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nvidia-docker - Build and run Docker containers leveraging NVIDIA GPUs
The full documentation and frequently asked questions are available on the repository wiki. An introduction to the NVIDIA Container Runtime is also covered in our blog post.
nvidia-docker docker cuda gpuDeepVideoAnalytics - A distributed visual search and visual data analytics platform.
Deep Video Analytics is a platform for indexing and extracting information from videos and images. With latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command. Deep Video Analytics implements a client-server architecture pattern, where clients can access state of the server via a REST API. For uploading, processing data, training models, performing queries, i.e. mutating the state clients can send DVAPQL (Deep Video Analytics Processing and Query Language) formatted as JSON. The query represents a directed acyclic graph of operations.
deep-learning nvidia-docker face-recognition face-detection image-retrieval visual-search video-analytics cbir deep-video-analyticsBMW-TensorFlow-Training-GUI - This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy
This repository allows you to get started with training a State-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it with TensorBoard. You can even test your model with our built-in Inference REST API. Training with TensorFlow has never been so easy.
docker gui deep-neural-networks computer-vision deep-learning neural-network tensorflow rest-api tensorboard resnet deeplearning object-detection nvidia-docker computervision objectdetection no-code tensorflow-training detection-api tensorflow-gui inference-apibatch-shipyard - Execute batch and HPC Dockerized workloads on Azure Batch with shared file system provisioning and linking support
Additionally, Batch Shipyard provides the ability to provision and manage entire standalone remote file systems (storage clusters) in Azure, independent of any integrated Azure Batch functionality.Batch Shipyard is now integrated directly into Azure Cloud Shell and you can execute any Batch Shipyard workload using your web browser or the Microsoft Azure Android and iOS app.
azure-batch docker hpc mpi gpu infiniband rdma azure nvidia-docker batch-processing nfs glusterfs smb azure-functionsazure-bigcompute - :star: :penguin: GPU Sku usage for Ubuntu 16
For portal Deployment, the following pic might assist.For the latest version, to contribute, and for more information, please go through this README.md.
centos-hpc oms-workspace torque pbs-pro gpu rdma cluster docker ubuntu-hpc ubuntu-nvidia centos-nvidia h-series infiniband cuda intel-mpi hpc n-series nvidia raid0 nvidia-docker azure directluda - ludicrously awesome [w]rapper for nvidia-docker
Opinionated wrapper for docker/nvidia-docker designed to provide Singularity-like functionality to Docker images. Best used for container images that run DL/HPC-like jobs, not suited for long-running daemons or services that require root.
docker nvidia-dockertfmesos - Tensorflow in Docker on Mesos #tfmesos #tensorflow #mesos
TFMesos is a lightweight framework to help running distributed Tensorflow Machine Learning tasks on Apache Mesos within Docker and Nvidia-Docker . TFMesos dynamically allocates resources from a Mesos cluster, builds a distributed training cluster for Tensorflow, and makes different training tasks mangeed and isolated in the shared Mesos cluster with the help of Docker.
tensorflow mesos nvidia-docker machine-learning distributed deep-learning deep-neural-networks ml neural-network dockercoreos-nvidia - Yet another NVIDIA driver container for Container Linux (aka CoreOS)
Yet another NVIDIA driver container for Container Linux (aka CoreOS). Executing the srcd/coreos-nvidia for your CoreOS version the nvidia modules are loaded in the kernel and the devices are created in the rootfs.
coreos coreos-container-linux nvidia nvidia-docker nvidia-driverdocker-nheqminer-cuda - CUDA capable docker image of nheqminer (zcash/equihash miner)
I have now had my docker running 4+ weeks which meets my standards for "stable". This assumes that current version of NVIDIA drivers and Docker is installed, it also requires the nvidia-docker plugin which allows the image to access the host GPU and drivers with minimal extra requirements on you or the host.
nheqminer zcash cuda nvidia nvidia-docker nicehash blockchain docker-image docker gpuPaintsChainer-Docker - Docker container for PaintsChainer
If you want to run this on Windows and have no familiarity with Docker, etc you can find Complete setup instructions for Windows (including Docker) on the wiki.
docker docker-container nvidia-docker paintschainer docker-imageDistributedDeepLearning - Tutorials on running distributed deep learning on Batch AI
This repo is a tutorial on how to train a CNN model in a distributed fashion using Batch AI. The scenario covered is image classification, but the solution can be generalized for other deep learning scenarios such as segmentation and object detection. Image classification is a common task in computer vision applications and is often tackled by training a convolutional neural network (CNN). For particularly large models with large datasets, the training process can take weeks or months on a single GPU. In some situations, the models are so large that it isn’t possible to fit reasonable batch sizes onto the GPU. Using distributed training in these situations helps shorten the training time. In this specific scenario, a ResNet50 CNN model is trained using Horovod on the ImageNet dataset as well as on synthetic data. The tutorial demonstrates how to accomplish this using three of the most popular deep learning frameworks: TensorFlow, Keras, and PyTorch. There are number of ways to train a deep learning model in a distributed fashion, including data parallel and model parallel approaches based on synchronous and asynchronous updates. Currently the most common scenario is data parallel with synchronous updates—it’s the easiest to implement and sufficient for the majority of use cases. In data parallel distributed training with synchronous updates the model is replicated across N hardware devices and a mini-batch of training samples is divided into N micro-batches (see Figure 2). Each device performs the forward and backward pass for a micro-batch and when it finishes the process it shares the updates with the other devices. These are then used to calculate the updated weights of the entire mini-batch and then the weights are synchronized across the models. This is the scenario that is covered in the GitHub repository. The same architecture though can be used for model parallel and asynchronous updates.
deep-learning convolutional-neural-networks distributed-training nvidia nvidia-docker azure batch-aized-docker - Docker images for the ZED SDK
Since we need CUDA, nvidia-docker must be used (except for compilation only). --privileged option is used to pass through all the device to the docker container, it might not be very safe but provides an easy solution to connect the USB3 camera to the container.
nvidia-docker docker zed-camera cudatensorflow-gpu-docker - tensorflow gpu docker file to build docker image
since the image can not build with tensorflow git ,so i build it .
docker dockerfile machine-learning tensorflow jupyter-notebook nvidia-docker
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