A Workflow Management System geared towards scientific workflows. Cromwell is open sourced under the BSD 3-Clause license.
- Getting Help
- Requirements
- Building
- Installing
- Command Line Usage
- Getting Started with WDL
- Configuring Cromwell
- Backends
- Runtime Attributes
- Logging
- Workflow Options
- Call Caching
- REST API
- REST API Versions
- POST /api/workflows/:version
- POST /api/workflows/:version/batch
- POST /api/workflows/:version/validate
- GET /api/workflows/:version/query
- POST /api/workflows/:version/query
- GET /api/workflows/:version/:id/status
- GET /api/workflows/:version/:id/outputs
- GET /api/workflows/:version/:id/timing
- GET /api/workflows/:version/:id/outputs/:call
- GET /api/workflows/:version/:id/logs/:call
- GET /api/workflows/:version/:id/logs
- GET /api/workflows/:version/:id/metadata
- POST /api/workflows/:version/:id/abort
- POST /api/workflows/:version/:id/call-caching
- POST /api/workflows/:version/:id/call-caching/:call
- Error handling
- Developer
The WDL website is the best place to go for more information on both WDL and Cromwell. In particular new users should check out the user guide which has many tutorials, examples and other bits to get you started.
If you have questions that aren't covered by the website you can ask them in the support forum.
There is a Cromwell gitter channel where people can discuss Cromwell and related topics with both the developers and user community.
The following is the toolchain used for development of Cromwell. Other versions may work, but these are recommended.
sbt assembly
will build a runnable JAR in target/scala-2.11/
Tests are run via sbt test
. Note that the tests do require Docker to be running. To test this out while downloading the Ubuntu image that is required for tests, run docker pull ubuntu:latest
prior to running sbt test
OS X users can install Cromwell with Homebrew: brew install cromwell
.
Run the JAR file with no arguments to get the usage message:
$ java -jar cromwell.jar
java -jar cromwell.jar <action> <parameters>
Actions:
run <WDL file> [<JSON inputs file> [<JSON workflow options>
[<OUTPUT workflow metadata>]]]
Given a WDL file and JSON file containing the value of the
workflow inputs, this will run the workflow locally and
print out the outputs in JSON format. The workflow
options file specifies some runtime configuration for the
workflow (see README for details). The workflow metadata
output is an optional file path to output the metadata.
Use a single dash ("-") to skip optional files. Ex:
run noinputs.wdl - - metadata.json
server
Starts a web server on port 8000. See the web server
documentation for more details about the API endpoints.
Given a WDL file and a JSON inputs file (see inputs
subcommand), Run the workflow and print the outputs:
$ java -jar cromwell.jar run 3step.wdl inputs.json
... play-by-play output ...
{
"three_step.ps.procs": "/var/folders/kg/c7vgxnn902lc3qvc2z2g81s89xhzdz/T/stdout1272284837004786003.tmp",
"three_step.cgrep.count": 0,
"three_step.wc.count": 13
}
The JSON inputs can be left off if there's a file with the same name as the WDL file but with a .inputs
extension. For example, this will assume that 3step.inputs
exists:
$ java -jar cromwell.jar run 3step.wdl
If your workflow has no inputs, you can specify -
as the value for the inputs parameter:
$ java -jar cromwell.jar run my_workflow.wdl -
The third, optional parameter to the 'run' subcommand is a JSON file of workflow options. By default, the command line will look for a file with the same name as the WDL file but with the extension .options
. But one can also specify a value of -
manually to specify that there are no workflow options.
See the section workflow options for more details.
$ java -jar cromwell.jar run my_jes_wf.wdl my_jes_wf.json wf_options.json
The fourth, optional parameter to the 'run' subcommand is a path where the workflow metadata will be written. By default, no workflow metadata will be written.
$ java -jar cromwell.jar run my_wf.wdl - - my_wf.metadata.json
... play-by-play output ...
$ cat my_wf.metadata.json
{
"calls": {
"example.my_task": [{
"executionStatus": "Done",
"stdout": "/Users/cromwell/cromwell-executions/example/22b6f829-e2f9-4813-9d20-3328669c786b/call-my_task/stdout",
"outputs": {
"result": "my example output"
},
"inputs": {
},
"returnCode": 0,
"backend": "Local",
"end": "2015-10-29T03:16:51.732-03:00",
"stderr": "/Users/cromwell/cromwell-executions/example/22b6f829-e2f9-4813-9d20-3328669c786b/call-my_task/stderr",
"start": "2015-10-29T03:16:51.213-03:00"
"executionEvents": [{
"description": "running docker",
"startTime": "2015-10-29T03:16:51.213-03:00",
"endTime": "2015-10-29T03:16:51.732-03:00"
}],
"attempt": 1
}]
},
"outputs": {
"example.my_task.result": "my = /root/22b6f829-e2f9-4813-9d20-3328669c786b/call-my_task"
},
"id": "22b6f829-e2f9-4813-9d20-3328669c786b",
"inputs": {
},
"submission": "2015-10-29T03:16:51.125-03:00",
"status": "Succeeded",
"end": "2015-10-29T03:16:51.740-03:00",
"start": "2015-10-29T03:16:51.125-03:00"
}
Start a server on port 8000, the API for the server is described in the REST API section.
For many examples on how to use WDL see the WDL site
Cromwell's default configuration file is located at src/main/resources/application.conf
.
The configuration file is in Hocon which means the configuration file can specify configuration as JSON-like stanzas like:
webservice {
port = 8000
interface = 0.0.0.0
instance.name = "reference"
}
Or, alternatively, as dot-separated values:
webservice.port = 8000
webservice.interface = 0.0.0.0
webservice.instance.name = "reference"
This allows any value to be overridden on the command line:
java -Dwebservice.port=8080 cromwell.jar ...
It is recommended that one copies src/main/resources/application.conf
, modify it, then link to it via:
java -Dconfig.file=/path/to/application.conf cromwell.jar ...
Cromwell uses either an in-memory or MySQL database to track the execution of workflows and store outputs of task invocations.
By default, Cromwell uses an in-memory database which will only live for the duration of the JVM. This provides a quick way to run workflows locally without having to set up MySQL, though it also makes workflow executions somewhat transient.
To configure Cromwell to instead point to a MySQL database, first create the empty database. In the example below, the database name is cromwell
.
Then, edit the configuration file database
stanza, as follows:
database {
config = main.mysql
main {
mysql {
db.url = "jdbc:mysql://localhost:3306/cromwell"
db.user = "root"
db.password = ""
db.driver = "com.mysql.jdbc.Driver"
db.connectionTimeout = 5000 // NOTE: The default 1000ms is often too short for production mysql use
driver = "slick.driver.MySQLDriver$"
}
}
test {
...
