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Source channel @devilsbelow · Post #629 · Mar 9

🌐Weekly News Digest [ March 2 – March 8 ] That was a week when a wider Gulf war sent oil prices jumping, while Central Africa’s mineral struggle spilled into sanctions and landslides. 💡...And here are more key highlights: 🇨🇩 DR Congo — A landslide at the Rubaya mine leaves hundreds of people dead — A third Rubaya-area landslide in 38 days hits Gakombe after heavy rains 🇬🇭 Ghana — Ghana faces joint US-China pressure to drop a 5%-12% gold royalty scale 🇳🇪 Niger — Niger cancels three gold-refinery agreements after repeated alleged contract breaches 🇳🇬 Nigeria — Nigeria signs a $1.3bn alumina refinery pact with AFC — Ogoniland groups say oil restart must wait for cleanup and justice 🇷🇼 Rwanda — The US sanctions Rwanda’s army and four officers over support for M23 🇿🇦 South Africa — South Africa expands illegal-mining raids and lines up military support 🇿🇲 Zambia — Kasumbalesa bridge collapse cuts Congo’s main copper export route to southern ports 🇿🇼 Zimbabwe — A Zimbabwe court jails a Chinese mine supervisor five years over a gold panner’s killing 🌍 Global — Oil prices jump 12% as Gulf escalation triggers fuel-supply fears #NewsDigest ✈️ Stay informed - @devilsbelow

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djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,