Cloud

bcbio has two approaches to running on cloud providers like Amazon Web Services (AWS), Google Cloud (GCP) and Microsoft Azure. For smaller projects we use a simplified ansible based approach which automates spinning up single multicore machines for running either traditional or Common Workflow Language (CWL) bcbio runs.

For larger distributed projects, we’re actively working on using Common Workflow Language (CWL) support with runners like Cromwell that directly interface and run on cloud services. We’ll document these approaches here as they’re tested and available.

For getting started, the CWL Installation documentation describes how to install bcbio-vm, which provides a wrapper around bcbio that automates interaction with cloud providers and Docker. bcbio_vm.py also cleans up the command line usage to make it more intuitive and provides a superset of functionality available in bcbio_nextgen.py.

Google Cloud (GCP)

Cromwell runs bcbio CWL pipelines on Google Cloud using the Google Pipelines API.

Setup

To setup a Google Compute environment, you’ll make use of the Web based console and gcloud and gsutil from the Google Cloud SDK, which provide command line interfacts to manage data in Google Storage and Google Compute instances. You can install with:

bcbio_conda install -c conda-forge -c bioconda google-cloud-sdk

For authentication, you want to set up a Google Cloud Platform service account. The environmental variable GOOGLE_APPLICATION_CREDENTIALS identifies a JSON file of credentials. which bcbio passes to Cromwell for authentication:

gcloud auth login
gcloud projects create your-project
gcloud iam service-accounts create your-service-account
gcloud projects add-iam-policy-binding your-project --member \
  "serviceAccount:your-service-account@your-project.iam.gserviceaccount.com" --role "roles/owner"
gcloud iam service-accounts keys create ~/.config/gcloud/your-service-account.json \
  --iam-account your-service-account@your-project.iam.gserviceaccount.com
export GOOGLE_APPLICATION_CREDENTIALS=~/.config/gcloud/your-service-account.json

You’ll need a project for your run along, with the Google Genomics API enabled, and a Google Storage bucket for your data and run intermediates:

gcloud config set project your-project
gcloud services enable genomics.googleapis.com
gsutil mb gs://your-project

Additional documentation for Cromwell: Google Pipelines API and Google authentication.

Data preparation

Cromwell can localize data present in Google Storage buckets as part of the run process and bcbio will translate the data present in these storage bucket into references for the CWL run inputs.

Upload your data with gsutil:

gsutil cp your_data.bam gs://your-project/inputs/

Create a bcbio_system-gcp.yaml input file for Generating CWL for input to a tool:

gs:
  ref: gs://bcbiodata/collections
  inputs:
    - gs://your-project/inputs
resources:
  default: {cores: 8, memory: 3G, jvm_opts: [-Xms750m, -Xmx3000m]}

Then create a sample input CSV and template YAML file for Automated sample configuration. The first column of the CSV file should contain references to your input files (your_file.bam or your_file_R1.fastq.gz;your_file_R2.fastq.gz), which avoids needing to specify the inputs on the command line.

Generate a Common Workflow Language representation:

bcbio_vm.py template --systemconfig bcbio_system-gcp.yaml ${TEMPLATE}-template.yaml $PNAME.csv
bcbio_vm.py cwl --systemconfig bcbio_system-gcp.yaml $PNAME/config/$PNAME.yaml

Running

Run the CWL using Cromwell by specifying the project and root Google Storage bucket for intermediates:

bcbio_vm.py cwlrun cromwell $PNAME-workflow --cloud-project your-project \
    --cloud-root gs://your-project/work_cromwell

Amazon Web Services (AWS Batch)

We’re working to support Amazon Web Services (AWS) using AWS Batch and Cromwell, following the AWS for Genomics documentation. This documents the current work in progress; it is not yet fully running and needs additional Cromwell development for AWS CWL support.

