Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.
deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8sTensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.
tensorflow node-tensorflow nodejs machine-learning deep-learning npm-package tf tensor ml ai neural-networks neuralnetworks deeplearning model numerical-computation googleAoE (AI on Edge,终端智能,边缘计算) 是一个终端侧AI集成运行时环境 (IRE),帮助开发者提升效率。
machine-learning ai aoe android ios tensorflow deep-learning mnist squeezenet edge mnn ncnn mace demo didi ml tools benchmark ire deviceAn existing Kubernetes cluster (e.g., Minikube for local testing). Once installed, use the command make minikube to start Minikube and set up local network routes. Alternatively, use the Vagrant based setup to automatically install a local Kubernetes cluster.
ai ml deep-learning kubernetes-cluster tensorflow caffe pytorch keras
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