Programming/Development: Java, GitLab, C++ and Python
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Hazelcast joins Eclipse Foundation to collaborate on open source enterprise Java
Hazelcast, the open source In-Memory Data Grid (IMDG) with tens of thousands of installed clusters and over 39 million server starts per month, announced it had joined the Eclipse Foundation, bringing extensive Java-driven community experience to a host of open source projects.
Working collaboratively with other members of the Eclipse community, Hazelcast’s primary focus will be on JCache, the Eclipse MicroProfile and EE4J.
In particular, Hazelcast will be collaborating with members to popularise JCache, a Java Specification Request (JSR-107) which specifies API and semantics for temporary, in-memory caching of Java objects, including object creation, shared access, spooling, invalidation, and consistency across JVM’s. These operations help scale out applications and manage their high-speed access to frequently used data. In the Java Community Process (JCP), Hazelcast’s CEO, Greg Luck, has been the co spec lead and then maintenance lead on “JCache – Java Temporary Caching API” since 2007.
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GitLab update: Moving to the next step
I have good news, after few meetings and discussions with GitLab we reached an agreement on a way to bring the features we need and to fix our most important blockers in a reasonable time and in a way that are synced with us. Their team will fix our blockers in the next 1-2 months, most of them will be fix in the release of 22th of December and the rest if everything goes well in the release of 22th of January. The one left that out of those 2 months is a richer UI experience for duplicates, which is going to be an ongoing effort.
Apologies for the blockage for those that regularly asked to migrate their project, I wanted to make sure we are doing things in the right steps. I also wanted to make sure that I get feedback and comments about the initiative all around in my effort to make a representation of the community for taking these decisions. Now it’s the point where I’m confident, the feedback and comments both inside and outside of our core community has been largely that we should start our path to fully migrate to GitLab.
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Khronos Releases SYCL 1.2.1 With TensorFlow Acceleration, C++17 Alignment
SYCL as a reminder is Khronos' higher-level OpenCL programming model based on C++. It's been a while since the last update, but a new point release is now available.
SYCL 1.2.1 is based on OpenCL 1.2 and improves support for machine learning tasks, supports TensorFlow acceleration, and aligns with the latest C++17 standard. SYCL 1.2 had previously been based on C++11/C++14. The C++17 standard was just firmed up this month.
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Python data classes
The reminder that the feature freeze for Python 3.7 is coming up fairly soon (January 29) was met with a flurry of activity on the python-dev mailing list. Numerous Python enhancement proposals (PEPs) were updated or newly proposed; other features or changes have been discussed as well. One of the updated PEPs is proposing a new type of class, a "data class", to be added to the standard library. Data classes would serve much the same purpose as structures or records in other languages and would use the relatively new type annotations feature to support static type checking of the use of the classes.
PEP 557 ("Data Classes") came out of a discussion on the python-ideas mailing list back in May, but its roots go back much further than that. The attrs module, which is aimed at reducing the boilerplate code needed for Python classes, is a major influence on the design of data classes, though it goes much further than the PEP. attrs is not part of the standard library, but is available from the Python Package Index (PyPI); it has been around for a few years and is quite popular with many Python developers. The idea behind both attrs and data classes is to automatically generate many of the "dunder" methods (e.g. __init__(), __repr__()) needed, especially for a class that is largely meant to hold various typed data items.
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