}
}
For backends that support aborting task invocations, Cromwell can be configured to automatically try to abort all currently running calls (and set their status to Aborted
) when a SIGINT is sent to the Cromwell process. To turn this feature on, set the configuration option
backend {
abortJobsOnTerminate=true
}
Or, via -Dbackend.abortJobsOnTerminate=true
command line option.
A backend represents a way to run the user's command specified in the task
section. Currently three backends are supported:
- Local - Run jobs as subprocesses. Supports launching in Docker containers.
- Sun GridEngine - Use
qsub
and job monitoring to run scripts. - Google JES - Launch jobs on Google Compute Engine through the Job Execution Service (JES).
Backends are specified via the configuration option backend.backend
which can accept the values: sge
, local
, and jes
(e.g. java -Dbackend.backend=sge
).
Each backend will utilize one filesystem to store the directory structure of an executed workflow. Currently, the backend and the type of filesystem that the backend uses are tightly coupled. In future versions of Cromwell, they may be more loosely coupled.
The backend/filesystem pairings are as follows:
- Local Backend uses the Shared Local Filesystem
- SGE Backend uses the Shared Local Filesystem
- JES Backend uses the Google Cloud Storage Filesystem
For the local and Sun GridEngine backends, the following is required of the underlying filesystem:
- (
local
backend) Subprocesses that Cromwell launches can use child directories that Cromwell creates as their CWD. The subprocess must have write access to the directory that Cromwell assigns as its current working directory. - (
sge
backend) Jobs launched withqsub
can use directories that Cromwell creates as the working directory of the job, and write files to those directories.
The root directory that Cromwell uses for all workflows (cromwell-root
) defaults to ./cromwell-executions
. However, this is can be overwritten with the -Dbackend.shared-filesystem.root=/your/path
option on the command line, or via Cromwell's configuration file
When cromwell runs a workflow, it first creates a directory <cromwell-root>/<workflow_uuid>
. This is called the workflow_root
and it is the root directory for all activity in this workflow.
Each call
has its own subdirectory located at <workflow_root>/call-<call_name>
. This is the <call_dir>
. Within this directory are special files written by the backend and they're supposed to be backend specific things tho
10000
ugh there are commonalities. For example, having a stdout
and stderr
file is common among both backends and they both write a shell script file to the <call_dir>
as well. See the descriptions below for details about backend-specific files that are written to these directories.
An example of a workflow output directory would look like this:
cromwell-executions/
└── three_step
└── df6363df-d812-4088-acfd-5b00ef3f5dcc
├── call-cgrep
│  ├── cromwell_root
│  │  └── cromwell
│  │  └── cromwell-executions
│  │  └── three_step
│  │  └── df6363df-d812-4088-acfd-5b00ef3f5dcc
│  │  └── call-ps
│  │  └── stdout
│  ├── rc
│  ├── script
│  ├── stderr
│  └── stdout
├── call-ps
│  ├── rc
│  ├── script
│  ├── stderr
│  └── stdout
└── call-wc
├── cromwell_root
│  └── cromwell
│  └── cromwell-executions
│  └── three_step
│  └── df6363df-d812-4088-acfd-5b00ef3f5dcc
│  └── call-ps
│  └── stdout
├── rc
├── script
├── stderr
└── stdout
WDL File
task ps {
command {
ps
}
output {
File procs = stdout()
}
}
task cgrep {
String pattern
File in_file
command {
grep '${pattern}' ${in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
task wc {
File in_file
command {
cat ${in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
workflow three_step {
call ps
call cgrep {
input: in_file=ps.procs
}
call wc {
input: in_file=ps.procs
}
}
This workflow output directory would be the result of running the above WDL file with Cromwell from the directory /cromwell_root
.
In the above directory structure, you'll notice that the call-cgrep
and call-wc
sub-directories both contain a directory structure to point to the stdout
file from the invocation of ps
. In these cases, that stdout
file is a localized version of the one within call-ps/stdout
. By default both of those stdout
files would be hard-links but they could also be symbolic links or copies of the file, depending on how Cromwell is configured (see below). The directory structure is nested so deeply to avoid collisions. For example, if either of these call invocations referenced two files called stdout
, they'd collide if they were put into the same directory so the full directory structure is maintained.
Any input files to a call need to be localized into the <call_dir>
. There are a few localization strategies that Cromwell will try until one works. Below is the default order specified in application.conf
but this order can be overridden:
hard-link
- This will create a hard link (not symbolic) link to the filesoft-link
- Create a symbolic link to the file. This strategy is not applicable for tasks which specify a Docker image and will be ignored.copy
- Make a copy the file
These options can be overridden with command line options to Java. For instance, to use the strategies copy
and hard-link
, in that order:
java -Dbackend.shared-filesystem.localization.0=copy -Dbackend.shared-filesystem.localization.1=hard-link cromwell.jar ...
Backends that use the shared filesystem can accept the following values for File
variables:
- Local file system paths, either relative or absolute. Relative paths are interpreted as relative to the current working directory of the Cromwell process.
- Google Cloud Storage URIs (e.g.
gs://my-bucket/x/y/z.txt
). Any GCS URI will be downloaded locally
The Google Cloud Storage (GCS) Filesystem is only used for when a workflow is run on Google JES. It uses the same directory structure as the Shared Local Filesystem, however it is rooted at one of the following GCS locations:
- If the
jes_gcs_root
workflow option is set, this is used first. - Otherwise,
backend.jes.baseExecutionBucket
in the configuration file, which can also be set viajava -Dbackend.jes.baseExecutionBucket="gs://my-bucket/"
, will be used instead.
Google Cloud Storage URIs are the only acceptable values for File
inputs for workflows using the JES backend.
The local backend will simply launch a subprocess for each task invocation and wait for it to exit.
This backend creates three files in the <call_dir>
(see previous section):
script
- A shell script of the job to be run. This contains the user's command from thecommand
section of the WDL code.stdout
- The standard output of the processstderr
- The standard error of the process
The script
file contains:
cd <container_call_root>
<user_command>
echo $? > rc
<container_call_root>
would be equal to <call_dir>
for non-Docker jobs, or it would be under /root/<workflow_uuid>/call-<call_name>
if this is running in a Docker container.
The subprocess command that the local backend will launch is:
"/bin/bash" "-c" "cat script | <docker_run> /bin/bash <&0"
Where <docker_run>
will be non-empty if this particular task specified a Docker container to run in. <docker_run>
looks like this:
docker run -v <local_workflow_dir>:/root/<workflow_uuid> -i <image>
NOTE: If you are using the local backend with Docker and Docker Machine on Mac OS X, by default Cromwell can only run from in any path under your home directory.