Setup

  1. Optionally, create a bcbio IAM user and bcbio keypair for creating AWS Batch specific resources. bcbio-vm can automate this process, although they can also be pre-existing. If you’d like to use bcbio-vm automation, you’ll need to have an account at Amazon and your Access Key ID and Secret Key ID from the AWS security credentials page. These can be IAM credentials instead of root credentials as long as they have administrator privileges. Make them available to bcbio using the standard environmental variables:

    export AWS_ACCESS_KEY_ID=your_access_key
    export AWS_SECRET_ACCESS_KEY=your_secret_key
    

    With this in place, ceate public/private keys and a bcbio IAM user with:

    bcbio_vm.py aws iam --region=us-east-1
    
  2. Use either existing credentials or those created by bcbio, setup AWS Credentials for accessing AWS resources from your machine by editing ~/.aws/credentials:

    [default]
    aws_access_key_id = YOURACCESSID
    aws_secret_access_key = yoursecretkey
    region = us-east-1
    
  3. Automation creation of resources for AWS Batch. This includes creating a custom Amazon Machine Image (AMI) for AWS Batch, which allows automatic allocation of additional disk space during workflow runs. It also sets up an AWS Batch environment, VPC and IAM for running workflows. A single bcbio-vm commands runs both CloudFormation scripts:

    bcbio_vm.py aws cromwell --keypair bcbio --bucket bcbio-batch-cromwell-test
    

    This will output the S3 bucket and job queue for running Cromwell:

    AMI: ami-00bd75374ccaa1fc6
    Region: us-east-1
    S3 bucket: s3://your-project
    Job Queue (Spot instances): arn:aws:batch:us-east-1:678711657553:job-queue/GenomicsDefaultQueue-358a1deb9f4536b
    High priority Job Queue: arn:aws:batch:us-east-1:678711657553:job-queue/GenomicsHighPriorityQue-3bff21e3c4f44d4
    

Data preparation

The easiest way to organize AWS projects is using an analysis folder inside an S3 bucket. Create a bucket and folder for your analysis and upload input files (fastq or BAM) and other associated files.. Bucket names should include only lowercase letters, numbers and hyphens (-) to conform to S3 bucket naming restrictions and avoid issues with resolution of SSL keys. You can create buckets and upload files using the the AWS cli client or AWS S3 web console:

aws s3 sync /local/inputs s3://your-bucket/inputs

Create a bcbio_system-aws.yaml input file for Generating CWL for input to a tool:

s3:
  ref: s3://bcbiodata/collections
  inputs:
    - s3://your-bucket/inputs
resources:
  default: {cores: 8, memory: 3G, jvm_opts: [-Xms750m, -Xmx3000m]}

Generate a Common Workflow Language representation:

CLOUD=aws
bcbio_vm.py template --systemconfig bcbio_system-$CLOUD.yaml ${TEMPLATE}-template.yaml $PNAME.csv
bcbio_vm.py cwl --systemconfig bcbio_system-$CLOUD.yaml $PNAME/config/$PNAME.yaml

Running

Run the CWL using Cromwell by specifying the batch job queue Amazon Resource Name (ARN) and bucket from the setup process:

bcbio_vm.py cwlrun cromwell $PNAME-workflow \
  -cloud-project arn:aws:batch:us-east-1:678711657553:job-queue/GenomicsDefaultQueue-358a1deb9f4536b \
  -cloud-root s3://your-project

Amazon Web Services (old)

We’re phasing out this approach to AWS support in bcbio and are actively moving to Common Workflow Language based approaches. This documents the old Elasticluster approach to build a cluster on AWS with an encrypted NFS mounted drive and an optional Lustre shared filesystem.

Data preparation

You need a template file describing the type of run to do and a CSV file mapping samples in the bucket to names and any other metadata. See the Automated sample configuration docs for more details about these files. Also upload both of these files to S3.

