8 Alternatives to TensorFlow Serving

TensorFlow Serving is an easy-to-deploy, versatile and excessive performing serving system for machine learning fashions constructed for manufacturing environments. It permits straightforward deployment of algorithms and experiments whereas permitting builders to maintain the identical server structure and APIs. TensorFlow Serving gives seamless integration with TensorFlow fashions, and may also be simply prolonged to different fashions and information. 

Below, we checklist just a few alternate options to TensorFlow Serving: 

Cortex 

Open-source platform Cortex makes execution of real-time inference at scale seamless. It is designed to deploy educated machine studying fashions straight as an internet service in manufacturing. 

The set up and deployment configurations for Cortex are straightforward and versatile. It comes with an in-built help mechanism to implement educated machine studying fashions. It may be deployed in all Python-based machine studying frameworks, together with TensorFlow, PyTorch, and Keras. Cortex provides the next options: 

  • Automatically scales prediction APIs to assist handle the ups and downs of manufacturing workloads.
  • Its net infrastructure companies can run inferences seamlessly on CPU and GPU.
  • Cortex can simply handle cluster, uptime and reliability of the APIs.
  • Helps within the transition of the up to date mannequin to the deployed APIs within the net service with out downtime.

For extra data, click on here.

TorchServe

PyTorch has develop into the popular ML mannequin coaching framework for information scientists within the final couple of years. TorchServe (the results of a collaboration between AWS and Facebook) is a PyTorch mannequin serving library that permits straightforward deployment of PyTorch fashions at scale with out writing a customized code.TorchServe is on the market as part of the PyTorch open supply library. 

Besides offering a low latency prediction API, TorchServe comes with the next options: 

  • Embeds default handlers for typical purposes equivalent to object detection and textual content classification. 
  • Supports multi-model serving, logging, mannequin versioning for A/B testing, and monitoring metrics.
  • Supports the creation of RESTful endpoints for utility integration.
  • Cloud and atmosphere agnostic and helps machine studying environments equivalent to Amazon SageMaker, container companies, and Amazon Elastic Compute Cloud. 

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Triton Inference Server

NVIDIA Triton Inference Server simplifies the deployment of AI fashions at scale in manufacturing. The open-source serving software program permits the deployment of educated AI fashions from any framework, equivalent to TensorFlow, NVIDIA, PyTorch or ONNX, from native storage or cloud platform. It helps an HTTP/REST and GRPC protocol, permitting distant shoppers to request interfacing for any mannequin managed by the server. 

It provides the next options: 

  • Supports a number of deep learning frameworks. 
  • Runs fashions concurrently to allow high-performance inference, serving to builders deliver fashions to manufacturing quickly. 
  • Implements a number of scheduling and batching algorithms, combining particular person inference requests. 
  • Provides a backend API to increase with any mannequin execution logic applied in Python or C++. 

For extra data, click on here

KFServing 

Part of Kubeflow venture, KFServing focuses on fixing the challenges of mannequin deployment to manufacturing by means of a model-as-data method by offering an API for inference requests. It makes use of cloud-native applied sciences Knative and Istio. KFServing requires a minimal of Kubernetes 1.16+. 

KFServing provides the next options: 

  • Provides a customisable InferenceService so as to add useful resource requests for CPU, GPU, TPU and reminiscence requests. 
  • Supports multi-model serving, revision administration and batching particular person mannequin inference requests. 
  • Compatible with varied frameworks, together with Tensorflow, PyTorch, XGBoost, ScikitLearn and ONNX. 

For extra data, click on here

ForestFlow

Cloud-native machine studying mannequin server ForestFlow, used for simple deployment and administration, is scalable and policy-based. It can both be run natively or as docker containers. Built to cut back the friction between information science, engineering and operation groups, it gives information scientists with the flexibleness to make use of instruments they need. 

It provides the next options: 

  • Can be both run as a single occasion or deployed as a cluster of nodes.
  • Offers Kubernetes integration for the simple deployment of Kubernetes clusters. 
  • Allows mannequin deployment in Shadow Mode.
  • Automatically scales down fashions when not in use, and mechanically scales them up when required, whereas sustaining cost-efficient reminiscence and useful resource administration. 
  • Allows deployment of fashions for a number of use-cases. 

For extra data, click on here

Multi Model Server

Multi Model Server is an open-source instrument for serving deep studying and neural web fashions for inference, exported from MXNet or ONNX. The easy-to-use and versatile instrument utilises REST-based APIs to deal with state prediction requests. Multi Model Server makes use of java Eight or a later model to serve HTTP requests. 

See Also


It provides the next options: 

  • Ability to develop customized inference companies. 
  • Multi Model Server benchmarking.
  • Multi-model endpoints to host a number of fashions inside a single container.
  • Pluggable backend that helps pluggable customized backend handler.

For extra data, click on here

DeepDetect 

Machine studying API DeepDetect is written in C++11 and integrates into present purposes. DeepDetect implements help for supervised and unsupervised deep studying of photos, textual content, and time collection. It additionally helps classification, object detection, segmentation and regression. 

It provides the next options: 

  • DeepDetect comes with straightforward setup options and is prepared for manufacturing. 
  • Allows the constructing and testing of datasets from Jupyter notebooks. 
  • Comes with greater than 50 pre-trained fashions for fast switch coaching convergence. 
  • Allows export of fashions for the cloud, desktop and embedded units. 

For extra data, click on here

BentoML 

BentoML is a high-performance framework that bridges the hole between Data Science and DevOps. It comes with multi-framework help and works with TensorFlow, PyTorch, Scikit-Learn, XGBoost, H2O.ai, Core ML, Keras, and FastAI. It is constructed to work with DevOps and Infrastructure instruments, together with Amazon SageMaker, NVIDIA, Heroku, REST API, Kubeflow, Kubernetes and Amazon Lamdba. 

The key options of BentoML are: 

  • Comes in a unified mannequin packaging format, enabling each on-line and offline serving on all platforms. 
  • Can package deal fashions educated with any ML frameworks and reproduce them for mannequin serving in manufacturing. 
  • Works as a central hub for managing fashions and deployment processes by means of Web UI and APIs. 

For extra data, click on here


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