The
-v
flag will only work if<local_workflow_dir>
is within your home directory because VirtualBox with Docker Machine only exposes the home directory by default. Any local path used in-v
that is not within the user's home directory will silently be interpreted as references to paths on the VirtualBox VM. This can manifest in Cromwell as tasks failing for odd reasons (like missing RC file)See https://docs.docker.com/engine/userguide/dockervolumes/ for more information on volume mounting in Docker.
The GridEngine backend uses qsub
to launch a job and will poll the filesystem to determine if a job is completed.
This backend makes the same assumption about the filesystem that the local backend does: the Cromwell process and the jobs both have read/write access to the CWD of the job.
The CWD will contain a script.sh
file which will contain:
\#!/bin/sh
<user_command>
echo $? > rc
The job is launched using the following command:
qsub -q <queue_name> -P <project_name> -N <job_name> -V -b n -wd <call_dir> -o stdout -e stderr <call_dir>/script.sh
<job_name>
is the string: cromwell_<workflow_uuid_short>_<call_name>
(e.g. cromwell_5103f8db_my_task
).
<queue_name>
is an optional parameter; (e.g., long
).
<project_name>
is an optional parameter; (e.g., MyProjectName
). These optional parameters can be configured in the Cromwell configuration file as follows:
backend {
sge {
queue: "long"
project: "MyProjectName"
}
}
the <call_dir>
contains the following special files added by the SGE backend:
qsub.stdout
,qsub.stderr
- The results of the qsub command.script.sh
- File containing the user's command and some wrapper code.stdout
,stderr
- Standard output streams of the actual job.rc
- Return code of the SGE job, populated when the job has finished.
The SGE backend gets the job ID from parsing the qsub.stdout
text file.
Since the script.sh
ends with echo $? > rc
, the backend will wait for the existence of this file, parse out the return code and determine success or failure and then subsequently post-process.
Google JES (Job Execution Service) is a Docker-as-a-service from Google.
You'll need the following things to get started:
- A Google Project (Manage/create projects here)
- A Google Cloud Storage bucket (View/create buckets in your project here)
On your Google project, open up the API Manager and enable the following APIs:
- Google Compute Engine
- Google Cloud Storage
- Genomics API
If your project is my-project
your bucket is gs://my-bucket/
, then update your Cromwell configuration file as follows:
backend {
backend = "jes"
jes {
// Google project
project = "my-project"
// Location to store workflow results, must be a gs:// URL
baseExecutionBucket = "gs://my-bucket/cromwell-executions"
// Root URL for the API
endpointUrl = "https://genomics.googleapis.com/"
// Polling for completion backs-off gradually for slower-running jobs.
// This is the maximum polling interval (in seconds):
maximumPollingInterval = 600
}
}
The google
stanza in the Cromwell configuration file defines how to authenticate to Google. There are three authentication schemes:
application_default
- (default, recommended) Use application default credentials.service_account
- Use a specific service account and key file (in PEM format) to authenticate.user_account
- Authenticate as a user.
By default, application default credentials will be used. The configuration file should look like this to use application default credentials:
google {
applicationName = "cromwell"
cromwellAuthenticationScheme = "application_default"
}
To authenticate, run the following commands from your command line (requires gcloud):
$ gcloud auth login
$ gcloud config set project my-project
This should be all that's necessary to run Cromwell using the JES backend.
First create a new service account through the API Credentials page. Go to Create credentials -> Service account key. Then in the Service account dropdown select New service account. Fill in a name (e.g. my-account
), and select key type of JSON.
Creating the account will cause the JSON file to be downloaded. The structure of this file is roughly like this (account name is my-account
):
{
"type": "service_account",
"project_id": "my-project",
"private_key_id": "OMITTED",
"private_key": "-----BEGIN PRIVATE KEY-----\nBASE64 ENCODED KEY WITH \n TO REPRESENT NEWLINES\n-----END PRIVATE KEY-----\n",
"client_email": "my-account@my-project.iam.gserviceaccount.com",
"client_id": "22377410244549202395",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://accounts.google.com/o/oauth2/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/my-account%40my-project.iam.gserviceaccount.com"
}
Most importantly, the value of the client_email
field should go into the google.serviceAuth.serviceAccountId
field in the configuration (see below).
The private_key
portion needs to be pulled into its own file (e.g. my-key.pem
). The \n
s in the string need to be converted to newline characters.
google {
applicationName = "cromwell"
cromwellAuthenticationScheme = "service_account"
// If cromwellAuthenticationScheme is "service_account"
serviceAuth {
pemFile = "/path/to/secret/my-key.pem"
serviceAccountId = "my-account@my-project.iam.gserviceaccount.com"
}
}
Data localization can be performed on behalf of an other entity (typically a user).
This allows cromwell to localize file that otherwise wouldn't be accessible using whichever cromwellAuthenticationScheme
has been defined in the google
configuration (e.g. if data has restrictive ACLs).
To enable this feature, two pieces of configuration are needed:
1 - ClientID/Secret
An entry must be added in the google
stanza, indicating a pair of client ID / client Secret that have been used to generate a refresh token for the entity that will be used during localization:
google {
cromwellAuthenticationScheme = "service_account"
serviceAuth {
pemFile = "/path/to/secret/cromwell-svc-acct.pem"
serviceAccountId = "806222273987-gffklo3qfd1gedvlgr55i84cocjh8efa@developer.gserviceaccount.com"
}
userAuthenticationScheme = "refresh"
refreshTokenAuth = {
client_id = "myclientid.apps.googleusercontent.com"
client_secret = "clientsecretpassphrase"
}
}
2 - Refresh Token
A refresh_token field must be specified in the workflow options when submitting the job. Omitting this field will cause the workflow to fail.
The refresh token is passed to JES along with the client ID and Secret pair, which allows JES to localize and delocalize data as the entity represented by the refresh token. Note that upon generation of the refresh token, the application must ask for GCS read/write permission using the appropriate scope.
It is possible to reference private docker images in DockerHub to be run on JES. However, in order for the image to be pulled, the docker credentials with access to this image must be provided in the configuration file.
docker {
dockerAccount = "mydockeraccount@mail.com"
dockerToken = "mydockertoken"
}
It is now possible to reference an image only this account has access to:
task mytask {
command {
...
}
runtime {
docker: "private_repo/image"
memory: "8 GB"
cpu: "1"
}
...
}
Note that if the docker image to be used is public there is no need to add this configuration.
In order to monitor metrics (CPU, Memory, Disk usage...) about the VM during Call Runtime, a workflow option can be used to specify the path to a script that will run in the background and write its output to a log file.
{
"monitoring_script": "gs://cromwell/monitoring/script.sh"
}
The output of this script will be written to a monitoring.log
file that will be available in the call gcs bucket when the call completes.
