dsub 0.3.7

A command-line tool that makes it easy to submit and run batch scripts in the cloud

dsub: simple batch jobs with Docker

Overview

dsub is a command-line tool that makes it easy to submit and run batch scripts in the cloud.

The dsub user experience is modeled after traditional high-performance computing job schedulers like Grid Engine and Slurm. You write a script and then submit it to a job scheduler from a shell prompt on your local machine.

Today dsub supports Google Cloud as the backend batch job runner, along with a local provider for development and testing. With help from the community, we'd like to add other backends, such as a Grid Engine, Slurm, Amazon Batch, and Azure Batch.

Getting started

You can install dsub from PyPI, or you can clone and install from github.

Sunsetting Python 2 support

Python 2 support will end in January 2020. We will turn off our tests running on Python 2. See Python's official article for details.

Pre-installation steps

1. This is optional, but whether installing from PyPI or from github, you are encouraged to use a Python virtualenv.

   If necessary, install virtualenv.

2. Create and activate a Python virtualenv.

   # (You can do this in a directory of your choosing.)
   virtualenv dsub_libs
   source dsub_libs/bin/activate
   

Install dsub

Choose one of the following:

Install from PyPI

1. If necessary, install pip.

2. Install dsub

    pip install dsub
   

Install from github

1. Be sure you have git installed

   Instructions for your environment can be found on the git website.

2. Clone this repository.

   git clone https://github.com/DataBiosphere/dsub
   cd dsub
   

3. Install dsub (this will also install the dependencies)

   python setup.py install
   

4. Set up Bash tab completion (optional).

   source bash_tab_complete
   

Post-installation steps

1. Minimally verify the installation by running:

   dsub --help
   

2. (Optional) Install Docker.

   This is necessary only if you're going to create your own Docker images or use the local provider.

Getting started with the local provider

We think you'll find the local provider to be very helpful when building your pipelines. You'll get quicker turnaround times and won't incur cloud charges using it.

1. Run a dsub job and wait for completion.

   Here is a very simple "Hello World" test:

    dsub \
      --provider local \
      --logging /tmp/dsub-test/logging/ \
      --output OUT=/tmp/dsub-test/output/out.txt \
      --command 'echo "Hello World" > "${OUT}"' \
      --wait
   

2. View the output file.

    cat /tmp/dsub-test/output/out.txt
   

Getting started on Google Cloud

1. Sign up for a Google Cloud Platform account and create a project.

2. Enable the APIs.

3. Install the Google Cloud SDK and run

   gcloud init
   

   This will set up your default project and grant credentials to the Google Cloud SDK. Now provide credentials so dsub can call Google APIs:

   gcloud auth application-default login
   

4. Create a Google Cloud Storage bucket.

   The dsub logs and output files will be written to a bucket. Create a bucket using the storage browser or run the command-line utility gsutil, included in the Cloud SDK.

   gsutil mb gs://my-bucket
   

   Change my-bucket to a unique name that follows the bucket-naming conventions.

   (By default, the bucket will be in the US, but you can change or refine the location setting with the -l option.)

5. Run a dsub job and wait for completion.

   Here is a very simple "Hello World" test:

   dsub \
     --provider google-v2 \
     --project my-cloud-project \
     --regions us-central1 \
     --logging gs://my-bucket/logging/ \
     --output OUT=gs://my-bucket/output/out.txt \
     --command 'echo "Hello World" > "${OUT}"' \
     --wait
   

   Change my-cloud-project to your Google Cloud project, and my-bucket to the bucket you created above.

   The output of the script command will be written to the OUT file in Cloud Storage that you specify.

6. View the output file.

    gsutil cat gs://my-bucket/output/out.txt
   

Backend providers

Where possible, dsub tries to support users being able to develop and test locally (for faster iteration) and then progressing to running at scale.

To this end, dsub provides multiple "backend providers", each of which implements a consistent runtime environment. The current providers are:

• local
• google (deprecated: use google-v2)
• google-v2 (the default)

More details on the runtime environment implemented by the backend providers can be found in dsub backend providers.

Deprecation of the google provider

The original dsub provider was the google provider, built on top of the Google Genomics Pipelines API v1alpha2. The Pipelines API v1alpha2 has been deprecated and was scheduled for turn down at the end of 2018. For more details, see Cloud Genomics v1alpha2 Migration Guide

Replacing v1alpha2 is v2alpha1. dsub has added the google-v2 provider which use v2alpha1 as the backend for running dsub jobs on Google Cloud.

dsub users are encouraged today to use the google-v2 provider. The google-v2 provider is now the default. The google provider for dsub will be removed in a future release.

