sparkhub 0.2.1.1

Cloud utilities for running Hail systematically.

pycloud

Introduction

pycloud is codebase that provides an user-friendly interface around using gsutil and gcloud in interactive Python environemnts such as Jupyter.

Information on gcloud and gsutil

Command-line references

gcloud Overview

gsutil Overview

Regions and zones

Google regions and zones

Regions are collections of zones. In order to deploy fault-tolerant applications that have high availability, Google recommends deploying applications across multiple zones and multiple regions.

If you have specific needs that require your data to live in the US, it makes sense to store your resources in zones in the us-central1 region or zones in the us-east1 region.

A zone is an isolated location within a region.

Stick with Eastern US, us-east1-b, us-east1-c, us-east1-d

Permissions

Project permission (IAM)

• Owner

• Editor

• Viewer

• Browser

Ch roles:

R: READ
W: WRITE
O: OWNER

GCloud class

To sign into Google Cloud,

gc = GCloud(account = '[e-mail]', 
            project = 'daly-neale-sczmeta', 
            local = True)

To logout of Google Cloud,

gc.logout()

Common functions

ls:

gc.ls('gs://sczmeta_genomes/test/*')

rm:

gc.rm('gs://test/', recursive=True)

cp:

gc.cpi(glob.glob('test/*'), 'gs://test/')

ComputeEngine class

The ComputeEngine class is used to monitor ComputeEngine virtual machines currently running in Google Cloud.

Create a ComputeEngine object:

ce = gc.ComputeEngine() or ce = ComputeEngine(account = '[e-mail]', project = 'daly-neale-sczmeta')

Get all running VM instances:

ce.get_instances()

Load a running instance:

cei = ce.ComputeEngineInstance('vm01')

ComputeEngineInstance class

The ComputeEngineInstance class is used to create, modify, and delete a single virtual machine running in Google Cloud.

To load a running instance:

cei = ce.ComputeEngineInstance('vm01')

or

cei = ComputeEngineInstance(instance_name, project_name)

DataProc class

The ComputeEngine class is used to monitor DataProc clusters currently running in Google Cloud.

To create a DataProc object:

dp = DataProc(account='[e-mail]', 
              project='daly-neale-sczmeta')

DataProcInstance class

The DataProcInstance class is used to create, modify, and delete a DataProc cluster running in Google Cloud.

dpi = DataProcInstance('cluster01', '[e-mail]', 'daly-neale-sczmeta', 
                       zone = 'us-central1-b',
                       master_machine_type = 'n1-highmem-4', 
                       master_boot_disk_size = 50, 
                       n_workers = 2, 
                       worker_machine_type = 'n1-highmem-4', 
                       worker_boot_disk_size = 50,
                       n_pre_workers = 2, create = False)

To change the number of preemptible workers:

dpi.update(n_pre_workers = 20)

To delete a cluster,

dpi.delete()

HailRunner class

The HailRunner class provides a user-friendly interface to submitting sequential Hail jobs to the Spark cluster. HailRunner can be inherited into downstream classes to be adapted into different Cloud infrastructure.

outdir = 'outdir'

runner = HailRunner(outdir = outdir, 
                    input_file = "/Users/tsingh/1000Genomes_248samples_coreExome10K.vcf.bgz",
                    hail_jar_path = "/Users/tsingh/repos/hail/build/libs/hail-all-spark.jar", 
                    pyhail_zip = "/Users/tsingh/repos/hail/python/")

runner.add_batch(""" 
vds = hc.import_vcf('{}')
vds = vds.split_multi()
vds.write('{}')""", os.path.join(outdir, '1kg.vds'))

runner.submit('save_vds')

runner.quick_submit("""
vds = hc.read('{}')

print(vds.count(genotypes=True))
""".format(runner.f))

HailRunnerGC class

The HailRunner class adapted for use with Google Cloud and DataProc.

outdir = 'test/'
bucket_dir = 'gs://test/'

runner = HailRunnerGC('vm01',
                      outdir = outdir, 
                      input_file = "gs://data/exomes.vcf.bgz")

runner.add_batch(""" 
vds = hc.import_vcf('{}')

vds = vds.split_multi()

vds.write('{}')""", change_ext(runner.f, '.vds', '.vcf.bgz', bucket_dir))

runner.submit('save_vds')

runner.quick_submit("""

vds = hc.read('{}')

print(vds.count())
print(vds.variant_schema)
print(vds.sample_schema)
print(vds.global_schema)

""".format(runner.f))

Installing Hail on Broad prem

Add SPARK_HOME, HAIL_HOME, and add Hail and Spark bins to PATH.

On Broad cluster, get the version of Spark correct. ./gradlew -Dspark.version=2.1.0.cloudera shadowJar

use CMake
use GCC-5.2
use Java-1.8

before running gradlew