Runtime attributes are used to customize tasks. Within a task one can specify runtime attributes to customize the environment for the call.
For example:
task jes_task {
command {
echo "Hello JES!"
}
runtime {
docker: "ubuntu:latest"
memory: "4G"
cpu: "3"
zones: "us-central1-c us-central1-a"
disks: "/mnt/mnt1 3 SSD, /mnt/mnt2 500 HDD"
}
}
workflow jes_workflow {
call jes_task
}
This table lists the currently available runtime attributes for cromwell:
Runtime Attribute | LOCAL | JES | SGE |
---|---|---|---|
continueOnReturnCode | x | x | x |
cpu | x | ||
disks | x | ||
zones | x | ||
docker | x | x | x |
failOnStderr | x | x | x |
memory | x | ||
preemptible | x | ||
bootDiskSizeGb | x |
Runtime attribute values are interpreted as expressions. This means that it is possible to express the value of a runtime attribute as a function of one of the task's inputs. For example:
task runtime_test {
String ubuntu_tag
Int memory_gb
command {
./my_binary
}
runtime {
docker: "ubuntu:" + ubuntu_tag
memory: memory_gb + "GB"
}
}
Default values for runtime attributes can be specified via workflow options. For example, consider this WDL file:
task first {
command { ... }
}
task second {
command {...}
runtime {
docker: "my_docker_image"
}
}
workflow w {
call first
call second
}
And this set of workflow options:
{
"defaultRuntimeOptions": {
"docker": "ubuntu:latest",
"zones": "us-central1-a us-central1-b"
}
}
Then these values for docker
and zones
will be used for any task that does not explicitly override them in the WDL file. So the effective runtime for task first
is:
{
"docker": "ubuntu:latest",
"zones": "us-central1-a us-central1-b"
}
And the effective runtime for task second
is:
{
"docker": "my_docker_image",
"zones": "us-central1-a us-central1-b"
}
Note how for task second, the WDL value for docker
is used instead of the default provided in the workflow options.
When each task finishes it returns a code. Normally, a non-zero return code indicates a failure. However you can override this behavior by specifying the continueOnReturnCode
attribute.
When set to false, any non-zero return code will be considered a failure. When set to true, all return codes will be considered successful.
runtime {
continueOnReturnCode: true
}
When set to an integer, or an array of integers, only those integers will be considered as successful return codes.
runtime {
continueOnReturnCode: 1
}
runtime {
continueOnReturnCode: [0, 1]
}
Defaults to "0".
Passed to JES: "The minimum number of cores to use."
runtime {
cpu: 2
}
Defaults to "1".
Passed to JES: "Disks to attach."
The disks are specified as a comma separated list of disks. Each disk is further separated as a space separated triplet of:
- Mount point (absolute path), or
local-disk
to reference the mount point where JES will localize files and the task's current working directory will be - Disk size in GB (ignored for disk type LOCAL)
- Disk type. One of: "LOCAL", "SSD", or "HDD" (documentation)
All tasks launched on JES must have a local-disk
. If one is not specified in the runtime section of the task, then a default of local-disk 10 SSD
will be used. The local-disk
will be mounted to /cromwell_root
.
The Disk type must be one of "LOCAL", "SSD", or "HDD". When set to "LOCAL", the size of the drive is automatically provisioned by Google so any size specified in WDL will be ignored. All disks are set to auto-delete after the job completes.
Example 1: Changing the Localization Disk
runtime {
disks: "local-disk 100 SSD"
}
Example 2: Mounting an Additional Two Disks
runtime {
disks: "/mnt/my_mnt 3 SSD, /mnt/my_mnt2 500 HDD"
}
In addition to working disks, JES allows specification of a boot disk size. This is the disk where the docker image itself is booted, not the working directory of your task on the VM. Its primary purpose is to ensure that larger docker images can fit on the boot disk.
runtime {
# Yikes, we have a big OS in this docker image! Allow 50GB to hold it:
bootDiskSizeGb: 50
}
Since no local-disk
entry is specified, Cromwell will automatically add local-disk 10 SSD
to this list.
The ordered list of zone preference (see Region and Zones documentation for specifics)
The zones are specified as a space separated list, with no commas.
runtime {
zones: "us-central1-a us-central1-b"
}
Defaults to "us-central1-a"
When specified, cromwell will run your task within the specified Docker image.
runtime {
docker: "ubuntu:latest"
}
This attribute is mandatory when submitting tasks to JES. When running on other backends, they default to not running the process within Docker.
Some programs write to the standard error stream when there is an error, but still return a zero exit code. Set failOnStderr
to true for these tasks, and it will be considered a failure if anything is written to the standard error stream.
runtime {
failOnStderr: true
}
Defaults to "false".
Passed to JES: "The minimum amount of RAM to use."
The memory size is specified as an amount and units of memory, for example "4 G".
runtime {
memory: "4G"
}
Defaults to "2G".
Passed to JES: "If applicable, preemptible machines may be used for the run."
Take an Int as a value that indicates the maximum number of times Cromwell should request a preemptible machine for this task before defaulting back to a non-preemptible one. eg. With a value of 1, Cromwell will request a preemptible VM, if the VM is preempted, the task will be retried with a non-preemptible VM. Note: If specified, this attribute overrides workflow options.
runtime {
preemptible: 1
}
Defaults to "false".
Cromwell accepts two Java Properties or Environment Variables for controlling logging:
LOG_MODE
- Accepts eitherpretty
orstandard
(defaultpretty
). Instandard
mode, logs will be written without ANSI escape code coloring, with a layout more appropriate for server logs, versuspretty
that is easier to read for a single workflow run.LOG_LEVEL
- Level at which to log (defaultinfo
).
Additionally, a directory may be set for writing per workflow logs. By default, the per workflow logs will be erased once the workflow completes.
// In application.conf or specified via system properties
workflow-options {
workflow-log-dir: "cromwell-workflow-logs"
workflow-log-temporary: true
}
The usual case of generating the temporary per workflow logs is to copy them to a remote directory, while deleting the local copy to preserve local disk space. To specify the remote directory to copy the logs to use the separate workflow option workflow_log_dir
.
When running a workflow from the command line or REST API, one may specify a JSON file that toggles various options for running the workflow. From the command line, the workflow options is passed in as the third positional parameter to the 'run' subcommand. From the REST API, it's an optional part in the multi-part POST request. See the respective sections for more details.
