DELTA is a deep learning based natural language and speech processing platform.
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Updated
Jun 3, 2020 - Python
DELTA is a deep learning based natural language and speech processing platform.
Gathers scalable tensorflow and infrastructure deployment
tensorflow prediction using c++ api
export bert model for serving
Helmut Hoffer von Ankershoffen 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.
Open Toolkit for Painless Object Detection
基于tensorflow & tf-servering & flask 的图像检索
Deploying Keras models using TensorFlow Serving and Flask
Kafka Streams + Java + gRPC + TensorFlow Serving => Stream Processing combined with RPC / Request-Response
Examples to server tensorflow models with tensorflow serving
A Tutorial for Serving Tensorflow Models using Kubernetes
YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
Real-time analysis of bitcoin markets with Kafka and Tensorflow Serving
Code and presentation for Strata Model Serving tutorial
Building an image classifier in TF2
A Scala binding of TensorFlow for Serving TensorFlow Models (Provides RESTful API)
Bitnami Docker Image for TensorFlow Serving
Example of using TensorFlow Serving with OpenFaaS
Train, predict, export and reload a tf.estimator for inference
Serve machine learning models using tensorflow serving
Implement some hair segmentation network
bert sentiment analysis tensorflow serving with RESTful API
Create a visual search engine using tensorflow serving, elasticsearch, vuejs and nginx.
Serve TF models simple and easy as an HTTP API
Implement Tensor Flow Serving C# client example with gRPC. MNIST prediction console application and web paint ASP.NET Core 2.0 and ReactJS application.
NSFW classify model implemented with tensorflow.
End to End Machine Learning with Google Cloud Platform
JuPyter Notebooks and Python Package for Deep Learning Model Exploration, Translation and Deployment
**UNOFFICIAL and redistributed** TensorFlow Serving API libraries for Python3. See DEPRECATION WARNING in README.
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When I run this line as part of the KubeFlow chapter:
ks param set kubeflow-core cloud aksI end up with the following error:
ERROR could not find component: open C:\users\xxx\my-kubeflow\components\C:\users\xxx\my-kubeflow\components:
The filename, directory name, or volume label syntax is incorrect.
For some reason its concatenating the directory twice. If i manually edit the libs