FlatBuffers.jl: A Pure Julia Implementation Of Google Flatbuffers

FlatBuffers

A Julia implementation of google flatbuffers

Installation

The package is registered in METADATA.jl and so can be installed with Pkg.add.

julia> Pkg.add("FlatBuffers")

Documentation

  • STABLEmost recently tagged version of the documentation.
  • LATESTin-development version of the documentation.

Project Status

The package is tested against Julia 1.0, 1.1, 1.2, 1.3, and nightly on Linux, OS X, and Windows.

Contributing and Questions

Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems or would just like to ask a question.

Download Details:

Author: JuliaData
Source Code: https://github.com/JuliaData/FlatBuffers.jl 
License: View license

#julia #google 

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FlatBuffers.jl: A Pure Julia Implementation Of Google Flatbuffers

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

FlatBuffers.jl: A Pure Julia Implementation Of Google Flatbuffers

FlatBuffers

A Julia implementation of google flatbuffers

Installation

The package is registered in METADATA.jl and so can be installed with Pkg.add.

julia> Pkg.add("FlatBuffers")

Documentation

  • STABLEmost recently tagged version of the documentation.
  • LATESTin-development version of the documentation.

Project Status

The package is tested against Julia 1.0, 1.1, 1.2, 1.3, and nightly on Linux, OS X, and Windows.

Contributing and Questions

Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems or would just like to ask a question.

Download Details:

Author: JuliaData
Source Code: https://github.com/JuliaData/FlatBuffers.jl 
License: View license

#julia #google 

What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

Image for post

Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

Image for post

Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

Image for post

On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud

Monty  Boehm

Monty Boehm

1659664380

GoogleCharts.jl: Julia interface to Google Chart Tools

GoogleCharts

Julia interface to Google Chart Tools.

A Google chart involves basically four steps:

  • a specification of a Google "DataTable"
  • a specification of chart options
  • a call to make the type of chart desired.
  • a call to draw the chart

This package allows this to be done within julia by

mapping a DataFrame object into a Google DataTable.

mapping a Dict of options into a JSON object of chart options. Many of these options can be specified through keyword arguments.

providing various constructors to make the type of chart

providing a method to see the charts. This is called through Julia's show mechanism. In general, the render method can draw the chart or charts to an IOStream or file.

A basic usage (see the test/ directory for more)

using GoogleCharts, DataFrames

scatter_data = DataFrame(
    Age    = [8,  4,   11, 4, 3,   6.5],
    Weight = [12, 5.5, 14, 5, 3.5, 7  ]
)

options = Dict(:title => "Age vs. Weight comparison",
           :hAxis =>  Dict(:title => "Age", 
                       :minValue => 0, 
                       :maxValue => 15),    
           :vAxis =>  Dict(:title => "Weight", 
                       :minValue => 0, 
                       :maxValue => 15)
)

scatter_chart(scatter_data, options)

For non-nested options, keyword arguments can be given, as opposed to a dictionary:

chart = scatter_chart(scatter_data, title="Age vs. Weight comparison")

There are constructors for the following charts (cf. Charts Gallery)

       area_chart, bar_chart, bubble_chart, candlestick_chart, column_chart, combo_chart,
       gauge_chart, geo_chart, line_chart, pie_chart, scatter_chart, stepped_area_chart,
       table_chart, tree_chart, annotated_time_line, intensity_map, motion_chart, org_chart,
       image_spark_line

The helper function help_on_chart("chart_name") will open Google's documentation for the specified chart in a local browser.

The names of the data frame are used by the various charts. The order of the columns is important to the charting tools. The "Data Format" section of each web page describes this. We don't have a mechanism in place supporting Google's "Column roles".

The options are specified through a Dict which is translated into JSON by JSON.to_json. There are numerous options described in the "Configuration Options" section of each chart's web page. Some useful ones are shown in the example to set labels for the variables and the viewport. Google charts seem to like integer ranges in the viewports by default. Top-level properties, can be set using keyword arguments.

In the tests/ subdirectory is a file with implementations with this package of the basic examples from Google's web pages. Some additional examples of configurations can be found there.

The GoogleCharts.render method can draw a chart to an IOStream, a specified filename, or (when used as above) to a web page that is displayed locally. One can specify more than one chart at a time using a vector of charts.

A Plot function

There is a Plot function for plotting functions with a similar interface as Plot's plot function:

Plot(sin, 0, 2pi)

A vector of functions:

Plot([sin, u -> cos(u) > 0 ? 0 : NaN], 0, 2pi, 
       lineWidth=5, 
       title="A function and where its derivative is positive",
           vAxis=Dict(:minValue => -1.2, :maxValue => 1.2))

The Plot function uses a line_chart. The above example shows that NaN values are handled gracefully, unlike Inf values, which we replace with NaN.

Plot also works for paired vectors:

x = linspace(0, 1., 20)
y = rand(20)
Plot(x, y)                     # dot-to-dot plot
Plot(x, y, curveType="function")         # smooths things out

parametric plots

Passing a tuple of functions will produce a parametric plot:

Plot((x -> sin(2x), cos), 0, 2pi)

scatter plots

The latter shows that Plot assumes your data is a discrete approximation to a function. For scatterplots, the Scatter convenience function is given. A simple use might be:

x = linspace(0, 1., 20)
y = rand(20)
Scatter(x, y)

If the data is in a data frame format we have a interface like:

using RDatasets
mtcars = dataset("datasets", "mtcars")
Scatter(:WT, :MPG, mtcars)

And we can even use with groupby objects:

iris = dataset("datasets", "iris")
d=iris[:, [2,3,6]]          ## in the order  "x, y, grouping factor"
gp = groupby(d, :Species)
Scatter(gp)                 ## in R this would be plot(Sepal.Width ~ Sepal.Length, iris, col=Species)
                            ## or ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point()

Surface plots

Some experimental code is in place for surface plots. It needs work. The basic use is like:

surfaceplot((x,y) -> x^2 + y^2, linspace(0,1,20), linspace(0,2,20))

The above does not seem to work in many browsers and does not work reliably in IJulia (only success has been with Chrome).

TODO

The googleVis package for R does a similar thing, but has more customizability. This package should try and provide similar features. In particular, the following could be worked on:

  • Needs a julian like interface,
  • some features for interactive usage,
  • some integration with local web server.

Author: jverzani
Source Code: https://github.com/jverzani/GoogleCharts.jl 
License: View license

#julia #google #charts