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Rubén Llorente ... _virtual_environment_file.network_cloud_config, 18 ] 19 20 initialization { 21 user_data_file_id = proxmox_virtual_environment_file.rproxy_cloud_config.id 22 network_data_file_id = proxmox_virtual_environment_file.network_cloud_config[0

The Valkey project has released Valkey 8.0.0, marking the first major release since the project became generally available earlier this year. “The project continues the traditions of the seven ... Valkey 8.0.0 Released

IP Fabric has announced the 7.0 release of its Automated Network Assurance Platform, which aims to provide the necessary insight and control to manage complex cloud, security, and network ... IP Fabric 7.0 Released

The OpenMP Architecture Review Board (ARB) has released version 6.0 of the OpenMP API Specification. According to the announcement, this major upgrade makes it easier to develop parallel programs ... OpenMP 6.0 Released

) or Link Layer Discovery Protocol (LLDP). Disable Internet Protocol (IP) source routing. Disable Secure Shell (SSH) version 1. Ensure only SSH v2.0 is used with the following cryptographic ... In the news: Hetzner Announces S3-Compatible Object Storage; Ongoing Cyberattack Prompts New CISA Guidance for Communications Infrastructure; OpenMP 6.0 Released; Open Source Development Improves

={"city": "New York"}), PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"city": "Beijing"}), PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44], payload={"city": "Mumbai"}),], ) The database

. Canonical Kubernetes 1.32 LTS is currently available and ready for production use. Read more at Canonical: https://canonical.com/blog/12-year-lts-for-kubernetes. Mirantis Releases Open Source k0rdent ... In the news: Palo Alto Networks Introduces Cortex Cloud; Canonical to Provide 12 Years of Kubernetes Support; Mirantis Releases Open Source k0rdent; D-Wave Now Offers On-Premises Quantum Computing

virtual instance for development and test purposes on which to carry out the work. Again, Ubuntu 22.04 is a good choice. The steps are quickly completed: Use curl to download the k0s binary, which you

(pool_size=(2,2))) model.add(layers.Dropout(0.3)) The next size layers of the model (Listing 4) are the same except for some small changes: input_shape  does not need to be specified in the first 2D

.add(layers.BatchNormalization()) model.add(layers.Conv2D(32, (3,3), padding='same', activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2,2))) model.add(layers.Dropout(0.3)) The next