nskool Goto Github PK

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

amazon-sagemaker-operator-for-k8s

Amazon SageMaker operator for Kubernetes

backend

Common source, scripts and utilities for creating Triton backends.

checksum_repository_agent

The Triton repository agent that verifies model checksums.

client

Triton Python, C++ and Java client libraries, and GRPC-generated client examples for go, java and scala.

common

Common source, scripts and utilities shared across all Triton repositories.

core

The core library and APIs implementing the Triton Inference Server.

dali_backend

The Triton backend that allows running GPU-accelerated data pre-processing pipelines implemented in DALI's python API.

deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

deeplearning-cfn

Distributed Deep Learning on AWS Using CloudFormation (CFN), MXNet and TensorFlow

djl

An Engine-Agnostic Deep Learning Framework in Java

djl-serving

A universal scalable machine learning model deployment solution

fastertransformer_backend

fil_backend

FIL backend for the Triton Inference Server

identity_backend

Example Triton backend that demonstrates most of the Triton Backend API.

incubator-mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

l3cube-chanakya

Program for L3Cube BE Project Sponsorship Programming Round

metadata_analyser

Analyses Metadata of media files present in a given folder.

multi-model-server

Multi Model Server is a tool for serving neural net models for inference

neo-ai-dlr

Neo-AI-DLR is a common runtime for machine learning models compiled by AWS SageMaker Neo, TVM, or TreeLite.

nskool.github.io

Things I have fun with !

onnxruntime_backend

The Triton backend for the ONNX Runtime.

python_backend

Triton backend that enables pre-process, post-processing and other logic to be implemented in Python.

pytorch_backend

The Triton backend for the PyTorch TorchScript models.

rapids-triton

repeat_backend

An example Triton backend that demonstrates sending zero, one, or multiple responses for each request.

sagemaker-benchmarks

Benchmark scripts on SageMaker for various frameworks

sagemaker-huggingface-inference-toolkit

sagemaker-inference-toolkit

Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

sagemaker-mxnet-container

This support code is used for making the MXNet framework run on Amazon SageMaker.