Search Results for "nvidia"
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Showing 13 open source projects for "nvidia"
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1
Megatron
Ongoing research training transformer models at scale
...Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. ... -
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Triton Inference Server
The Triton Inference Server provides an optimized cloud
...Triton enables teams to deploy any AI model from multiple deep learning and machine learning frameworks, including TensorRT, TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton supports inference across cloud, data center, edge, and embedded devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton delivers optimized performance for many query types, including real-time, batched, ensembles, and audio/video streaming. Provides Backend API that allows adding custom backends and pre/post-processing operations. Model pipelines using Ensembling or Business Logic Scripting (BLS). HTTP/REST and GRPC inference protocols based on the community-developed KServe protocol. ... -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual... -
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5
SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and... -
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ECCV2022-RIFE
Real-Time Intermediate Flow Estimation for Video Frame Interpolation
...The software supports arbitrary-timestep interpolation, enabling flexible frame generation between two input images. Optimized implementations can perform 720p frame interpolation at more than 30 frames per second on an NVIDIA RTX 2080 Ti. Users can process videos through Python scripts, pretrained models, and Google Colab demonstrations. The project supports research and practical applications involving slow-motion video, animation enhancement, and frame-rate conversion. -
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SimSiam
PyTorch implementation of SimSiam
SimSiam is a PyTorch implementation of “Exploring Simple Siamese Representation Learning” by Xinlei Chen and Kaiming He. The project introduces a minimalist approach to self-supervised learning that avoids negative pairs, momentum encoders, or large memory banks—key complexities of prior contrastive methods. SimSiam learns image representations by maximizing similarity between two augmented views of the same image through a Siamese neural network with a stop-gradient operation, preventing... -
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TensorLayer
Deep learning and reinforcement learning library for scientists
...This project can also be found at OpenI and Gitee. 3.0.0 has been pre-released, the current version supports TensorFlow, MindSpore and PaddlePaddle (partial) as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. In the future, it will support TensorFlow, MindSpore, PaddlePaddle, PyTorch and other backends. TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive examples. -
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10
PyText
A natural language modeling framework based on PyTorch
...We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
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DIGITS
Deep Learning GPU training system
...DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. DIGITS is available as a free download to the members of the NVIDIA Developer Program. DIGITS is available on NVIDIA GPU Cloud (NGC) as an optimized container for on-demand usage. Sign-up for an NGC account and get started with DIGITS in minutes. -
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
...To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively. NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in the development version). The goal of libgpuarray is (from the documentation) make a common GPU ndarray (n dimensions array) that can be reused by all projects that is as future proof as possible, while keeping it easy to use for simple need/quick test. ... -
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Neural Enhance
Super Resolution for images using deep learning
...Pretrained models support photo enhancement and JPEG artifact repair. Users can also train custom networks with perceptual and adversarial approaches for specialized datasets. The command-line tool supports batch processing, CPU or NVIDIA GPU execution, and Docker-based deployment for easier setup.
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