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Search results for deep learning model serving
deep-learning
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model-serving
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16 search results found
Bentoml
⭐
6,488
The most flexible way to serve AI models in production
Deep Learning In Production
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4,138
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Fedml
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3,946
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://nexus.fedml.ai) is the dedicated cloud service for generative AI
Lightllm
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1,417
LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
Mosec
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661
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
Model_server
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618
A scalable inference server for models optimized with OpenVINO™
Pinferencia
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473
Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
Chitra
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219
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Clearml Serving
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115
ClearML - Model-Serving Orchestration and Repository Solution
Fastdeploy
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90
Deploy DL/ ML inference pipelines with minimal extra code.
Monai Deploy App Sdk
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74
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
Hexgen
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30
Serving LLMs on heterogeneous decentralized clusters.
Console
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20
⛅ Versatile Data Pipeline (VDP) console website
Model_deployment
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12
A collection of model deployment library and technique.
Ventu
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6
Serving the deep learning models easily.
Openvino Model Server Wrapper
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5
Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code.
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