With that in place, prepare and upload the final configuration to S3 with:

bcbio_vm.py template s3://your-project/your-analysis/template.yaml s3://your-project/your-analysis/name.csv

This will find the input files in the s3://your-project/your-analysis bucket, associate fastq and BAM files with the right samples, and add a found BED files as variant_regions in the configuration. It will then upload the final configuration back to S3 as s3://your-project/your-analysis/name.yaml, which you can run directly from a bcbio cluster on AWS. By default, bcbio will use the us-east S3 region, but you can specify a different region in the s3 path to the metadata file: s3://your-project@eu-central-1/your-analysis/name.csv

We currently support human analysis with both the GRCh37 and hg19 genomes. We can also add additional genomes as needed by the community and generally welcome feedback and comments on reference data support.

AWS setup

The first time running bcbio on AWS you’ll need to setup permissions, VPCs and local configuration files. We provide commands to automate all these steps and once finished, they can be re-used for subsequent runs. To start you’ll need to have an account at Amazon and your Access Key ID and Secret Key ID from the AWS security credentials page. These can be IAM credentials instead of root credentials as long as they have administrator privileges. Make them available to bcbio using the standard environmental variables:

export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key

With this in place, two commands setup your elasticluster and AWS environment to run a bcbio cluster. The first creates public/private keys, a bcbio IAM user, and sets up an elasticluster config in ~/.bcbio/elasticluster/config:

bcbio_vm.py aws iam --region=us-east-1

The second configures a VPC to host bcbio:

bcbio_vm.py aws vpc --region=us-east-1

The aws vpc command is idempotent and can run multiple times if you change or remove parts of the infrastructure. You can also rerun the aws iam command, but if you’d like to generate a new elasticluster configuration file (~/.bcbio/elasticluster/config) add the recreate flag: bcbio_vm.py aws iam --recreate. This generates a new set of IAM credentials and public/private keys. These are only stored in the ~/.bcbio directory so you need to fully recreate them if you delete the old ones.

Running a cluster

Following this setup, you’re ready to run a bcbio cluster on AWS. We start from a standard Ubuntu AMI, installing all software for bcbio and the cluster as part of the boot process.

To configure your cluster run:

bcbio_vm.py aws config edit

This dialog allows you to define the cluster size and machine resources you’d like to use. The defaults only have small instances to prevent accidentally starting an expensive run. If you’re planning a run with less than 32 cores, do not use a cluster and instead run directly on a single machine using one of the large r3 or c3 instances.

This script also sets the size of the encrypted NFS-mounted drive, which you can use to store processing data when running across a distributed cluster. At scale, you can replace this with a Lustre shared filesystem. See below for details on launching and attaching a Lustre filesystem to a cluster.

To ensure everything is correctly configured, run:

bcbio_vm.py aws info

When happy with your setup, start the cluster with:

bcbio_vm.py aws cluster start

The cluster will take five to ten minutes to start and be provisioned. If you encounter any intermittent failures, you can rerun the cluster configuration step with bcbio_vm.py aws cluster setup or the bcbio-specific installation with bcbio_vm.py aws cluster bootstrap.

Running Lustre

Elasticluster mounts the /encrypted directory as a NFS share available across all of the worker machines. You can use this as a processing directory for smaller runs but for larger runs may need a scalable distributed file system. bcbio supports using Intel Cloud Edition for Lustre (ICEL) to set up a Lustre scratch filesystem on AWS.

  • Subscribe to ICEL in the Amazon Marketplace.

  • By default, the Lustre filesystem will be 2TB and will be accessible to all hosts in the VPC. Creation takes about ten minutes and can happen in parallel while elasticluster sets up the cluster. Start the stack:

    bcbio_vm.py aws icel create
    

    If you encounter any intermittent failures when installing collectl plugin, that means lustre server is created successfully, you can rerun the lustre configuration step with bcbio_vm.py aws icel create --setup. If you had any failure creating the lustre server before the collectl plugin installation, you should stop it, and try again.

  • Once the ICEL stack and elasticluster cluster are both running, mount the filesystem on the cluster:

    bcbio_vm.py aws icel mount
    
  • The cluster instances will reboot with the Lustre filesystem mounted.

