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Source channel @githubtrending · Post #14789 · Jun 5

#go#golang#mesh#mesh_networks#nat_traversal#netbird#vpn#wireguard#wireguard_vpn#wiretrustee#zero_trust_network_access NetBird lets you quickly set up a secure private network for your organization or home without needing to configure complex firewall rules, open ports, or manage VPN gateways. It uses direct peer-to-peer connections based on WireGuard, so your devices connect to each other securely and efficiently, bypassing the need for a central server. You get strong security with features like access control, activity logging, and support for single sign-on, all managed from a simple web interface. This means you can connect and protect your devices anywhere, with minimal setup and maintenance, making remote access and network management much easier[2][1][5]. https://github.com/netbirdio/netbird

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djangoproject

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

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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

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