Example workflow options file:
{
"jes_gcs_root": "gs://my-bucket/workflows",
"google_project": "my_google_project",
"refresh_token": "1/Fjf8gfJr5fdfNf9dk26fdn23FDm4x"
}
Valid keys and their meanings:
- Global (use with any backend)
- write_to_cache - Accepts values
true
orfalse
. Iffalse
, the completed calls from this workflow will not be added to the cache. See the Call Caching section for more details. - read_from_cache - Accepts values
true
orfalse
. Iffalse
, Cromwell will not search the cache when invoking a call (i.e. every call will be executed unconditionally). See the Call Caching section for more details. - workflow_log_dir - Specifies a path where per-workflow logs will be written. If this is not specified, per-workflow logs will not be copied out of the Cromwell workflow log temporary directory/path before they are deleted.
- outputs_path - Specifies a path where final workflow outputs will be written. If this is not specified, workflow outputs will not be copied out of the Cromwell workflow execution directory/path.
- call_logs_dir - Specifies a path where final call logs will be written. If this is not specified, call logs will not be copied out of the Cromwell workflow execution directory/path.
- defaultRuntimeOptions - A JSON object where the keys are runtime attributes and the values are defaults that will be used through the workflow invocation. Individual tasks can choose to override these values. See the runtime attributes section for more information.
- workflowFailureMode - What happens after a task fails. Choose from:
- ContinueWhilePossible - continues to start and process calls in the workflow, as long as they did not depend on the failing call
- NoNewCalls - no new calls are started but existing calls are allowed to finish
- The default is
NoNewCalls
but this can be changed using theworkflow-options.workflow-failure-mode
configuration option.
- backend - Override the default backend specified in the Cromwell configuration for this workflow only.
- write_to_cache - Accepts values
- JES Backend Only
- jes_gcs_root - (JES backend only) Specifies where outputs of the workflow will be written. Expects this to be a GCS URL (e.g.
gs://my-bucket/workflows
). If this is not set, this defaults to the value withinbackend.jes.baseExecutionBucket
in the configuration. - google_project - (JES backend only) Specifies which google project to execute this workflow.
- refresh_token - (JES backend only) Only used if
localizeWithRefreshToken
is specified in the configuration file. See the Data Localization section below for more details. - auth_bucket - (JES backend only) defaults to the the value in jes_gcs_root. This should represent a GCS URL that only Cromwell can write to. The Cromwell account is determined by the
google.authScheme
(and the correspondinggoogle.userAuth
andgoogle.serviceAuth
) - monitoring_script - (JES backend only) Specifies a GCS URL to a script that will be invoked prior to the WDL command being run. For example, if the value for monitoring_script is "gs://bucket/script.sh", it will be invoked as
./script.sh > monitoring.log &
. The valuemonitoring.log
file will be automatically de-localized. - preemptible - (JES backend only) Specifies the maximum number of times a call should be executed with a preemptible VM. This option can be overridden by runtime attributes. By default the value is 0, which means no Preemptible VM will be used.
- jes_gcs_root - (JES backend only) Specifies where outputs of the workflow will be written. Expects this to be a GCS URL (e.g.
Call Caching allows Cromwell to detect when a job has been run in the past so it doesn't have to re-compute results. Cromwell searches the cache of previously run jobs for a one that has the exact same command and exact same inputs. If a previously run job is found in the cache, Cromwell will copy the results of the previous job instead of re-running it.
Cromwell's call cache is maintained in its database. For best mileage with call caching, configure Cromwell to point to a MySQL database instead of the default in-memory database. This way any invocation of Cromwell (either with run
or server
subcommands) will be able to utilize results from all calls that are in that database.
Call Caching is disabled by default. Once enabled, Cromwell will search the call cache for every call
statement invocation, assuming read_from_cache
is enabled (see below):
- If there was no cache hit, the
call
will be executed as normal. Once finished it will add itself to the cache, assumingread_from_cache
is enabled (see below) - If there was a cache hit, outputs are copied from the cached job to the new job's output directory
Note: If call caching is enabled, be careful not to change the contents of the output directory for any previously run job. Doing so might cause cache hits in Cromwell to copy over modified data and Cromwell currently does not check that the contents of the output directory changed.
To enable Call Caching, add the following to your Cromwell configuration:
call-caching {
enabled = true
lookup-docker-hash = false
}
When call-caching.enabled=true
, Cromwell will add completed calls to the cache as well as do a cache lookup before running any call.
When call-caching.lookup-docker-hash=true
, Cromwell will contact external services like DockerHub or Google Container Registry to resolve Docker floating container identifiers like ubuntu:latest
into immutable hashes while computing the hash of the call invocation. If this option is false, then the raw value specified in the WDL file for the Docker image is the value that will be used.
Cromwell also accepts two workflow option related to call caching:
- If call caching is enabled, but one wishes to run a workflow but not add any of the calls into the call cache when they finish, the
write_to_cache
option can be set tofalse
. This value defaults totrue
. - If call caching is enabled, but you don't want to check the cache for any
call
invocations, set the optionread_from_cache
tofalse
. This value also defaults totrue
Note: If call caching is disabled, the to workflow options
read_from_cache
andwrite_to_cache
will be ignored and the options will be treated as though they were 'false'.
The server
subcommand on the executable JAR will start an HTTP server which can accept WDL files to run as well as check status and output of existing workflows.
The following sub-sections define which HTTP Requests the web server can accept and what they will return. Example HTTP requests are given in HTTPie and cURL
All web server requests include an API version in the url. The current version is v1
.
This endpoint accepts a POST request with a multipart/form-data
encoded body. The form fields that may be included are:
wdlSource
- Required Contains the WDL file to submit for execution.workflowInputs
- Optional JSON file containing the inputs. A skeleton file can be generated from wdltool using the "inputs" subcommand.workflowOptions
- Optional JSON file containing options for this workflow execution. See the run CLI sub-command for some more information about this.
cURL:
$ curl -v "localhost:8000/api/workflows/v1" -F wdlSource=@src/main/resources/3step.wdl -F workflowInputs=@test.json
HTTPie:
$ http --print=hbHB --form POST localhost:8000/api/workflows/v1 wdlSource=@src/main/resources/3step.wdl workflowInputs@inputs.json
Request:
POST /api/workflows/v1 HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 730
Content-Type: multipart/form-data; boundary=64128d499e9e4616adea7d281f695dca
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--64128d499e9e4616adea7d281f695dca
Content-Disposition: form-data; name="wdlSource"
task ps {
command {
ps
}
output {
File procs = stdout()
}
}
task cgrep {
command {
grep '${pattern}' ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
task wc {
command {
cat ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
workflow three_step {
call ps
call cgrep {
input: in_file=ps.procs
}
call wc {
input: in_file=ps.procs
}
}
--64128d499e9e4616adea7d281f695dca
Content-Disposition: form-data; name="workflowInputs"; filename="inputs.json"
{
"three_step.cgrep.pattern": "..."
