Relevant files for the docker containing Python and R installation for GPU Deep Learning tests
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Updated
Feb 27, 2021 - Dockerfile
Relevant files for the docker containing Python and R installation for GPU Deep Learning tests
Setup a Google cloud deep learning server with gpu-ready docker container to run Tensorflow from local browser using Jupyter
Dockerfiles for setting up GPUs with NVIDIA CUDA and other machine intelligence packages
Dockerfiles for deep learning dev
Ansible Playbook Demo for Nvidia GPU supported Docker from www.datadrivers.de
Containerized development workflow for the NorLab_MPPI and SNOW_AutoRally projects leveraging nvidia-docker technology. Believe it or not, it's configured for developing with ROS melodic in python 3.6.
Docker image for running Udacity Self-driving code
GPU-Enabled Development Environment using Docker
Dockerfile for building NVIDIA CUDA image for PyTorch 1.0 and fastai 1.0 deep learning
Ubuntu 18.04 + ROS Melodic + Conda in a Docker container with Nvidia acceleration
Training, inference, image vizualisation, database connector scripts
An all-in-one mirror for installing NVIDIA Docker.
Sample YAML file for setting up the NVIDIA driver and NVIDIA device plugin for better scheduling
for nvidia graphic metrics on datadog
Detect, track and count different classes of objects.
🖧 Simple gRPC client in Go to communicate with TensorRT Inference Server
Jetson Nano Home Automator with Homebridge
Add a description, image, and links to the nvidia-docker topic page so that developers can more easily learn about it.
To associate your repository with the nvidia-docker topic, visit your repo's landing page and select "manage topics."