diff --git a/README.md b/README.md index f81bf75..0f7b9f5 100644 --- a/README.md +++ b/README.md @@ -850,8 +850,8 @@ EC2 - **GPU support:** You can rent GPU-enabled instances on EC2 for use in machine learning or graphics rendering workloads. - There are [three types](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using_cluster_computing.html) of GPU-enabled instances currently available: - - The P3 series offers NVIDIA Tesla V100 GPUs in 1, 4 and 8 GPU configurations targeting machine learning, scientific workloads, and other high performance computign applications. - - The P2 series offers NVIDIA Tesla K80 GPUs in 1, 8 and 16 GPU configurations targeting machine learning, scientific workloads, and other high performance computign applications. + - The P3 series offers NVIDIA Tesla V100 GPUs in 1, 4 and 8 GPU configurations targeting machine learning, scientific workloads, and other high performance computing applications. + - The P2 series offers NVIDIA Tesla K80 GPUs in 1, 8 and 16 GPU configurations targeting machine learning, scientific workloads, and other high performance computing applications. - The G3 series offers NVIDIA Tesla M60 GPUs in 1, 2, or 4 GPU configurations targeting graphics and video encoding. - AWS offers two different AMIs that are targeted to GPU applications. In particular, they target deep learning workloads, but also provide access to more stripped-down driver-only base images. - AWS offers both an Amazon Linux [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B077GF11NF?qid=1536363169916&sr=0-3&ref_=srh_res_product_title) (based on Amazon Linux) as well as an Ubuntu [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B077GCH38C). Both come with most NVIDIA drivers and ancillary software (CUDA, CUBLAS, CuDNN, TensorFlow, PyTorch, etc.) installed to lower the barrier to usage.