Fuzzfile Syntax Guide

Overview

Workflows are the fundamental unit of work in Fuzzball, orchestrating container execution, data movement, and resource allocation across compute clusters. They are described by Fuzzfiles — YAML 1.2 documents with a well-defined structure and syntax. A workflow definition consists of several top-level sections that work together to define the complete execution lifecycle describing how data flows into and out of the workflow, what compute operations to perform, what resources those operations require, and how jobs depend on each other. The top level sections are:


version: v1
files:
defaults:
annotations:
volumes:
jobs:
services:

Each section serves a specific purpose:

version: Specifies the workflow syntax version (currently always v1) [required]
files: Defines inline file content that can be referenced in volumes and jobs [optional]
defaults: Sets default configurations (mounts, environment, policy, resources) for jobs [optional]
annotations: Provides workflow-level metadata for scheduling and placement [optional]
volumes: Defines storage volumes with data ingress (importing) and egress (exporting) capabilities [optional]
jobs: Specifies the computational work to be performed, organized as a directed acyclic graph (DAG)
services: Defines long-running container services that support jobs or provide external endpoints

A workflow needs to have at least 1 job or service.

Top-Level Sections

version

Required. Denotes the workflow syntax version. Currently the only allowed value is v1.

Structure:


version: v1

Example:


version: v1

files

Optional. Defines inline file contents that can be referenced in volume ingress and jobs using a file://<arbitrary_name> URI scheme. This is useful for embedding small configuration files, scripts, or data directly in the workflow definition rather than storing them externally. Note that some content may have to be base64 encoded to ensure that the resulting yaml file is valid.

Structure:


files:
  <file-name>: |
    <file-contents>
  <another-file-name>: |
    <more-contents>

Fields:

<file-name>: An arbitrary name to identify this inline file. This name is used in file:// URIs to reference the content. Can be any valid YAML key.
<file-contents>: The actual content of the file. Use the | literal block scalar for multi-line content.

Example:


files:
  config.ini: |
    [settings]
    debug=true
    timeout=30
  data.csv: |
    id,name,value
    1,alpha,42
    2,beta,3.14

Usage:

Inline files can be referenced in two ways:

In volume ingress - to copy the content into a volume:


volumes:
  my:
    reference: volume://user/ephemeral
    ingress:
      - source:
          uri: file://config.ini          # References the inline file
        destination:
          uri: file://config/app.ini      # Destination in the volume

In job files - to bind mount the content directly into a container:


jobs:
  my-job:
    image:
      uri: docker://alpine:latest
    files:
      /etc/app/config.ini: file://config.ini   # Mounted at this path in container
    script: |
      #!/bin/sh
      cat /etc/app/config.ini

defaults

Optional. Sets default configurations that are applied to all jobs in the workflow. Individual jobs can override these defaults by specifying their own values. This is useful for reducing repetition when multiple jobs share common settings like volume mounts or environment variables.

Structure:


defaults:
  job:
    env:                    # Optional
      - <VAR>=<value>
    mounts:                 # Optional
      <volume-name>:
        location: <path>
    policy:                 # Optional
      timeout:
        execute: <duration>
      retry:
        attempts: <number>
    resource:               # Optional
      cpu:
        cores: <number>
        affinity: <CORE|SOCKET|NUMA>
        sockets: <number>
        threads: <boolean>
      memory:
        size: <size>
        by-core: <boolean>
      devices:
        <device-type>: <count>
      exclusive: <boolean>
      annotations:
        <key>: <value>

Fields:

job: Container for job-level defaults
- env (optional): List of environment variables in KEY=VALUE format that will be set in all job containers
- mounts (optional): Map of volume names to mount specifications. Each mount defines:
  - location: Absolute path in the container where the volume will be mounted
- policy (optional): Default execution policies for all jobs
  - timeout (optional): Time limits for job execution
    - execute: Maximum duration (e.g., 2h, 30m, 1h30m)
  - retry (optional): Retry behavior on failure
    - attempts: Number of retry attempts before permanent failure
- resource (optional): Default hardware resource requirements
  - cpu (required if resource is specified): CPU requirements
    - cores: Number of CPU cores
    - affinity: Binding strategy - CORE, SOCKET, or NUMA
    - sockets (optional): Number of CPU sockets
    - threads (optional): Whether to expose hardware threads (hyperthreading)
  - memory (required if resource is specified): Memory requirements
    - size: Amount of RAM (e.g., 4GB, 512MB)
    - by-core (optional): If true, size is per CPU core
  - devices (optional): Map of device types to counts (e.g., nvidia.com/gpu: 1)
  - exclusive (optional): If true, job gets exclusive access to the node
  - annotations (optional): Custom key-value pairs for advanced scheduling

Example:


defaults:
  job:
    env:
      - SCRATCH=/scratch
      - LOG_LEVEL=info
    mounts:
      scratch:
        location: /scratch
    policy:
      timeout:
        execute: 1h
      retry:
        attempts: 2
    resource:
      cpu:
        cores: 2
        affinity: CORE
        threads: false
      memory:
        size: 4GB
        by-core: false

In this example, all jobs will automatically:

Have /scratch and LOG_LEVEL environment variables set
Mount the scratch at /scratch
Timeout after 1 hour and retry up to 2 times on failure
Request 2 CPU cores and 4GB of memory

Individual jobs can override any of these defaults by specifying their own values. Default and job specified environment variables are merged with job variables taking precedence over defaults.

annotations

Optional. Provides workflow-level metadata as key-value pairs. Currently used by the scheduler to influence job placement decisions, such as placing jobs in specific topology zones or node pools.

