Open Source AI For Everyone: Three Projects to Know



  • open source AI

    We look at three open source AI projects aimed at simplifying access to AI tools and insights.

    At the intersection of open source and artificial intelligence, innovation is flourishing, and companies ranging from Google to Facebook to IBM are open sourcing AI and machine learning tools.

    According to research from IT Intelligence Markets, the global artificial intelligence software market is expected to reach 13.89 billion USD by the end of 2022. However, talk about AI has accelerated faster than actual deployments. According to a detailed McKinsey report on the growing impact of AI, “only about 20 percent of AI-aware companies are currently using one or more of its technologies in a core business process or at scale.” Here, we look at three open source AI projects aimed at simplifying access to AI tools and insights.

    TensorFlow

    Google has open sourced a software framework called TensorFlow that it spent years developing to support its AI software and other predictive and analytics programs. TensorFlow is the engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app.

    Google has also released two new AIY kits that let individuals easily get hands-on with artificial intelligence. Focused on computer vision, and voice assistants, the two kits come as small self-assembly cardboard boxes with all the components needed for use. The kits are currently available at Target in the United States, and, notably, are both based on the open source Raspberry Pi platform—more evidence of how much is going on at the intersection of open source and AI.

    Sparkling Water

    H2O.ai, formerly known as OxData, has carved out a niche in the machine learning and artificial intelligence arena, offering platform tools as well as Sparkling Water, a package that works with Apache Spark. H2O.ai’s tools, which you can access simply by downloading, operate under Apache licenses, and you can run them on clusters powered by Amazon Web Services (AWS) and others for just a few hundred dollars. Never before has this kind of AI-focused data sifting power been so affordable and easy to deploy.

    Sparkling Water includes a toolchain for building machine learning pipelines on Apache Spark. In essence, Sparkling Water is an API that allows Spark users to leverage H2O’s open source machine learning platform instead of — or alongside — the algorithms that are included in Spark’s existing machine-learning library. H2O.ai has published several use cases for how Sparkling Water and its other open tools are used in fields ranging from genomics to insurance, demonstrating that organizations everywhere can now leverage open source AI tools.

    H2O.ai’s Vinod Iyengar, who oversees business development at the company, says they are working to bring the power of AI to businesses. “Our machine learning platform features advanced algorithms that can be applied to specialized use cases and the wide variety of problems that organizations face,” he notes.

    Just as open source focused companies such as Red Hat have combined commercial products and services with free and open source ones, H2O.ai is exploring the same model on the artificial intelligence front. Driverless AI is a new commercial product from H2O.ai that aims to ease AI and data science tasks at enterprises. With Driverless AI, non-technical users can gain insights from data, optimize algorithms, and apply machine learning to business processes. Note that, although it leverages tools with open source roots, Driverless AI is a commercial product.

    Acumos

    Acumos is another open source project aimed at simplifying access to AI. Acumos AI, which is part of the LF Deep Learning Foundation, is a platform and open source framework that makes it easy to build, share, and deploy AI apps. According to the website, “It standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation.”

    The goal is to make these critical new technologies available to developers and data scientists, including those who may have limited experience with deep learning and AI. Acumos also has a thriving marketplace where you can grab and deploy applications.

    “An open and federated AI platform like the Acumos platform allows developers and companies to take advantage of the latest AI technologies and to more easily share proven models and expertise,” said Jim Zemlin, executive director at The Linux Foundation. “Acumos will benefit developers and data scientists across numerous industries and fields, from network and video analytics to content curation, threat prediction, and more.” You can learn more about Acumos here.

    The post Open Source AI For Everyone: Three Projects to Know appeared first on The Linux Foundation.

    https://www.linuxfoundation.org/blog/open-source-ai-for-everyone-three-projects-to-know/





Tmux Commands

screen and tmux

A comparison of the features (or more-so just a table of notes for accessing some of those features) for GNU screen and BSD-licensed tmux.