}
--64128d499e9e4616adea7d281f695dca--
Response:
HTTP/1.1 201 Created
Content-Length: 74
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:28 GMT
Server: spray-can/1.3.3
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096e",
"status": "Submitted"
}
To specify workflow options as well:
cURL:
$ curl -v "localhost:8000/api/workflows/v1" -F wdlSource=@wdl/jes0.wdl -F workflowInputs=@wdl/jes0.json -F workflowOptions=@options.json
HTTPie:
http --print=HBhb --form POST http://localhost:8000/api/workflows/v1 wdlSource=@wdl/jes0.wdl workflowInputs@wdl/jes0.json workflowOptions@options.json
Request (some parts truncated for brevity):
POST /api/workflows/v1 HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 1472
Content-Type: multipart/form-data; boundary=f3fd038395644de596c460257626edd7
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--f3fd038395644de596c460257626edd7
Content-Disposition: form-data; name="wdlSource"
task x { ... }
task y { ... }
task z { ... }
workflow myworkflow {
call x
call y
call z {
input: example="gs://my-bucket/cromwell-executions/myworkflow/example.txt", int=3000
}
}
--f3fd038395644de596c460257626edd7
Content-Disposition: form-data; name="workflowInputs"; filename="jes0.json"
{
"myworkflow.x.x": "100"
}
--f3fd038395644de596c460257626edd7
Content-Disposition: form-data; name="workflowOptions"; filename="options.json"
{
"jes_gcs_root": "gs://myworkflow-dev/workflows"
}
--f3fd038395644de596c460257626edd7--
This endpoint accepts a POST request with a multipart/form-data
encoded body. The form fields that may be included are:
wdlSource
- Required Contains the WDL file to submit for execution.workflowInputs
- Required JSON file containing the inputs in a JSON array. A skeleton file for a single inputs json element can be generated from wdltool using the "inputs" subcommand. The orderded endpoint responses will contain one workflow submission response for each input, respectively.workflowOptions
- Optional JSON file containing options for this workflow execution. See the run CLI sub-command for some more information about this.
cURL:
$ curl -v "localhost:8000/api/workflows/v1/batch" -F wdlSource=@src/main/resources/3step.wdl -F workflowInputs=@test_array.json
HTTPie:
$ http --print=hbHB --form POST localhost:8000/api/workflows/v1/batch wdlSource=@src/main/resources/3step.wdl workflowInputs@inputs_array.json
Request:
POST /api/workflows/v1/batch HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 750
Content-Type: multipart/form-data; boundary=64128d499e9e4616adea7d281f695dcb
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--64128d499e9e4616adea7d281f695dcb
Content-Disposition: form-data; name="wdlSource"
task ps {
command {
ps
}
output {
File procs = stdout()
}
}
task cgrep {
command {
grep '${pattern}' ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
task wc {
command {
cat ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
workflow three_step {
call ps
call cgrep {
input: in_file=ps.procs
}
call wc {
input: in_file=ps.procs
}
}
--64128d499e9e4616adea7d281f695dcb
Content-Disposition: form-data; name="workflowInputs"; filename="inputs_array.json"
[
{
"three_step.cgrep.pattern": "..."
},
{
"three_step.cgrep.pattern": "..."
}
]
--64128d499e9e4616adea7d281f695dcb--
Response:
HTTP/1.1 201 Created
Content-Length: 96
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:28 GMT
Server: spray-can/1.3.3
[
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096e",
"status": "Submitted"
},
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096f",
"status": "Submitted"
}
]
To specify workflow options as well:
cURL:
$ curl -v "localhost:8000/api/workflows/v1/batch" -F wdlSource=@wdl/jes0.wdl -F workflowInputs=@wdl/jes0_array.json -F workflowOptions=@options.json
HTTPie:
http --print=HBhb --form POST http://localhost:8000/api/workflows/v1/batch wdlSource=@wdl/jes0.wdl workflowInputs@wdl/jes0_array.json workflowOptions@options.json
Request (some parts truncated for brevity):
POST /api/workflows/v1/batch HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 1492
Content-Type: multipart/form-data; boundary=f3fd038395644de596c460257626edd8
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--f3fd038395644de596c460257626edd8
Content-Disposition: form-data; name="wdlSource"
task x { ... }
task y { ... }
task z { ... }
workflow myworkflow {
call x
call y
call z {
input: example="gs://my-bucket/cromwell-executions/myworkflow/example.txt", int=3000
}
}
--f3fd038395644de596c460257626edd8
Content-Disposition: form-data; name="workflowInputs"; filename="jes0_array.json"
[
{
"myworkflow.x.x": "100"
}, {
"myworkflow.x.x": "101"
}
]
--f3fd038395644de596c460257626edd8
Content-Disposition: form-data; name="workflowOptions"; filename="options.json"
{
"jes_gcs_root": "gs://myworkflow-dev/workflows"
}
--f3fd038395644de596c460257626edd8--
This endpoint allows WDL to be validated in the context of a running Cromwell server and a given inputs file.
Validation includes checking that the WDL is syntactically correct, that the runtime attributes are correct and (if supplied) that the inputs file satisfies the WDL file's input requirements.
wdlSource
- Required Contains the WDL file to submit for execution.workflowInputs
- Optional JSON file containing the inputs.workflowOptions
- Optional JSON file containing the workflow options. The options file is validated structurally and can supply default runtime attributes for tasks in the source file.
cURL:
$ curl -v "localhost:8000/api/workflows/v1/validate" -F wdlSource=@src/main/resources/3step.wdl -F workflowInputs=@test.json
HTTPie:
$ http --print=hbHB --form POST localhost:8000/api/workflows/v1/validate wdlSource=@src/main/resources/3step.wdl workflowInputs@inputs.json
Request:
POST /api/workflows/v1/validate HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 463
Content-Type: application/x-www-form-urlencoded; charset=utf-8
Host: localhost:8000
User-Agent: HTTPie/0.9.2
wdlSource=...wdlInputs=...
Response (successful validation):
HTTP/1.1 200 OK
Content-Length: 63
Content-Type: application/json; charset=UTF-8
Date: Thu, 21 Jan 2016 16:57:39 GMT
Server: spray-can/1.3.2
{
"message": "Validation succeeded.",
"status": "success"
}
Response (failed validation example):
HTTP/1.1 400 Bad Request
Content-Length: 159
Content-Type: application/json; charset=UTF-8
Date: Thu, 21 Jan 2016 16:56:32 GMT
Server: spray-can/1.3.2
{
"errors": [
"Missing required keys in runtime configuration for backend 'JES': docker"
],
"message": "RuntimeAttribute is not valid.",
"status": "fail"
}
This endpoint allows for querying workflows based on the following criteria:
name
id
status
start
(start datetime)end
(end datetime)page
(page of results)pagesize
(# or results per page)
Names, ids, and statuses can be given multiple times to include workflows with any of the specified names, ids, or statuses. When multiple names are specified, any workflow matching one of the names will be returned. The same is true for multiple ids or statuses. When different types of criteria are specified, for example names and statuses, the results must match both the one of the specified names and one of the statuses. Using page and pagesize will enable server side pagination.
