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Source channel @devilsbelow · Post #445 · Jan 13

🌐Weekly News Digest [ January 5 – January 11 ] That was the first full-fledged week of the new year of 2026, during which rulers forgave those who polluted their land, dismissed those who were managing their oil. 💡Here are the key highlights: 🇧🇼 Botswana — Botswana Invites Russia to Invest in Its Mining Sector 🇨🇩 DR Congo — Congolese clergy speaks against the US-DRC agreement. — The government allows processing units to accept ore from artisanal miners amid protests 🇬🇶 Equatorial Guinea — Equatorial Guinea moves its capital to a brand new city built on oil revenues 🇬🇭 Ghana — A Ghanaian prophet predicts the discovery of major onshore oil deposits in Ghana — Ghana hopes to keep its oil fields viable until 2040 🇲🇱 Mali — JNIM militants attack a gold mine in southeastern Mali 🇳🇪 Niger — Niger replaces its oil minister 🇳🇬 Nigeria — President reshuffles the country's oil sector management 🇸🇩 Sudan — Sudan’s central bank and Sudanese Mineral Resources Company set up a joint commission to curb illegal gold exports. 🇺🇬 Uganda — Uganda to start its first oil exports by October, despite environmental concerns 🇿🇲 Zambia — First report on the toxic pollution caused by a Chinese company designates 160 people as victims — Zambia is concerned over the safety of its workers in southern DRC #NewsDigest ➡️ Follow to stay informed - @devilsbelow

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning