a warning that floating-point operations can have subtle differences depending on the order of computation, so you shouldn't expect exactly the same results. In most cases, you shouldn't care about this log because it's information for performance optimization, To turn off oneDNN optimization tf_enable_onednn_options=0 You can disable oneDNN by setting an environment variable. This allows you to change the order of operations to avoid small floating point errors.

Setting up TensorFlow

To check if TensorFlow works well on GPUs, you can run the code below to see if your GPU is being recognized correctly.

import tensorflow as tf
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT available")

If you see "GPU is available" when you run this code, it means that TensorFlow is using your GPU. If your GPU is not recognized, there may have been a problem with your CUDA and cuDNN installation, so double-check.

TensorFlow Use Cases

TensorFlow is used in a variety of AI projects, including image classification, natural language processing, and autonomous driving. Here are a few examples.

1. categorize images

Categorizing imagesis a classic use case for TensorFlow. MNIST, CIFAR-10, ImageNet The same image dataset can be used to build deep learning models, which are used to automatically classify images. This allows you to automatically process and classify image data for object recognition, face recognition, disease diagnosis, and more.

• Example: Google Photosleverages deep learning models powered by TensorFlow to automatically categorize and tag photos. It recognizes people, places, objects, and more in your photos to make them easier to search and organize.

2. natural language processing

Natural language processing (NLP) is the art of analyzing and processing textual data, and TensorFlow plays an important role in this field. Categorizing text, Create text, Translation, Chatbots For many of the same tasks, TensorFlow is effective at processing and training on large amounts of textual data.

• Google TranslateThe Google Translate system uses TensorFlow to automate translations between multiple languages, enabling natural translations in real-time. BERT, GPThave been implemented in TensorFlow to demonstrate better language understanding and generation capabilities.

3. autonomous driving

Autonomous vehiclesmust analyze and respond to various situations on the road in real time. Data collected by sensors such as cameras, LiDAR, and radar is processed through deep learning models based on tensorflow to recognize vehicles, pedestrians, traffic lights, and more in the environment.

• Tesla Autopilot System: Tesla's self-driving cars use deep learning-based computer vision technology to recognize the road environment in real time. TensorFlow is used to analyze their surroundings and predict their path in real time while driving. Using TensorFlow's image recognition models, self-driving cars can recognize and avoid objects such as pedestrians and vehicles.

4. medical image analysis

Medical image analyticsTensorFlow is useful for analyzing medical images and diagnosing diseases. Medical images such as X-rays, MRIs, and CT scans can be analyzed to detect diseases such as cancer, tumors, and more at an early stage. Deep learning models can learn from tons of medical data to make diagnoses faster and more accurately than human experts.

• Stanford University's AI for skin cancer diagnosisA Stanford University research team has developed a model for diagnosing skin cancer using TensorFlow, which uses deep learning to learn about various skin conditions and then performs expert-level diagnoses.
• COVID-19 DiagnosisDuring the COVID-19 pandemic, TensorFlow was used to analyze X-ray images to detect signs of lung disease. This allowed healthcare providers to quickly assess a patient's condition and determine the appropriate treatment.

5. Recommendation system

Recommendation systemis a technology that delivers personalized content based on user behavioral data. TensorFlow is used to build systems that learn from large amounts of data to recommend the right products, videos, music, and more for each user.

• Netflix Recommendation System: Netflix uses TensorFlow to recommend new content based on the movies and shows you've watched. TensorFlow's deep learning models analyze viewing patterns and preferences to deliver personalized recommendations to each user in real time.
• Amazon's personalized recommendationsAmazon uses a recommendation system powered by TensorFlow to recommend products that users are more likely to buy. This improves the shopping experience and helps keep customers coming back for more.

Conclusion: From installing to utilizing TensorFlow

In this article, we've covered everything from how to install TensorFlow to GPU settings, version checking, and how to use TensorFlow in practice. TensorFlow is an invaluable tool for those new to deep learning and for advanced researchers alike. Now that you've installed TensorFlow, you're ready to get started. Once you've installed TensorFlow, you're ready to get started with your AI project!