caffeThis fork of BVLC/Caffe is dedicated to supporting Cambricon deep learning processor and improving performance of this deep learning framework when running on Machine Learning Unit(MLU).
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Mutual labels: inference
onnxruntime backendThe Triton backend for the ONNX Runtime.
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Mutual labels: inference
surepy🐾 Library & CLI to monitor and control the Pet Door & Cat Flap Connect 🚪 the Pet Feeder Connect 🍽 and the Felaqua 💦 sold by Sure Petcare
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Mutual labels: monitor
kzmonitorkafka zookeeper monitor
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Mutual labels: monitor
mc-monitorMonitor the status of Minecraft servers and provides Prometheus exporter and Influx line protocol output
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Mutual labels: monitor
serving-runtimeExposes a serialized machine learning model through a HTTP API.
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Mutual labels: inference
healthchecksioUpdate and display the status of your healthchecks.io checks.
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Mutual labels: monitor
AnyStatusA remote control for your CI/CD pipelines and more
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Mutual labels: monitor
PFL-Non-IIDThe origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage…
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Mutual labels: federated-learning
WindowsMonitorWMI namespaces and classes
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Mutual labels: monitor
drmonDraconic Reactor computercraft monitoring and failsafe interface
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Mutual labels: monitor
FedLab-benchmarksStandard federated learning implementations in FedLab and FL benchmarks.
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Mutual labels: federated-learning
monitorcontrolPython monitor controls using DDC-CI.
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Mutual labels: monitor
backdoors101Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.
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Mutual labels: federated-learning
JOCIOrdinal Common-sense Inference
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Mutual labels: inference
KD3AHere is the official implementation of the model KD3A in paper "KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation".
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Mutual labels: federated-learning
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Mutual labels: monitor
k8s-mon滴滴夜莺Kubernetes monitor
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Mutual labels: monitor
easyFLAn experimental platform to quickly realize and compare with popular centralized federated learning algorithms. A realization of federated learning algorithm on fairness (FedFV, Federated Learning with Fair Averaging, https://fanxlxmu.github.io/publication/ijcai2021/) was accepted by IJCAI-21 (https://www.ijcai.org/proceedings/2021/223).
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Mutual labels: federated-learning
