CVAT is completely re-designed and re-implemented version of [Video Annotation Tool from Irvine, California](http://carlvondrick.com/vatic/) tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team.
1.**On Nvidia GPU Machine:** Tensorflow annotation feature is dependent on GPU hardware. One of the easy ways to launch CVAT with the tf-annotation app is to use AWS P3 instances, which provides the NVIDIA GPU. Read more about [P3 instances here.](https://aws.amazon.com/about-aws/whats-new/2017/10/introducing-amazon-ec2-p3-instances/)
Overall setup instruction is explained in [main readme file](https://github.com/opencv/cvat/), except Installing Nvidia drivers. So we need to download the drivers and install it. For Amazon P3 instances, download the Nvidia Drivers from Nvidia website. For more check [Installing the NVIDIA Driver on Linux Instances](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-nvidia-driver.html) link.
2.**On Any other AWS Machine:** We can follow the same instruction guide mentioned in the [Readme file](https://github.com/opencv/cvat/). The additional step is to add a [security group and rule to allow incoming connections](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-network-security.html).
For any of above, don't forget to add exposed AWS public IP address to `docker-compose.override.com`.