Run FireWorks workflows in Google Cloud
What is it?
FireWorks is an open-source workflow management system, that is, it drives interdependent computing tasks. Among the many workflow systems available, FireWorks is exceptionally straightforward, lightweight, and adaptable. It's well tested and supported. The only shared services it needs are a MongoDB server (acting as the workflow "LaunchPad") and a file store.
Borealis lets you spin up as many temporary worker machines as you want in the Google Cloud Platform to run your workflow. That means pay-per-use and no contention between workflows.
How does Borealis support workflows on Google Cloud Platform?
TL;DR: Spin up worker machines when you need them, deploy your task code to the workers in Docker Images, and store the data in Google Cloud Storage instead of NFS.
Figure 1. Blue hexagons represent Google Cloud Platform services. The LaunchPad is shown in a blue hexagon because it's implemented as a MongoDB server that you can run on Compute Engine.
Worker VMs: As a cloud computing platform, Google Compute Engine (GCE) has a vast number of machines available. You can spin up lots of GCE "instances" (also called Virtual Machines or VMs) to run your workflow, change your code, re-run some tasks, then let the workers time out and shut down. Google will charge you based on usage and there's no resource contention with your teammates.
Borealis provides the
ComputeEngine class and its command line wrapper
to create, tweak, and delete groups of worker VMs.
Borealis provides the
fireworker Python script to run as the top level program
of each worker. It calls FireWorks'
You can run these Fireworkers on and off GCE as long as they can connect to your MongoDB server and to the data store for their input and output files.
Docker: You need to deploy your payload task code to those GCE VMs. It might be Python source code and its runtime environment, e.g. Python 3.8, Python pip packages, Linux apt packages, compiled Cython code, data files, and environment variable settings. A GCE VM starts up from a GCE Disk Image which could have all that preinstalled (with or without the Python source code) but it'd be hard to keep it up to date and hard to keep track of how to reproduce it.
This is what Docker Images are designed for. You maintain a
instructions to build the Docker Image, then use the Google Cloud Build
service to build the Image and store it in the Google Container Registry.
Borealis provides the
DockerTask Firetask to run a task in Docker. It
pulls a named Docker Image, starts up a Docker Container, runs a given shell
command in that Container, and shuts down the Container. Running in a Container
also isolates the task's runtime environment and side effects from the
Fireworker and other tasks.
Google Cloud Storage: Although you can set up an NFS shared file service for the workers' files, Google Cloud Storage (GCS) is the native storage service. GCS costs literally 1/10th as much as NFS service and it scales up better. GCS lets you archive your files in yet lower cost tiers intended for infrequent access. Pretty much all of Google's cloud services revolve around GCS, e.g., Pub/Sub can trigger an action on a particular upload to GCS.
But Cloud Storage is not a file system. It's an object store with a light
weight protocol to fetch/store/list whole files, called "blobs." It does not
support simultaneous writers. Instead, the last "store" of a blob wins. Blob
pathnames can contain
/ characters but GCS doesn't have actual directory objects,
so e.g. there's no way to atomically rename a directory.
DockerTask supports Cloud Storage by fetching the task's input files from GCS
and storing its output files to GCS.
DockerTask logs the Container's stdout and stderr, and
fireworker sets up Python logging to write to Google's
StackDriver cloud logging service so you can watch your workers running
in real time.
Projects: With Google Cloud Platform, you set up a project for your team to use. All services, VMs, data, and access controls are scoped by the project.
How to run a workflow
After doing one-time setup, the steps to run a workflow are:
Build a Docker container Image containing your payload tasks to run in the workflow. The
gcloud builds submitcommand will upload your code and a
Dockerfile, then trigger a Google Cloud Build server server to build the Docker Image and store it in the Google Container Registry.
See Building Your Docker Image for how to build the Docker Image, starting with writing the
Build your workflow and upload it to MongoDB. You can do this manually by writing a
.yamlfile and running the
lpadcommand line tool to upload it, or automate it by implementing a workflow builder program which calls FireWorks APIs to construct and upload a
The workflow will run instances of the
DockerTaskFiretask. Of course it can run other Firetasks as well; they just won't get deployment, storage, logging, and timeout features from
If you need to open a secure ssh tunnel to the MongoDB server running in a Google Compute Engine VM, use the
Start one or more
fireworkerprocesses to run the workflow tasks.
There are three ways to do this:
- Run the
fireworkerprocess locally (this is handy for debugging) by running the
- Launch a group of fireworkers on Compute Engine VMs (this is handy for
getting a lot of work done in parallel) by running the Python script
- Automate the launching of a group of fireworkers on GCE by making your
workflow builder code call on the Borealis
ComputeEngineclass after it builds and uploads the workflow.
- Run the
While it runs and afterwards, you can:
watch the main GCP dashboard
Tip: Add VM load charts to this page.
watch the Compute Engine VM Instances console to see your list of workers
watch the Google Cloud Logs Viewer of your running workers
(The INFO level will include the big picture for running workers. The DEBUG level will also include the console output and other details for the firetasks. Filter to just your worker nodes with an advanced filter like
resource.labels.instance_id:"fireworker-USER-", inserting your $USER name.)
