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Code Issues Releases Wiki Activity

Update outdated GPU section. (#629)

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Luke de Oliveira 2018-09-07 16:53:17 -07:00 • committed by Corey Quinn
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@ -836,11 +836,14 @@ EC2
- Avoid sharing keys and [add individual ssh keys](http://security.stackexchange.com/questions/87480/managing-multiple-ssh-private-keys-for-a-team) for individual users.
- **GPU support:** You can rent GPU-enabled instances on EC2 for use in machine learning or graphics rendering workloads.
- There are [three generations](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using_cluster_computing.html) of GPU-enabled instances available:
- Third generation P2 series offers NVIDIA K80 GPUs in 1, 8 and 16 GPU configurations targeting machine learning and scientific workloads.
- Second generation G2 series offers NVIDIA K520 GPUs in 1 or 4 GPU configurations targeting graphics and video encoding.
- First generation CG1 instances are still available in some regions in a single configuration with a NVIDIA M2050 GPU.
- ⛓ AWS offers an [AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref_=srh_res_product_title) (based on Amazon Linux) with most NVIDIA drivers and ancillary software (CUDA, CUBLAS, CuDNN, TensorFlow) installed to lower the barrier to usage. Note, however, that this leads to lock-in due to Amazon Linux and the fact that you have no direct access to software configuration or versioning.
- 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 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.
- ⛓ Note that using these AMIs can lead to lock in due to the fact that you have no direct access to software configuration or versioning.
- 🔸 The compendium of frameworks included can lead to long instance startup times and difficult-to-reason-about environments.
- 🔹As with any expensive EC2 instance types, [Spot instances can offer significant savings](#ec2-cost-management) with GPU workloads when interruptions are tolerable.
- All current EC2 instance types can take advantage of IPv6 addressing, so long as they are launched in a subnet with an allocated CIDR range in an IPv6-enabled VPC.