Valid statuses are Submitted
, Running
, Aborting
, Aborted
, Failed
, and Succeeded
. start
and end
should
be in ISO8601 datetime format and start
cannot be after end
.
cURL:
$ curl "http://localhost:8000/api/workflows/v1/query?start=2015-11-01&end=2015-11-03&status=Failed&status=Succeeded&page=1&pagesize=10"
HTTPie:
$ http "http://localhost:8000/api/workflows/v1/query?start=2015-11-01&end=2015-11-03&status=Failed&status=Succeeded&page=1&pagesize=10"
Response:
HTTP/1.1 200 OK
Content-Length: 133
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:56 GMT
Server: spray-can/1.3.3
{
"results": [
{
"name": "w",
"id": "fdfa8482-e870-4528-b639-73514b0469b2",
"status": "Succeeded",
"end": "2015-11-01T07:45:52.000-05:00",
"start": "2015-11-01T07:38:57.000-05:00"
},
{
"name": "hello",
"id": "e69895b1-42ed-40e1-b42d-888532c49a0f",
"status": "Succeeded",
"end": "2015-11-01T07:45:30.000-05:00",
"start": "2015-11-01T07:38:58.000-05:00"
},
{
"name": "crasher",
"id": "ed44cce4-d21b-4c42-b76d-9d145e4d3607",
"status": "Failed",
"end": "2015-11-01T07:45:44.000-05:00",
"start": "2015-11-01T07:38:59.000-05:00"
}
],
"page": 1,
"pageSize": 10,
"totalRecords": 3
}
This endpoint allows for querying workflows based on the same criteria as GET /api/workflows/:version/query.
Instead of specifying query parameters in the URL, the parameters
must be sent via the POST body. The request content type must be
application/json
. The json should be a list of objects. Each json
object should contain a different criterion.
cURL:
$ curl -X POST --header "Content-Type: application/json" -d "[{\"start\": \"2015-11-01\"}, {\"end\": \"2015-11-03\"}, {\"status\": \"Failed\"}, {\"status\": \"Succeeded\"}, {\"page\": 1}, {\"pagesize\": 10}]" "http://localhost:8000/api/workflows/v1/query"
HTTPie:
$ echo "[{\"start\": \"2015-11-01\"}, {\"end\": \"2015-11-03\"}, {\"status\": \"Failed\"}, {\"status\": \"Succeeded\"}, {\"page\": 1}, {\"pagesize\": 10}]" | http "http://localhost:8000/api/workflows/v1/query"
Response:
HTTP/1.1 200 OK
Content-Length: 133
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:56 GMT
Server: spray-can/1.3.3
{
"results": [
{
"name": "w",
"id": "fdfa8482-e870-4528-b639-73514b0469b2",
"status": "Succeeded",
"end": "2015-11-01T07:45:52.000-05:00",
"start": "2015-11-01T07:38:57.000-05:00"
},
{
"name": "hello",
"id": "e69895b1-42ed-40e1-b42d-888532c49a0f",
"status": "Succeeded",
"end": "2015-11-01T07:45:30.000-05:00",
"start": "2015-11-01T07:38:58.000-05:00"
},
{
"name": "crasher",
"id": "ed44cce4-d21b-4c42-b76d-9d145e4d3607",
"status": "Failed",
"end": "2015-11-01T07:45:44.000-05:00",
"start": "2015-11-01T07:38:59.000-05:00"
}
],
"page": 1,
"pageSize": 10,
"totalRecords": 3
}
cURL:
$ curl http://localhost:8000/api/workflows/v1/69d1d92f-3895-4a7b-880a-82535e9a096e/status
HTTPie:
$ http http://localhost:8000/api/workflows/v1/69d1d92f-3895-4a7b-880a-82535e9a096e/status
Response:
HTTP/1.1 200 OK
Content-Length: 74
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:56 GMT
Server: spray-can/1.3.3
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096e",
"status": "Succeeded"
}
cURL:
$ curl http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/outputs
HTTPie:
$ http http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/outputs
Response:
HTTP/1.1 200 OK
Content-Length: 241
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"id": "e442e52a-9de1-47f0-8b4f-e6e565008cf1",
"outputs": {
"three_step.cgrep.count": 8,
"three_step.ps.procs": "/var/folders/kg/c7vgxnn902lc3qvc2z2g81s89xhzdz/T/stdout2814345504446060277.tmp",
"three_step.wc.count": 8
}
}
This endpoint is meant to be used in a web browser. It will show a Gantt Chart of a particular workflow. The bars in the chart represent start and end times for individual task invocations.
cURL:
$ curl http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/outputs/three_step.wc
HTTPie:
$ http http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/outputs/three_step.wc
Response:
HTTP/1.1 200 OK
Content-Length: 241
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"id": "e442e52a-9de1-47f0-8b4f-e6e565008cf1",
"outputs": {
"three_step.wc.count": 8
}
}
This will return paths to the standard out and standard error files that were generated during the execution of a particular fully-qualified name for a call.
A call has one or more standard out and standard error logs, depending on if the call was scattered or not. In the latter case, one log is provided for each instance of the call that has been run.
cURL:
$ curl http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/logs/three_step.wc
HTTPie:
$ http http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/logs/three_step.wc
Response:
HTTP/1.1 200 OK
Content-Length: 379
Content-Type: application/json; charset=UTF-8
Date: Mon, 03 Aug 2015 17:11:28 GMT
Server: spray-can/1.3.3
{
"id": "b3e45584-9450-4e73-9523-fc3ccf749848",
"logs": {
"three_step.wc": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/three_step.wc/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/three_step.wc/stdout6128485235785447571.tmp"
}
]
}
}
In the case that the call is inside a scatter
block, the output for this API will contain a list of stdout/stderr files, one for each shard. Consider this example:
task add_one {
Int n
command {
python -c "print(${n}+1)"
}
output {
Int incremented = read_int(stdout())
}
}
workflow test {
Array[Int] list = [1,2,3,4]
scatter (x in list) {
call add_one {input: n=x}
}
}
Running this workflow then issuing this API call would return:
HTTP/1.1 200 OK
Content-Length: 1256
Content-Type: application/json; charset=UTF-8
Date: Fri, 04 Sep 2015 12:22:45 GMT
Server: spray-can/1.3.3
{
"id": "cbdefb0f-29ae-475b-a42c-90403f8ff9f8",
"logs": {
"test.add_one": [
{
"stderr": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-0/stderr",
"stdout": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-0/stdout"
},
{
"stderr": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-1/stderr",
"stdout": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-1/stdout"
},
{
"stderr": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-2/stderr",
"stdout": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-2/stdout"
},
{
"stderr": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-3/stderr",
"stdout": "/home/user/test/cbdefb0f-29ae-475b-a42c-90403f8ff9f8/call-add_one/shard-3/stdout"
}
]
}
}
This returns a similar format as the /api/workflows/:version/:id/logs/:call
endpoint, except that it includes the logs for ALL calls in a workflow and not just one specific call.
