🎯 Practical: Install TensorFlow 2 | Tech Viral News

COLLECTED BY

Organization: Archive Team

Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.

History is littered with hundreds of conflicts over the future of a community, group, location or business that were "resolved" when one of the parties stepped ahead and destroyed what was there. With the original point of contention destroyed, the debates would fall to the wayside. Archive Team believes that by duplicated condemned data, the conversation and debate can continue, as well as the richness and insight gained by keeping the materials. Our projects have ranged in size from a single volunteer downloading the data to a small-but-critical site, to over 100 volunteers stepping forward to acquire terabytes of user-created data to save for future generations.

The main site for Archive Team is at archiveteam.org and contains up to the date information on various projects, manifestos, plans and walkthroughs.

This collection contains the output of many Archive Team projects, both ongoing and completed. Thanks to the generous providing of disk space by the Internet Archive, multi-terabyte datasets can be made available, as well as in use by the Wayback Machine, providing a path back to lost websites and work.

Our collection has grown to the point of having sub-collections for the type of data we acquire. If you are seeking to browse the contents of these collections, the Wayback Machine is the best first stop. Otherwise, you are free to dig into the stacks to see what you may find.

The Archive Team Panic Downloads are full pulldowns of currently extant websites, meant to serve as emergency backups for needed sites that are in danger of closing, or which will be missed dearly if suddenly lost due to hard drive crashes or server failures.

Collection: ArchiveBot: The Archive Team Crowdsourced Crawler

ArchiveBot is an IRC bot designed to automate the archival of smaller websites (e.g. up to a few hundred thousand URLs). You give it a URL to start at, and it grabs all content under that URL, records it in a WARC, and then uploads that WARC to ArchiveTeam servers for eventual injection into the Internet Archive (or other archive sites).

To use ArchiveBot, drop by #archivebot on EFNet. To interact with ArchiveBot, you issue commands by typing it into the channel. Note you will need channel operator permissions in order to issue archiving jobs. The dashboard shows the sites being downloaded currently.

There is a dashboard running for the archivebot process at http://www.archivebot.com.

ArchiveBot's source code can be found at https://github.com/ArchiveTeam/ArchiveBot.

TIMESTAMPS

The Wayback Machine - https://web.archive.org/web/20260404180935/https://www.tensorflow.org/install

Install TensorFlow 2

TensorFlow is tested and supported on the following 64-bit systems:

Python 3.9–3.12
Ubuntu 16.04 or later
Windows 7 or later (with C++ redistributable)

macOS 10.12.6 (Sierra) or later (no GPU support)
WSL2 via Windows 10 19044 or higher including GPUs (Experimental)

# Requires the latest pip
pip install --upgrade pip

# Current stable release for CPU
pip install tensorflow

# Current stable release for GPU (Linux / WSL2)
pip install tensorflow[and-cuda]

# Or try the preview build (unstable)
pip install tf-nightly

Download a package

Install TensorFlow with Python's pip package manager.

TensorFlow 2 packages require a pip version >19.0 (or >20.3 for macOS).

Official packages available for Ubuntu, Windows, and macOS.

Read the pip install guide

Run a TensorFlow container

The TensorFlow Docker images are already configured to run TensorFlow. A Docker container runs in a virtual environment and is the easiest way to set up GPU support.

 docker pull tensorflow/tensorflow:latest  # Download latest stable image
 docker run -it -p 8888:8888 tensorflow/tensorflow:latest-jupyter  # Start Jupyter server

Read the Docker install guide

Google Colab: An easy way to learn and use TensorFlow

No install necessary—run the TensorFlow tutorials directly in the browser with Colaboratory, a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. Read the blog post.

Build your first ML app

Create and deploy TensorFlow models on web and mobile.

Web developers

TensorFlow.js is a WebGL accelerated, JavaScript library to train and deploy ML models in the browser, Node.js, mobile, and more.

Mobile developers

TensorFlow Lite is a lightweight solution for mobile and embedded devices.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-07-12 UTC.