Running an analysis

To run the analysis, connect to the head node with:

bcbio_vm.py aws cluster ssh

Create your project directory and link the global bcbio configuration file in there with:

  • NFS file system (no Lustre):

    mkdir /encrypted/your-project
    cd !$ && mkdir work && cd work
    
  • Lustre file system:

    sudo mkdir /scratch/cancer-dream-syn3-exome
    sudo chown ubuntu !$
    cd !$ && mkdir work && cd work
    

If you started a single machine, run with:

bcbio_vm.py run -n 8 s3://your-project/your-analysis/name.yaml

Where the -n argument should be the number of cores on the machine.

To run on a full cluster:

bcbio_vm.py ipythonprep s3://your-project/your-analysis/name.yaml slurm cloud -n 60
sbatch bcbio_submit.sh

Where 60 is the total number of cores to use across all the worker nodes. Of your total machine cores, allocate 2 for the base bcbio_vm script and IPython controller instances. The SLURM workload manager distributes jobs across your cluster on a queue called cloud. A slurm-PID.out file in the work directory contains the current status of the job, and sacct_std provides the status of jobs on the cluster. If you are new to SLURM, here is a summary of useful SLURM commands.

On successful completion, bcbio uploads the results of the analysis back into your s3 bucket and folder as s3://your-project/your-analysis/final. You can now cleanup the cluster and Lustre filesystem.

Graphing resource usage

AWS runs include automatic monitoring of resource usage with collectl. bcbio_vm uses collectl statistics to plot CPU, memory, disk and network usage during each step of a run. To prepare resource usage plots after finishing an analysis, first copy the bcbio-nextgen.log file to your local computer. Either use bcbio_vm.py elasticluster sftp bcbio to copy from the work directory on AWS (/encrypted/your-project/work/log/bcbio-nextgen.log) or transfer it from the output S3 bucket (your-project/your-analysis/final/DATE_your-project/bcbio-nextgen.log).

If your run worked cleanly you can use the log input file directly. If you had failures and restarts, or would only like to graph part of the run, you can edit the timing steps. Run grep Timing bcbio-nextgen.log > your-run.txt to get the timing steps only, then edit as desired.

Retrieve the collectl statistics from the AWS cluster and prepare the resource usage graphs with:

bcbio_vm.py graph bcbio-nextgen.log

By default the collectl stats will be in monitoring/collectl and plots in monitoring/graphs based on the above log timeframe. If you need to re-run plots later after shutting the cluster down, you can use the none cluster flag by running bcbio_vm.py graph bcbio-nextgen.log --cluster none.

If you’d like to run graphing from a local non-AWS run, such as a local HPC cluster, run bcbio_vm.py graph bcbio-nextgen.log --cluster local instead.

For convenience, there’s a “serialize” flag (‘-s’) that saves the dataframe used for plotting. In order to explore the data and extract specific datapoints or zoom, one could just deserialize the ouput like a python pickle file:

```

import cPickle as pickle with gzip.open(“./monitoring/collectl_info.pickle.gz”, “rb”) as decomp:

collectl_info = pickle.load(decomp) data, hardware, steps = collectl_info[1][0], collectl_info[1][1], collectl_info[1][2]

```

And plot, slice, zoom it in an jupyter notebook using matplotlib, [highcharts](https://github.com/arnoutaertgeerts/python-highcharts).

In addition to plots, the summarize_timing.py utility script prepares a summary table of run times per step.

Shutting down

The bcbio Elasticluster and Lustre integration can spin up a lot of AWS resources. You’ll be paying for these by the hour so you want to clean them up when you finish running your analysis. To stop the cluster:

bcbio_vm.py aws cluster stop

To remove the Lustre stack:

bcbio_vm.py aws icel stop

Double check that all instances have been properly stopped by looking in the AWS console.

Manual configuration

Experienced elasticluster users can edit the configuration files themselves. bcbio provides a small wrapper that automatically reads and writes these configurations to avoid users needing to understand elasticluster internals, but all functionality is fully available. Edit your ~/.bcbio/elasticluster/config file to change parameters. You can also see the latest example configuration. in the bcbio-vm GitHub repository for more details on the other available options.