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Ansible VS TensorFlow

Compare Ansible VS TensorFlow and see what are their differences

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Ansible logo Ansible

Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine

TensorFlow logo TensorFlow

TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
  • Ansible Landing page
    Landing page //
    2023-02-05
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Ansible features and specs

  • Agentless
    Ansible is agentless, meaning it doesn't require any software to be installed on the remote nodes. This simplifies management and reduces overhead.
  • Ease of Use
    Ansible uses a simple, easy-to-read YAML syntax for its playbooks, reducing the learning curve and making it accessible to those without extensive programming experience.
  • Scalability
    Ansible is designed to handle large-scale deployments, making it suitable for managing numerous machines or services efficiently.
  • Extensive Modules
    Ansible has a rich library of modules that support a wide variety of system tasks, cloud providers, and application deployments, offering great versatility.
  • Strong Community
    There is a large and active Ansible community that contributes to its development and provides support, which can be valuable for troubleshooting and learning best practices.
  • Idempotency
    Tasks in Ansible are idempotent, meaning they can be run multiple times without changing the system beyond the intended final state, ensuring reliable deployments.

Possible disadvantages of Ansible

  • Performance Overhead
    Being agentless, Ansible relies on SSH for communication with nodes, which can add performance overhead, especially when managing a large number of hosts.
  • Limited Windows Support
    Ansible's core is primarily designed for Unix-like systems, and while there is support for Windows, it's not as robust or as seamless as it is for Unix/Linux systems.
  • Lack of Built-in Error Handling
    Ansible's error handling is somewhat rudimentary out-of-the-box. Complex error handling scenarios often require custom solutions, which can complicate playbooks.
  • Learning Curve for Complex Scenarios
    While simple tasks are easy to set up, more complex configurations can become challenging quickly and may require a deep understanding of Ansible's modules and templating.
  • Reliance on YAML
    The use of YAML, while human-readable, can be prone to syntax errors such as incorrect indentation, which can potentially lead to hard-to-track-down bugs.
  • Dependency on Python
    Ansible requires Python to be installed on managed nodes. This could be an issue in environments where it's not feasible or desired to have Python installed.

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

Ansible videos

+ Add

What Is Ansible? | How Ansible Works? | Ansible Tutorial For Beginners | DevOps Tools | Simplilearn

More videos:

  • Review - Automation with Ansible Playbooks | Review on Ansible Architecture
  • Review - Book Review : Mastering Ansible (Jesse Keating) by Zareef Ahmed

TensorFlow videos

+ Add

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

Category Popularity

0-100% (relative to Ansible and TensorFlow)

Ansible

TensorFlow

DevOps Tools
100 100%
DevOps Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Continuous Integration
100 100%
Continuous Integration
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Ansible and TensorFlow. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Ansible and TensorFlow

Ansible Reviews

What Are The Best Alternatives To Ansible? | Attune, Jenkins &, etc.
To put it simply, Ansible automates a wide range of IT aspects that includes configuration management, application deployment, cloud provisioning, etc. Plus, while using Ansible, you can patch your application, automate deployments, and run compliances and governance on your application. You can easily manage it by using a web interface known as Ansible Tower. Furthermore,...
Source: www.apzomedia.com
Best 8 Ansible Alternatives & equivalent in 2022
Ansible is a simple IT automation tool that is easy to deploy. It connects to your nodes and pushes out small programs called “Ansible modules” to those nodes. Then it executes these models over SSH and removes them when finished. The library of modules will reside on any machine, therefore there is no requirement for any servers and databases.
Source: www.guru99.com
Top 5 Ansible Alternatives in 2022: Server Automation Solutions by Alexander Fashakin on the 19th Aug 2021 facebook Linked In Twitter
Your project connects to Ansible through nodes called Ansible Modules. You can use these modules to manage your project. As an agentless architecture, Ansible allows you to run modules on any system or server. It doesn’t require client/server software or an agent to be installed. With Ansible, you can use Python Paramiko modules or SSH protocols.
Source: www.servertribe.com
Ansible vs Chef: What’s the Difference?
For Ansible, Simplilearn presents the Ansible Foundation Training Course. Ansible 2.0, a simple, popular, agent-free tool in the automation domain, helps increase team productivity and improve business outcomes. Learn with
Source: www.simplilearn.com
Chef vs Puppet vs Ansible
Ansible supports considerable ease of learning for the management of configurations due to YAML as the foundation language. YAML (Yet Another Markup Language) is closely similar to English and is human-readable. The server can help in pushing configurations to all the nodes. The applications of Ansible are clearly suitable for real-time execution along with the facility of...
Source: www.whizlabs.com

TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...
Source: www.assemblyai.com

Social recommendations and mentions

Ansible might be a bit more popular than TensorFlow. We know about 9 links to it since March 2021 and only 7 links to TensorFlow. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Ansible mentions (9)

  • Mentorship Group
    We are open to practice using any open-source project, however, we want to set a sharp focus on projects maintained by the Red Hat, and our own projects in the Caravana Cloud organization on github. If there is no reason to do differently, we'll build using technologies such as OpenShift, Quarkus, Ansible and related projects. - Source: dev.to / almost 2 years ago
  • Observability Mythbusters: Yes, Observability-Landscape-as-Code is a Thing
    *Codifying the deployment of the OTel Collector *(to Nomad, Kubernetes, or a VM) using tools such as Terraform, Pulumi, or Ansible. The Collector funnels your OTel data to your Observability back-end. ✅. - Source: dev.to / over 2 years ago
  • Maintenance mode - vmware.vmware_rest Ansible collection
    Most of what I've learnt today was purley from this blog and only because it's from ansible.com - dated now I guess ... Source: almost 3 years ago
  • Proactive Kubernetes Monitoring with Alerting
    I installed the helm release using Ansible, but you can install with the following helm commands:. - Source: dev.to / almost 3 years ago
  • Cannot run a playbook in crontab - Python error
    [root@ansible ~]# pip show ansible Name: ansible Version: 2.9.25 Summary: Radically simple IT automation Home-page: https://ansible.com/ Author: Ansible, Inc. Author-email: info@ansible.com License: GPLv3+ Location: /usr/lib/python2.7/site-packagesRequires: jinja2, PyYAML, cryptography Required-by:. Source: over 3 years ago
View more

TensorFlow mentions (7)

  • Creating Image Frames from Videos for Deep Learning Models
    Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 2 years ago
  • Need help with a Tensorflow function
    So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 3 years ago
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: almost 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
View more

What are some alternatives?

When comparing Ansible and TensorFlow, you can also consider the following products

Chef - Automation for all of your technology. Overcome the complexity and rapidly ship your infrastructure and apps anywhere with automation.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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