Migrating existing code from google to google-v2

To migrate existing dsub calls from the google provider to the google-v2 provider:

• Add --provider google-v2 to your command-line
• Set the --machine-type or --min-ram and --min-cores if your tasks need other than 1 core and 3.75 GB of memory

NOTE: The conversion of --min-ram and --min-cores is different with the google-v2 provider than the google provider. Please read the section below to set your parameters appropriately.

--machine-type vs. --min-ram and --min-cores with google-v2

The google provider was backed by the Pipelines API v1alpha2, which accepted minimum ram and minimum cores as parameters and made a "best fit" attempt to translate those parameters into one of the Compute Engine Predefined Machine Types.

The google-v2 provider is backed by the Pipelines API v2alpha1, which does not perform this "best fit" computation, but requires an explicit machine type to be specified.

However, the v2alpha1 API also supports Compute Engine Custom Machine Types, which allow for greater control over machine resources than the predefined machine types do. This allows users to reduce costs by limiting any over-provisioning of Compute Engine VMs.

The google-v2 provider takes advantage of this by translating --min-ram and --min-cores into a custom machine type specification. When migrating existing dsub jobs from google to google-v2 you may find that tasks are allocated smaller VMs with google-v2 than with google because the --min-ram and --min-cores specified for the job are more precisely translated with google-v2. You will need to adjust your resource parameters accordingly.

Notes for google-v2 resource specifications:

• n1-standard-1 is the default machine type as it was with the google provider.
• Either --machine-type or --min-ram and --min-cores may be specified, but not both.
• To use one of the Shared-core machine types, use the --machine-type flag.

dsub features

The following sections show how to run more complex jobs.

Defining what code to run

You can provide a shell command directly in the dsub command-line, as in the hello example above.

You can also save your script to a file, like hello.sh. Then you can run:

dsub \
    ... \
    --script hello.sh

If your script has dependencies that are not stored in your Docker image, you can transfer them to the local disk. See the instructions below for working with input and output files and folders.

Selecting a Docker image

By default, dsub uses a stock Ubuntu image. You can change the image by passing the --image flag.

dsub \
    ... \
    --image ubuntu:16.04 \
    --script hello.sh

Note: your --image must include the Bash shell interpreter.

Passing parameters to your script

You can pass environment variables to your script using the --env flag.

dsub \
    ... \
    --env MESSAGE=hello \
    --command 'echo ${MESSAGE}'

The environment variable MESSAGE will be assigned the value hello when your Docker container runs.

Your script or command can reference the variable like any other Linux environment variable, as ${MESSAGE}.

Be sure to enclose your command string in single quotes and not double quotes. If you use double quotes, the command will be expanded in your local shell before being passed to dsub. For more information on using the --command flag, see Scripts, Commands, and Docker

To set multiple environment variables, you can repeat the flag:

--env VAR1=value1 \
--env VAR2=value2

You can also set multiple variables, space-delimited, with a single flag:

--env VAR1=value1 VAR2=value2

Working with input and output files and folders

dsub mimics the behavior of a shared file system using cloud storage bucket paths for input and output files and folders. You specify the cloud storage bucket path. Paths can be:

• file paths like gs://my-bucket/my-file
• folder paths like gs://my-bucket/my-folder
• wildcard paths like gs://my-bucket/my-folder/*

See the inputs and outputs documentation for more details.

Transferring input files to a Google Cloud Storage bucket.

If your script expects to read local input files that are not already contained within your Docker image, the files must be available in Google Cloud Storage.

If your script has dependent files, you can make them available to your script by:

• Building a private Docker image with the dependent files and publishing the image to a public site, or privately to Google Container Registry
• Uploading the files to Google Cloud Storage

To upload the files to Google Cloud Storage, you can use the storage browser or gsutil. You can also run on data that’s public or shared with your service account, an email address that you can find in the Google Cloud Console.

