AWS Batch

Most users wanting to run on AWS should use the Nextflow Tower instructions. This page describes a manual approach to create a custom AWS environment for advanced users.

We need two AWSBatch compute environments - one for the tasks that only require CPU and one for the tasks that require GPU. In addition to that we need an EFS volume that will be mounted on all EC2 instances used for running jobs.

Create the AWS EFS Volume and Access Point

This can be done from the AWS console following these instructions or if you have experience with AWS-CLI and you prefer that, you can follow these instructions.

When you select the VPC cloud make sure that you select one that is accessible by the EC2 instance as well so that you can mount the access point on it. Also remember the generated FileSystemId when you created the EFS and the path that you used for the access point.

Create the AMI instances

For creating the AMI instances the 2 environments launch 2 EC2 instances from the following public AMIs:

Once the instances are up and running ssh into them and run the following instructions:

sudo yum update -y
sudo amazon-linux-extras install -y epel
sudo yum install -y yum-utils pciutils wget fuse s3fs-fuse bzip2 nfs-utils

# install aws-cli using miniconda
bash -b -f -p $HOME/miniconda
$HOME/miniconda/bin/conda install -c conda-forge -y awscli

# change fs-509941e4:/ to your EFS volume ID and access point
sudo mkdir /efs-multifish
sudo echo -e 'fs-509941e4:/\t/efs-multifish\tefs\trw,_netdev\t0\t0' | sudo tee -a /etc/fstab

# change janelia-nextflow-demo:/multifish to your bucket and folder
sudo mkdir /s3-multifish
sudo echo -e 's3fs#janelia-nextflow-demo:/multifish\t/s3-multifish\tfuse\trw,_netdev,use_path_request_style,allow_other,umask=0000,iam_role=auto,kernel_cache,max_background=1000,max_stat_cache_size=100000,multipart_size=52,parallel_count=30,dbglevel=warn\t0\t0' | sudo tee -a /etc/fstab

sudo mount –a

For the GPU instance we also need to set the default docker runtime to nvidia

# /usr/libexec/docker/ already exists so you can simply edit it 
# and add the `echo -n "--default-runtime nvidia "` line
sudo cat > /usr/libexec/docker/ <<EOF
    echo -n "DOCKER_ADD_RUNTIMES=\""
    for file in /etc/docker-runtimes.d/*; do
        [ -f "$file" ] && [ -x "$file" ] && echo -n "--add-runtime $(basename "$file")=$file "
    echo -n "--default-runtime nvidia "
    echo "\""
} > /run/docker/runtimes.env

The last step before you save your AMI instances stop the ECS service:

sudo systemctl stop ecs
sudo rm -rf /var/lib/ecs/data/agent.db

Once you have the AMI IDs set them in the ‘serverless.yml’ file as well as with other custom properties.

Deploy the AWS batch environment

The AWS batch is deployed using serverless so first install serverless by simply running:

npm install

and then (change the stage to the appropriate value):

npm run sls -- deploy --stage dev

Copy the data to AWS S3

The jobs will get the raw data from AWS S3 but all the results and the temporary files will be generated to the EFS volume.

aws s3 cp /nrs/multifish/Pipeline/Examples/subset/ss s3://janelia-nextflow-demo:/multifish/small --recursive
aws s3 cp /nrs/multifish/Pipeline/segmentation/starfinity/model/starfinity_augment_all s3://janelia-nextflow-demo:/multifish/model

Running the pipeline

Here’s an example of a script:


export TOWER_ACCESS_TOKEN=<your nextflow tower acccess token>

nextflow run \
    -with-tower "" \
    -profile awsbatch \
    -w s3://janelia-nextflow-demo/multifish/work \
    --workers 1 \
    --worker_cores 16 \
    --wait_for_spark_timeout_seconds 3600 \
    --sleep_between_timeout_checks_seconds 2 \
    --gb_per_core 4 \
    --channels "c0,c1" \
    --stitching_ref 1 \
    --dapi_channel c1 \
    --segmentation_cpus 1 \
    --airlocalize_xy_stride 512 \
    --airlocalize_z_stride 256 \
    --airlocalize_cpus 2 \
    --airlocalize_memory "8 G" \
    --spark_local_dir "/tmp" \
    --spark_work_dir "/efs-multifish/spark/small" \
    --data_dir "/s3-multifish/small" \
    --output_dir "/efs-multifish/results/small" \
    --acq_names "LHA3_R3_small,LHA3_R5_small" \
    --ref_acq "LHA3_R3_small" \
    --segmentation_model_dir "/s3-multifish/models/starfinity-model"