I’m pleased to announce the immediate, free availability of the Nagios Plugins for Linux version 20.
Full details about what’s included can be found in the release notes.
As usual, you can download the sources from GitHub.
Bug reports, feature requests, and ideas for improvements are welcome!
Some insecure data handling issues discovered by Coverity in the new test framework have been fixed.
The Clang Static Analyser can now be executed by running the command
make -C tests check-clang-checker
in the project root directory. All the warnings spotted by this code analyser have been fixed.
A new Docker-based framework for packaging the Nagios Plugins for Linux (rpm and deb packages) is now available. The supported Linux distributions follow:
CentOS/RHEL 5, 6, 7
Debian 6, 7, 8
The messages displayed in case of a too large “count” or “delay” error have been improved.
The release 19 of the Nagios Plugins for Linux is now available for download!
You can download the tarball from GitHub.
As usual, bug reports, feature requests, and ideas for improvements are welcome!
Recent versions of multipath no longer open a multipathd socket file in the file system, but instead use an abstract namespace socket. Thanks to Chris Procter “chr15p” for reporting the issue and creating a pull request.
Fixed the performance data output.
Fixed the long-standing gcc compiler warning “dereferencing type-punned pointer might break strict-aliasing rules”. This was a false problem, but the code has been modified to quiet the warning.
A larger buffer for queries is now set, to make this plugin working with systems that have lots of mapped disks.
A framework for testing the code (make check) has been added and some tests are now available.
By default the abstract namespace socket “/org/kernel/linux/storage/multipathd” is now selected at build time.
If you need to monitor old distributions (RHEL5 and RHEL6 for instance) you need to configure this package as followed:
Here is it, version 18 of the Nagios Plugins for Linux.
It’s manly a bugfix release with a fix for an issue recently pointed out by Paul Dunkler: some of the plugins did not terminate with the correct return code when reaching a warning or critical threshold.
The check_memory plugin no more reports as cached memory the unreclaimable slab values, which cannot be reclaimed even under memory pressure.
The check_cpu plugin executed with the ‘-i | —cpuinfo‘ switch, now correctly detect on 64-bit architectures the CPU 64-bit op-mode.
A minor memory resource leak reported by the Coverity Scan tool has also been fixed.
You can download the source code (.xz compressed tarball) here and visit the GitHub project web page for more information.
As usual, bug reports, feature requests, and ideas for improvements are welcome!
Gradient boosting ensemble technique for regression
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. (source: Wikipedia)
This is a great video tutorial from Alexander Ihler, Associate Professor at Information & Computer Science, UC Irvine.
You can found other interesting data science tutorials made by Alexander Ihler in this YouTube channel:
Introduction to Deep Learning with Python
Alec Radford, Head of Research at indico Data Solutions, speaking on deep learning with Python and the Theano library.
An amazing data science YouTube tutorial with emphasis on high performance computing, natural language processing using recurrent neural nets, and large scale learning with GPUs.
This tutorial provides and excellent example of how deep learning can be practically applied to real world problems.
SlideShare presentation is available here: http://slidesha.re/1zs9M11
82 scientists and engineers simulate 37 million synapses in massive Blue Brain Project
The Blue Brain Project, the simulation core of the European Human Brain Project, has released a draft digital reconstruction of the neocortical microcircuitry of a piece of the rat-brain neocortex — about a third of a cubic millimeter of brain tissue containing about 30,000 neurons connected by nearly 40 million synapses.
The electrical behavior of the virtual brain tissue was simulated on supercomputers and found to match a range of previous observations made in experiments on the brain, validating its biological accuracy and providing new insights into the functioning of the neocortex. The project has published the full set of experimental data and the digital reconstruction, allowing other researchers to use them.
Although the resulting data collection is one of the most comprehensive to date on a part of the brain, it remains far from sufficient to reconstruct a complete map of the microcircuitry, admits Henry Markram. “We can’t and don’t have to measure everything. The brain is a well-ordered structure, so once you begin to understand the order at the microscopic level, you can start to predict much of the missing data.”
The Open Source Software (in C, C++, Java, Python) produced and used by the Blue Brain Project is available on GitHub.
Source: Neuroscientists simulate tiny part of rat brain in a supercomputer
Encrypt the entire web
Let’s Encrypt is a new, free, automated, and open certificate authority service provided by Internet Security Research Group (ISRG), a California public benefit corporation.
The project aims to make encrypted connections in the World Wide Web the default case, by providing free X.509 certificates for Transport Layer Security encryption (TLS).
They’ll be working towards general availability over the next couple of months by issuing certificates to domains participating in the beta program.
You can request that your domain be included in our beta program by clicking here.
“Getting Started with Storm“, by Jonathan Leibiusky, Gabriel Eisbruch & Dario Simonassi.
Countinuous Streaming Computation with Twitter’s Cluster Technology.
Even as big data is turning the world upside down, the next phase of the revolution is already taking shape: real-time data analysis. This hands-on guide introduces you to Storm, a distributed, JVM-based system for processing streaming data. Through simple tutorials, sample Java code, and a complete real-world scenario, you’ll learn how to build fast, fault-tolerant solutions that process results as soon as the data arrives.
Discover how easy it is to set up Storm clusters for solving various problems, including continuous data computation, distributed remote procedure calls, and data stream processing.
Apache Storm project site: https://storm.apache.org/
Note that this book is based on Storm 0.7.1 but so far the latest version is 0.9.5, so this book is quite outdated and need to be integrated with the online documentation.
Baidu hosted SF Analytics Meetup at their Sunnyvale office on August 19th, 2015 – Updates on Speech Recognition, Deep Learning and HPC.
SF Big Analytics Part 1. Deep Learning by Chief Scientist Andrew Ng
SF Big Analytics Part 2. Bryan Catanzaro, Senior Researcher: “Why is HPC So Important to AI?”
SF Big Analytics Part 3. Awni Hannun, Senior Researcher: “Update on Deep Speech”
“Is Parallel Programming Hard, And, If So, What Can You Do About It?” is a free book written by Paul E. McKenney from the Linux Technology Center IBM Beaverton, and covered by the terms of the Creative Commons Attribution-Share Alike 3.0 United States license.
As stated by the book’s author, this book focuses on shared-memory parallel programming, with an emphasis on software that lives near the bottom of the software stack, such as operating-system kernels, parallel data-management systems, low-level libraries, and the like.
The programming language used by this book is C.
The latest PDF versions (two columns and single column) are available here.
You can also browse its public git tree code and why not, contribute to this project by creating some patches or pull requests.