cURL:
$ curl http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/logs
HTTPie:
$ http http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/logs
Response:
HTTP/1.1 200 OK
Content-Length: 379
Content-Type: application/json; charset=UTF-8
Date: Mon, 03 Aug 2015 17:11:28 GMT
Server: spray-can/1.3.3
{
"id": "b3e45584-9450-4e73-9523-fc3ccf749848",
"logs": {
"call.ps": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-ps/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-ps/stdout6128485235785447571.tmp"
}
],
"call.cgrep": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-cgrep/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-cgrep/stdout6128485235785447571.tmp"
}
],
"call.wc": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-wc/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-wc/stdout6128485235785447571.tmp"
}
]
}
}
This endpoint returns a superset of the data from #get-workflowsversionidlogs in essentially the same format (i.e. shards are accounted for by an array of maps, in the same order as the shards). In addition to shards, every attempt that was made for this call will have its own object as well, in the same order as the attempts. Workflow metadata includes submission, start, and end datetimes, as well as status, inputs and outputs. Call-level metadata includes inputs, outputs, start and end datetime, backend-specific job id, return code, stdout and stderr. Date formats are ISO with milliseconds.
cURL:
$ curl http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/metadata
HTTPie:
$ http http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/metadata
Response:
HTTP/1.1 200 OK
Server spray-can/1.3.3 is not blacklisted
Server: spray-can/1.3.3
Date: Thu, 01 Oct 2015 22:18:07 GMT
Content-Type: application/json; charset=UTF-8
Content-Length: 8286
{
"workflowName": "sc_test",
"calls": {
"sc_test.do_prepare": [
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/stdout",
"shardIndex": -1,
"outputs": {
"split_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_aa",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ab",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ac",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ad"
]
},
"inputs": {
"input_file": "/home/jdoe/cromwell/11.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:55.000-05:00"
}
],
"sc_test.do_scatter": [
{
"executionStatus": "Preempted",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/stdout",
"shardIndex": 0,
"outputs": {},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
},
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/stdout",
"shardIndex": 0,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"returnCode": 0,
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/stderr",
"attempt": 2,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
},
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/stdout",
"shardIndex": 1,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
},
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-2/stdout",
"shardIndex": 2,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-2/output.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-2/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
},
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-3/stdout",
"shardIndex": 3,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-3/output.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-3/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
}
],
"sc_test.do_gather": [
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_gather/stdout",
"shardIndex": -1,
"outputs": {
"sum": 12
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-3/output.txt"
]
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:57.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_gather/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
}
]
},
"outputs": {
"sc_test.do_gather.sum": 12,
"sc_test.do_prepare.split_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_aa",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ab",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ac",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ad"
],
"sc_test.do_scatter.count_file": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-3/output.txt"
]
},
"id": "8e592ed8-ebe5-4be0-8dcb-4073a41fe180",
"inputs": {
"sc_test.do_prepare.input_file": "/home/jdoe/cromwell/11.txt"
},
"submission": "2016-02-04T13:47:55.000-05:00",
"status": "Succeeded",
"end": "2016-02-04T13:47:57.000-05:00",
"start": "2016-02-04T13:47:55.000-05:00"
}
The call
and workflow
may optionally contain failures shaped like this:
"failures": [
{
"failure": "The failure message",
"timestamp": "2016-02-25T10:49:02.066-05:00"
}
]
cURL:
$ curl -X POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/abort
HTTPie:
$ http POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/abort
Response:
HTTP/1.1 200 OK
Content-Length: 241
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"id": "e442e52a-9de1-47f0-8b4f-e6e565008cf1",
"status": "Aborted"
}
This endpoint allows for reconfiguration of call cache result reuse settings for all calls within a workflow.
Accepted parameters are:
allow
Mandatory boolean value, specifies whether call cache result reuse is allowed for all calls in the specified workflow.
cURL:
$ curl -X POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/call-caching?allow=false
HTTPie:
$ http POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/call-caching?allow=false
Response:
HTTP/1.1 200 OK
Content-Length: 17
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"updateCount": 3
}
This endpoint allows for reconfiguration of call cache result reuse settings for a single call within a workflow.
Accepted parameters are:
allow
Mandatory boolean value, specifies whether call cache result reuse is allowed for the specified call in the specified workflow.
For scattered calls, individual calls within the scatter can be targeted by appending a dot and the zero-based shard index.
e.g. scatter_workflow.A.0
would target the zeroth shard of a scattered A
call. If a shard index is not supplied for
a scattered call, all shards are targeted for update.
cURL:
$ curl -X POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/call-caching/three_step.wc?allow=false
HTTPie:
$ http POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/call-caching/three_step.wc?allow=false
Response:
HTTP/1.1 200 OK
Content-Length: 17
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"updateCount": 1
}
Requests that Cromwell can't process return a failure in the form of a JSON response respecting the following JSON schema:
{
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Error response schema",
"type": "object",
"properties": {
"status": {
"enum": [ "fail", "error"]
},
"message": {
"type": "string"
},
"errors": {
"type": "array",
"minItems": 1,
"items": { "type": "string" },
"uniqueItems": true
}
},
"required": ["status", "message"]
}
The status
field can take two values:
"fail" means that the request was invalid and/or data validation failed. "fail" status is most likely returned with a 4xx HTTP Status code. e.g.
{
"status": "fail",
"message": "Workflow input processing failed.",
"errors": [
"Required workflow input 'helloworld.input' not specified."
]
}
"error" means that an error occurred while processing the request. "error" status is most likely returned with a 5xx HTTP Status code. e.g.
{
"status": "error",
"message": "Connection to the database failed."
}
The message
field contains a short description of the error.
The errors
field is optional and may contain additional information about why the request failed.
$ pip install mdtoc
$ mdtoc --check-links README.md
Essentially run sbt doc
then commit the generated code into the gh-pages
branch on this repository
$ sbt doc
$ git co gh-pages
$ mv target/scala-2.11/api scaladoc
$ git add scaladoc
$ git commit -m "API Docs"
$ git push origin gh-pages