Files

To specify input and output files, use the --input and --output flags:

dsub \
    ... \
    --input INPUT_FILE_1=gs://my-bucket/my-input-file-1 \
    --input INPUT_FILE_2=gs://my-bucket/my-input-file-2 \
    --output OUTPUT_FILE=gs://my-bucket/my-output-file \
    --command 'cat "${INPUT_FILE_1}" "${INPUT_FILE_2}" > "${OUTPUT_FILE}"'

In this example:

• a file will be copied from gs://my-bucket/my-input-file-1 to a path on the data disk
• the path to the file on the data disk will be set in the environment variable ${INPUT_FILE_1}
• a file will be copied from gs://my-bucket/my-input-file-2 to a path on the data disk
• the path to the file on the data disk will be set in the environment variable ${INPUT_FILE_2}

The --command can reference the file paths using the environment variables.

Also in this example:

• a path on the data disk will be set in the environment variable ${OUTPUT_FILE}
• the output file will written to the data disk at the location given by ${OUTPUT_FILE}

After the --command completes, the output file will be copied to the bucket path gs://my-bucket/my-output-file

Multiple --input, and --output parameters can be specified and they can be specified in any order.

Folders

To copy folders rather than files, use the --input-recursive and output-recursive flags:

dsub \
    ... \
    --input-recursive FOLDER=gs://my-bucket/my-folder \
    --command 'find ${FOLDER} -name "foo*"'

Multiple --input-recursive, and --output-recursive parameters can be specified and they can be specified in any order.

Mounting "resource data"

If you have one of the following:

1. A large set of resource files, your code only reads a subset of those files, and the decision of which files to read is determined at runtime, or
2. A large input file over which your code makes a single read pass or only needs to read a small range of bytes,

then you may find it more efficient at runtime to access this resource data via mounting a Google Cloud Storage bucket read-only or mounting a persistent disk created from a Compute Engine Image read-only.

The google-v2 provider supports these two methods of providing access to resource data. The local provider supports mounting a local directory in a similar fashion to support your local development.

To have the google-v2 provider mount a Cloud Storage bucket using Cloud Storage FUSE, use the --mount command line flag:

--mount MYBUCKET=gs://mybucket

The bucket will be mounted into the Docker container running your --script or --command and the location made available via the environment variable ${MYBUCKET}. Inside your script, you can reference the mounted path using the environment variable. Please read Key differences from a POSIX file system and Semantics before using Cloud Storage FUSE.

To have the google-v2 provider mount a persistent disk created from an image, use the --mount command line flag and the url of the source image and the size (in GB) of the disk:

--mount MYDISK="https://www.googleapis.com/compute/v1/projects/your-project/global/images/your-image 50"

The image will be used to create a new persistent disk, which will be attached to a Compute Engine VM. The disk will mounted into the Docker container running your --script or --command and the location made available by the environment variable ${MYDISK}. Inside your script, you can reference the mounted path using the environment variable.

To create an image, see Creating a custom image.

To have the local provider mount a directory read-only, use the --mount command line flag and a file:// prefix:

--mount LOCAL_MOUNT=file://path/to/my/dir

The local directory will be mounted into the Docker container running your --scriptor --command and the location made available via the environment variable ${LOCAL_MOUNT}. Inside your script, you can reference the mounted path using the environment variable.

Notice

For the google provider, as a getting started convenience, if --input-recursive or --output-recursive are used, dsub will automatically check for and, if needed, install the Google Cloud SDK in the Docker container at runtime (before your script executes).

If you use the recursive copy features, install the Cloud SDK in your Docker image when you build it to avoid the installation at runtime.

If you use a Debian or Ubuntu Docker image, you are encouraged to use the package installation instructions.

If you use a Red Hat or CentOS Docker image, you are encouraged to use the package installation instructions.

The installation of the CloudSDK into your Docker image is not needed for the local or the google-v2 providers.

Setting resource requirements

dsub tasks run using the local provider will use the resources available on your local machine.

dsub tasks run using the google or google-v2 providers can take advantage of a wide range of CPU, RAM, disk, and hardware accelerator (eg. GPU) options.

See the Compute Resources documentation for details.

Submitting a batch job

Each of the examples above has demonstrated submitting a single task with a single set of variables, inputs, and outputs. If you have a batch of inputs and you want to run the same operation over them, dsub allows you to create a batch job.

Instead of calling dsub repeatedly, you can create a tab-separated values (TSV) file containing the variables, inputs, and outputs for each task, and then call dsub once. The result will be a single job-id with multiple tasks. The tasks will be scheduled and run independently, but can be monitored and deleted as a group.

Tasks file format

The first line of the TSV file specifies the names and types of the parameters. For example:

--env SAMPLE_ID<tab>--input VCF_FILE<tab>--output OUTPUT_PATH

Each addition line in the file should provide the variable, input, and output values for each task. Each line beyond the header represents the values for a separate task.

