Search Results for "nvidia"
Sort By:
Showing 3 open source projects for "nvidia"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
-
1
DALI
A GPU-accelerated library containing highly optimized building blocks
The NVIDIA Data Loading Library (DALI) is a library for data loading and pre-processing to accelerate deep learning applications. It provides a collection of highly optimized building blocks for loading and processing image, video and audio data. It can be used as a portable drop-in replacement for built-in data loaders and data iterators in popular deep learning frameworks. -
2
Totally Built Linux Distro
build linux from termcap to GTK-4.7 100% non-stop
Build all of a light linux distro non-stop and no buildfails with just a few commands. (not including un-tarring initial DL) Current Build Time: 3 to 4.7 hours* on an iCore7 using -j8 It's all automatic from Termcap to Gtk-4 and making new USB, it's new enough run firefox 20-130 and Mathematica 4 - 14. Minimal commandlines and configuration required; just a few in itself. Cross Compiles i386 to x86_64 (note not a canadian cross). ~500 pkgs built including configured database... -
3
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.
- Previous
- You're on page 1
- Next
