官术网_书友最值得收藏!

How to do it...

  1. Make sure the region you want to work in gives access to P2 or G3 instances. These instances include NVIDIA K80 GPUs and NVIDIA Tesla M60 GPUs, respectively. The K80 GPU is faster and has more GPU memory than the M60 GPU: 12 GB versus 8 GB. 
While the NVIDIA K80 and M60 GPUs are powerful GPUs for running deep learning models, these should not be considered state-of-the-art. Other faster GPUs have already been launched by NVIDIA and it takes some time before these are added to cloud solutions. However, a big advantage of these cloud machines is that it is straightforward to scale the number of GPUs attached to a machine; for example, Amazon's p2.16xlarge instance has 16 GPUs.
  1. There are two options when launching an AWS instance. Option 1: You build everything from scratch. Option 2: You use a preconfigured Amazon Machine Image (AMI) from the AWS marketplace. If you choose option 2, you will have to pay additional costs. For an example, see this AMI at https://aws.amazon.com/marketplace/pp/B06VSPXKDX.
  2. Amazon provides a detailed and up-to-date overview of steps to launch the deep learning AMI at https://aws.amazon.com/blogs/ai/get-started-with-deep-learning-using-the-aws-deep-learning-ami/.
  3. If you want to build the server from scratch, launch a P2 or G3 instance and follow the steps under the Installing CUDA and cuDNN and Installing Anaconda and Libraries recipes.
  4. Always make sure you stop the running instances when you're done to prevent unnecessary costs. 
A good option to save costs is to use AWS Spot instances. This allows you to bid on spare Amazon EC2 computing capacity.
主站蜘蛛池模板: 泰和县| 昭通市| 津南区| 浑源县| 嘉义县| 十堰市| 乌审旗| 成都市| 辉南县| 南投县| 石台县| 杭锦后旗| 博野县| 赤水市| 吉木乃县| 铜川市| 榆树市| 莱阳市| 佛山市| 白河县| 安远县| 囊谦县| 无极县| 河间市| 婺源县| 双流县| 资溪县| 博爱县| 乳山市| 克什克腾旗| 海盐县| 邹城市| 汉沽区| 西和县| 富源县| 普宁市| 郯城县| 佳木斯市| 南通市| 双江| 安龙县|