The formatting here is simple enough to understand (I would hope). ^ means ctrl+, so ^x is ctrl+x. M- means meta (generally left-alt or escape)+, so M-x is left-alt+x

It should be noted that this is no where near a full feature-set of either group. This - being a cheat-sheet - is just to point out the most very basic features to get you on the road.

Trust the developers and manpage writers more than me. This document is originally from 2009 when tmux was still new - since then both of these programs have had many updates and features added (not all of which have been dutifully noted here).

Action tmux screen
start a new session tmux OR
tmux new OR
tmux new-session
screen
re-attach a detached session tmux attach OR
tmux attach-session
screen-r
re-attach an attached session (detaching it from elsewhere) tmux attach -d OR
tmux attach-session -d
screen -dr
re-attach an attached session (keeping it attached elsewhere) tmux attach OR
tmux attach-session
screen -x
detach from currently attached session ^b d OR
^b :detach
^a ^d OR
^a :detach
rename-window to newname ^b , <newname> OR
^b :rename-window <newn>
^a A <newname>
list windows ^b w ^a w
list windows in chooseable menu ^a "
go to window # ^b # ^a #
go to last-active window ^b l ^a ^a
go to next window ^b n ^a n
go to previous window ^b p ^a p
see keybindings ^b ? ^a ?
list sessions ^b s OR
tmux ls OR
tmux list-sessions
screen -ls
toggle visual bell ^a ^g
create another window ^b c ^a c
exit current shell/window ^d ^d
split window/pane horizontally ^b " ^a S
split window/pane vertically ^b % ^a |
switch to other pane ^b o ^a <tab>
kill the current pane ^b x OR (logout/^D)
collapse the current pane/split (but leave processes running) ^a X
cycle location of panes ^b ^o
swap current pane with previous ^b {
swap current pane with next ^b }
show time ^b t
show numeric values of panes ^b q
toggle zoom-state of current pane (maximize/return current pane) ^b z
break the current pane out of its window (to form new window) ^b !
re-arrange current panels within same window (different layouts) ^b [space]
Kill the current window (and all panes within) ^b killw [target-window]
  • Make ISO from DVD

    In this case I had an OS install disk which was required to be on a virtual node with no optical drive, so I needed to transfer an image to the server to create a VM

    Find out which device the DVD is:

    lsblk

    Output:

    NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINT sda 8:0 0 465.8G 0 disk ├─sda1 8:1 0 1G 0 part /boot └─sda2 8:2 0 464.8G 0 part ├─centos-root 253:0 0 50G 0 lvm / ├─centos-swap 253:1 0 11.8G 0 lvm [SWAP] └─centos-home 253:2 0 403G 0 lvm /home sdb 8:16 1 14.5G 0 disk /mnt sr0 11:0 1 4.1G 0 rom /run/media/rick/CCSA_X64FRE_EN-US_DV5

    Therefore /dev/sr0 is the location , or disk to be made into an ISO

    I prefer simplicity, and sometimes deal with the fallout after the fact, however Ive repeated this countless times with success.

    dd if=/dev/sr0 of=win10.iso

    Where if=Input file and of=output file

    I chill out and do something else while the image is being copied/created, and the final output:

    8555456+0 records in 8555456+0 records out 4380393472 bytes (4.4 GB) copied, 331.937 s, 13.2 MB/s

    Fin!

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  • Recreate postrgresql database template encode to ASCII

    UPDATE pg_database SET datistemplate = FALSE WHERE datname = 'template1';

    Now we can drop it:

    DROP DATABASE template1;

    Create database from template0, with a new default encoding:

    CREATE DATABASE template1 WITH TEMPLATE = template0 ENCODING = 'UNICODE'; UPDATE pg_database SET datistemplate = TRUE WHERE datname = 'template1'; \c template1 VACUUM FREEZE;

    read more
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