Multiple --env, --input, and --output parameters can be specified and they can be specified in any order. For example:

--env SAMPLE<tab>--input A<tab>--input B<tab>--env REFNAME<tab>--output O
S1<tab>gs://path/A1.txt<tab>gs://path/B1.txt<tab>R1<tab>gs://path/O1.txt
S2<tab>gs://path/A2.txt<tab>gs://path/B2.txt<tab>R2<tab>gs://path/O2.txt

Tasks parameter

Pass the TSV file to dsub using the --tasks parameter. This parameter accepts both the file path and optionally a range of tasks to process. The file may be read from the local filesystem (on the machine you're calling dsub from), or from a bucket in Google Cloud Storage (file name starts with "gs://").

For example, suppose my-tasks.tsv contains 101 lines: a one-line header and 100 lines of parameters for tasks to run. Then:

dsub ... --tasks ./my-tasks.tsv

will create a job with 100 tasks, while:

dsub ... --tasks ./my-tasks.tsv 1-10

will create a job with 10 tasks, one for each of lines 2 through 11.

The task range values can take any of the following forms:

• m indicates to submit task m (line m+1)
• m- indicates to submit all tasks starting with task m
• m-n indicates to submit all tasks from m to n (inclusive).

Logging

The --logging flag points to a location for dsub task log files. For details on how to specify your logging path, see Logging.

Job control

It's possible to wait for a job to complete before starting another. For details, see job control with dsub.

Retries

It is possible for dsub to automatically retry failed tasks. For details, see retries with dsub.

Labeling jobs and tasks

You can add custom labels to jobs and tasks, which allows you to monitor and cancel tasks using your own identifiers. In addition, with the google provider, labeling a task will label associated compute resources such as virtual machines and disks.

For more details, see Checking Status and Troubleshooting Jobs

Viewing job status

The dstat command displays the status of jobs:

dstat --provider google-v2 --project my-cloud-project

With no additional arguments, dstat will display a list of running jobs for the current USER.

To display the status of a specific job, use the --jobs flag:

dstat --provider google-v2 --project my-cloud-project --jobs job-id

For a batch job, the output will list all running tasks.

Each job submitted by dsub is given a set of metadata values that can be used for job identification and job control. The metadata associated with each job includes:

• job-name: defaults to the name of your script file or the first word of your script command; it can be explicitly set with the --name parameter.
• user-id: the USER environment variable value.
• job-id: takes the form job-name--userid--timestamp where the job-name is truncated at 10 characters and the timestamp is of the form YYMMDD-HHMMSS-XX, unique to hundredths of a second.
• task-id: if the job is submitted with the --tasks parameter, each task gets a sequential value of the form "task-n" where n is 1-based.

Note that the job metadata values will be modified to conform with the "Label Restrictions" listed in the Checking Status and Troubleshooting Jobs guide.

Metadata can be used to cancel a job or individual tasks within a batch job.

For more details, see Checking Status and Troubleshooting Jobs

Summarizing job status

By default, dstat outputs one line per task. If you're using a batch job with many tasks then you may benefit from --summary.

$ dstat --provider google-v2 --project my-project --status '*' --summary

Job Name        Status         Task Count
-------------   -------------  -------------
my-job-name     RUNNING        2
my-job-name     SUCCESS        1

In this mode, dstat prints one line per (job name, task status) pair. You can see at a glance how many tasks are finished, how many are still running, and how many are failed/canceled.

Deleting a job

The ddel command will delete running jobs.

By default, only jobs submitted by the current user will be deleted. Use the --users flag to specify other users, or '*' for all users.

To delete a running job:

ddel --provider google-v2 --project my-cloud-project --jobs job-id

If the job is a batch job, all running tasks will be deleted.

To delete specific tasks:

ddel \
    --provider google-v2 \
    --project my-cloud-project \
    --jobs job-id \
    --tasks task-id1 task-id2

To delete all running jobs for the current user:

ddel --provider google-v2 --project my-cloud-project --jobs '*'

What next?

• See the examples:

  • Custom scripts
  • Decompress files
  • FastQC
  • Samtools index

• See more documentation for:

  • Scripts, Commands, and Docker
  • Input and Output File Handling
  • Logging
  • Compute Resources
  • Job Control
  • Retries
  • Checking Status and Troubleshooting Jobs
  • Backend providers