delta - DELTA is a deep learning based natural language and speech processing platform.

•    Python

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DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.

nlp deep-learning tensorflow speech sequence-to-sequence seq2seq speech-recognition text-classification speaker-verification nlu text-generation emotion-recognition tensorflow-serving tensorflow-lite inference asr serving front-end

tensorflow-java-client - Example of Java/Scala grpc client for tensorflow_serving (https://github

•    Java

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I had many dependency problems, that is why I had to build the grpc-java code and use the libs created during the build (the grpc-java version available in mavencentral seems to be outdated). Then you have to compile the tensorflow_serving .proto files inside serving/tensorflow_serving/apis and serving/tensorflow/tensorflow/core/framework.

tensorflow-serving protobuf tensorflow-java-client scala-grpc-client

kubeflow-labs - 👩‍🔬 Train and Serve TensorFlow Models at Scale with Kubernetes and Kubeflow on Azure

•    Python

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This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com. When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

kubernetes kubeflow machine-learning tensorflow tensorflow-serving distributed-tensorflow docker jupyter-notebook jupyterhub

datacollector-edge - StreamSets Data Collector Edge (SDC Edge)

•    Go

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The StreamSets Data Collector Edge (SDC Edge) enables at-scale data ingestion and analytics for edge systems. An ultralight, small-footprint agent, it is an ideal solution for use cases like Internet of Things (IoT) or cybersecurity applications that collect data from resource-constrained sensors and personal devices. StreamSets Data Collector Edge is built on open source technologies, our code is licensed with the Apache License 2.0.

iot cybersecurity edge-computing fog-computing mqtt coap kafka tensorflow tensorflow-serving

tensorflow-serving-openfaas - Example of using TensorFlow Serving with OpenFaaS

•    Dockerfile

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Example of packaging TensorFlow Serving with OpenFaaS to be deployed and managed through OpenFaaS with auto-scaling, scale-from-zero and a sane configuration for Kubernetes. You need to edit the stack.yml file and replace alexellis2 with your Docker Hub account.

tensorflow machine-learning ai tf tensorflow-serving docker openfaas serverless function

tf-bridge - Tensor Bridge: OpenAPI spec and REST wrapper around TensorFlow Serving

•    Python

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Tensor Bridge is an OpenAPI Specification as well as a simple Connexion wrapper for TensorFlow Serving. The specification was obtained by compiling an annotated tensor_bridge.proto using grpc-gateway. The result is located in swagger/tensor_bridge.json.

tensorflow-serving tensorflow tensorflow-models machine-learning machine-learning-api

tensorflow-object-detection - 📦 Operationalizing TensorFlow Object Detection on Azure

•    Python

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📦 Operationalizing TensorFlow Object Detection on Azure

tensorflow azure tensorflow-object-detection-api machine-learning tensorflow-serving docker

jetson - Experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT

•    Python

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Experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT. Total ca. $210 including options.

ansible k8s ml kubernetes cuda skaffold kustomize jupyter machine-learning smart-iot docker edge-devices vagrant tensorflow-serving nvidia-jetson-xavier nvidia-jetson-nano archiconda virtualbox tensorrt kubeflow