Workaround: If this command prints an error
TypeError: cannot pickle '_thread.lock' object
there's a compatibility problem with FireWorks on Python 3. As a workaround, run
lpad webgui -s
which starts the webgui server without the web browser. It prints the server URL such as http://127.0.0.1:5000. Then open a browser to that URL. (Just CMD-click the URL in iTerm.)
lpad get_fws lpad get_wflows lpad report
lpad rerun_fws -i <FW_IDS> lpad rerun_fws -s FIZZLED
lpad pause_fws -i <FW_IDS> lpad resume_fws -i <FW_IDS> lpad defuse_fws -i <FW_IDS> lpad reignite_fws -i <FW_IDS> lpad pause_wflows -i <FW_IDS> lpad defuse_wflows -i <FW_IDS> lpad rerun_fws -i <FW_IDS> lpad reignite_wflows -i <FW_IDS>
build a new Docker Image containing bug-fixes, then re-run Fireworks tasks.
If all the fireworkers have exited, run the
gcecommand to launch new fireworkers.
launch additional fireworkers:
gce --base <BASE> --count <COUNT> <NAME-PREFIX>
Set the BASE number argument or NAME-PREFIX argument so the new VM names won't conflict with your already-running VMs.
Archive or delete a workflow or reset the entire Launchpad database:
lpad archive_wflows -i <FW_IDS> lpad delete_wflows -i <FW_IDS> lpad reset
gsutil -mto transfer multiple files in parallel. (If it prints
TypeError: cannot pickle..., configure
gsutilto use Python 3.7 per the note in Install the development tools.)
More detail on the Borealis components
The Borealis components can be used separately. Together they enable running FireWorks workflows in Google Cloud.
ComputeEngine class and its command line wrapper
create, tweak, and delete a group of worker VMs.
After you generate a workflow, call FireWorks'
(or run FireWorks'
lpad add command line tool) to upload it to the
LaunchPad. Then call
ComputeEngine.create() (or the
gce command line)
to spin up a group of worker VMs to run that workflow and pass in the
parameters such as the LaunchPad db name and username
(via GCE metadata fields).
gce can also immediately delete a group of worker
VMs or ask them to quit cleanly between Firetasks, although we usually let
fireworkers shut down on their own after an idle timeout.
gce can also set GCE metadata fields on a group of
workers. This is used to implement the
After installing the
$ gce -h usage: gce [-h] [--delete | --set-metadata | --quit-soon] [-d] [-b BASE] [-c COUNT] [-f FAMILY] [-l LAUNCHPAD_FILENAME] [-m METADATA_KEY=VALUE,...] [-o OPTION_KEY=VALUE,...] NAME-PREFIX Create, delete, or set metadata on a group of Google Compute Engine VMs, e.g. workflow workers that start up from a disk image-family. (This code also has an API for direct use.) ...
Or to run it within the
borealis-fireworks source code repository:
$ python -m borealis.gce -h
fireworker Python script runs as as the top level program of each worker
fireworker reads the worker launch parameters and calls the FireWorks library
to "rapidfire" launch your FireWorks "rockets." It also handles server shutdown.
fireworker connects Python logging to Google Cloud's
StackDriver logging so you can watch all your worker machines in real time.
fireworker on GCE VMs, you'll need to create a GCE Disk Image that
contains Python, the borealis-fireworks pip, and such. See the instructions in
fireworker command can also run on your local computer for easier
debugging. For that, you'll need to install the
borealis-fireworks pip and set
up your computer to access the right Google Cloud Project.
$ fireworker -h usage: fireworker.py [-h] [-l LAUNCHPAD_FILENAME] [-s] [--development] Run as a FireWorks worker node, launching rockets rapidfire. Gets configuration settings from Google Compute Engine (GCE) metadata attributes when running on GCE, else from the Launchpad file (see the `-l` option), with fallbacks. The setup source files are "/Users/jerry/dev/borealis/borealis/setup/*" ...
DockerTask Firetask pulls a named Docker Image, starts up a Docker
Container, runs a given shell command in that Container, and stops the container.
imageto load up into a Container
commandto run in the Container
internal_prefixinput/output base pathname in the Container
storage_prefixinput/output base pathname in GCS
inputsinput files and directory trees
outputsoutput files and directory trees
timeoutfor stopping the shell command, in seconds
Docker always runs a shell command in the Container. If you want to run a
Firetask in the Container, include a little Python script to bridge the gap:
Take a Firetask name and a JSON dictionary as command line arguments,
instantiate the Firetask with those arguments, and call the Firetask's
DockerTask supports Google Cloud Storage (GCS) by fetching the task's input
files from GCS, mapping them into the Docker Container, running the task, and
storing its output files to GCS. This means you'll need to specify the input
and output paths as
(Your workflow builder code could use this path information to compute the
task-to-task interdependencies for FireWorks.)
Each path you specify in DockerTask's
denotes a directory tree of files iff it ends with a
When storing task output files,
DockerTask creates entries with names ending in
/ to act as directory placeholders that speed up tree-oriented traversal.
This means you can run
gcsfuse without using the
--implicit-dirs flag, resulting in mounted directories that run 10x faster.
DockerTask imposes a given timeout on the task running in the Docker
DockerTask logs the Container's stdout and stderr to a file and to Python
fireworker connects to StackDriver).
Also see Handy Links.
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