Overview

Introduction

Tower uses the concept of compute environments to define the execution platform where a pipeline will run. Compute environments enable Tower users to launch pipelines on a growing number of cloud and on-premise infrastructures.

Each compute environment must be configured to enable Tower to submit tasks. See the individual compute environment pages below for platform-specific configuration steps.

Platforms

• AWS Batch
• Azure Batch
• Google Cloud Batch
• Google Life Sciences
• Altair Grid Engine
• Altair PBS Pro
• IBM LSF
• Moab
• Slurm
• Kubernetes
• Amazon EKS
• Google GKE

Select a default compute environment

If you have more than one compute environment, you can select which one will be used by default when launching a pipeline.

1. In a workspace, select Compute Environments.

2. Select Make primary for a particular compute environment to make it your default.

GPU usage

The process for provisioning GPU instances in your compute environment differs for each cloud provider.

AWS Batch

The AWS Batch compute environment creation form in Tower includes an Enable GPUs option. This option makes it possible to run GPU-dependent workflows in the compute environment. Note that:

• The Enable GPUs setting alone does not cause GPU instances to deploy in your compute environment. You must still specify GPU-enabled instance types in the Advanced options > Instance types field.

• The Enable GPUs setting causes Tower Forge to specify the most current AWS-recommended GPU-optimized ECS AMI as the EC2 fleet AMI when creating the compute environment.

• This setting can be overridden by AMI ID in the advanced options.

• The NVIDIA Container Runtime uses environment variables in container images to specify a GPU accelerated container. These variables should be included in the containerOptions directive for each GPU-dependent process in your Nextflow script. For example:

process UseGPU {
    containerOptions '-e NVIDIA_DRIVER_CAPABILITIES=compute,utility -e NVIDIA_VISIBLE_DEVICES=all'
}