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Source channel @githubtrending · Post #14641 · Apr 27

#go#cloud#devsecops#k8s#kubernetes#mesh#mesh_network#network#networking#overlay_network#security#self_hosted#virtual_network#virtual_networking#vpn#vpn_server#wg_quick#wireguard#wireguard_ui#wireguard_vpn#zero_trust Netmaker is a powerful tool for creating and managing secure networks. It uses WireGuard to provide fast and secure connections, allowing you to connect devices anywhere in the world. With features like mesh VPNs and multi-network segmentation, you can organize your networks securely and efficiently. Netmaker also offers robust access controls and integration with OAuth for secure user management. This helps keep your network safe and compliant, making it ideal for businesses managing complex network setups. https://github.com/gravitl/netmaker

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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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