Structure:


annotations:
  <key>: <value>
  <another-key>: <another-value>

Fields:

<key>: String key identifying the annotation
<value>: String value for the annotation

Example:


annotations:
  scheduling.fuzzball.io/node-pool: gpu-nodes
  organization: research-team

Common Use Cases:

Topology placement: Control which availability zone or region jobs run in
Node pool selection: Target specific sets of compute nodes
Organizational metadata: Tag workflows for tracking, billing, or auditing

Note that annotation behavior is determined by cluster configuration and may vary between deployments and provisioners.

volumes

Optional. Defines storage volumes that jobs can access. Volumes provide persistent or ephemeral storage for workflow data and support data ingress (importing data at workflow start) and egress (exporting data at workflow end).

Structure:


volumes:
  <volume-name>:
    reference: <volume-uri>    # Required
    ingress:                   # Optional
      - source:
          uri: <source-uri>
          secret: <secret-ref>    # Optional
        destination:
          uri: <dest-uri>
        policy:                # Optional
          timeout:
            execute: <duration>
          retry:
            attempts: <number>
    egress:                    # Optional
      - source:
          uri: <source-uri>
        destination:
          uri: <dest-uri>
          secret: <secret-ref>    # Optional
        policy:                # Optional
          timeout:
            execute: <duration>
          retry:
            attempts: <number>

Fields:

<volume-name>: Arbitrary name for the volume, used to reference it in job mounts sections (e.g., scratch, data)
reference (required): Volume URI from a storage class. Common formats:
- volume://user/ephemeral - Ephemeral volume that exists only for the workflow's lifetime
- volume://user/persistent/<name> - Persistent volume that survives workflow completion. Note that <name> will only have an effect if your storage class is configured to use user-configured volume names.
ingress (optional): List of files to import into the volume at workflow start
- source (required): Where to fetch the data from
  - uri: Source location. Supported schemes: s3://, http://, https://, file:// (references inline files from the files section)
  - secret (optional): Reference to credentials for accessing the source (format: secret://<scope>/<name>)
- destination (required): Where to place the data in the volume
  - uri: Destination path in the volume (format: file://<path>). When jobs mount this volume, the path will be relative to the mount point
- policy (optional): Execution policies for the transfer
  - timeout (optional): Time limit for the transfer
    - execute: Maximum duration (e.g., 5m, 1h)
  - retry (optional): Retry behavior on failure
    - attempts: Number of retry attempts
egress (optional): List of files to export from the volume at workflow end. Structure is similar to ingress, but source is the file in the volume and destination is the external storage location

Example:


volumes:
  scratch:
    reference: volume://user/ephemeral
    ingress:
      - source:
          uri: s3://my-bucket/input-data.tar.gz
          secret: secret://account/AWS_CREDENTIALS
        destination:
          uri: file://inputs/data.tar.gz
        policy:
          timeout:
            execute: 10m
          retry:
            attempts: 3
      - source:
          uri: file://config.ini    # References inline file
        destination:
          uri: file://config/app.ini
    egress:
      - source:
          uri: file://results/output.tar.gz
        destination:
          uri: s3://my-bucket/results/output.tar.gz
          secret: secret://account/AWS_CREDENTIALS
  data:
    reference: volume://user/persistent/research-data

In this example:

scratch is ephemeral, downloads data from S3 at start, and uploads results at end
data is persistent and survives workflow completion with no data transfers

Path Resolution Example:

If a job mounts scratch at /scratch, a file ingressed to file://inputs/data.tar.gz will be available in the job container at /scratch/inputs/data.tar.gz.

jobs

Optional (but at least one job or service is required). Defines the computational work to be performed as a directed acyclic graph (DAG) of compute steps. Jobs can run scientific software, data processing scripts, or any containerized application. Jobs are the fundamental execution unit in a workflow.

Structure:


jobs:
  <job-name>:
    image:                            # Required
      uri: <image-uri>
      secret: <secret-ref>            # Optional
      decryption-secret: <secret-ref> # Optional
    command: [<arg1>, <arg2>, ...]    # One of command or script required (mutually exclusive with script)
    script: |                         # One of command or script required (mutually exclusive with command)
      <script-content>
    args: [<arg1>, <arg2>, ...]       # Optional
    env:                              # Optional
      - <VAR>=<value>
    cwd: <working-directory>          # Optional
    mounts:                           # Optional
      <volume-name>:
        location: <mount-path>
    files:                            # Optional
      <container-path>: <file-uri>
    resource:                         # Optional
      cpu:                            # Required if resource specified
        cores: <number>
        affinity: <CORE|SOCKET|NUMA>
        sockets: <number>             # Optional
        threads: <boolean>            # Optional
      memory:                         # Required if resource specified
        size: <size>
        by-core: <boolean>            # Optional
      devices:                        # Optional
        <device-type>: <count>
      exclusive: <boolean>            # Optional
      annotations:                    # Optional
        <key>: <value>
    policy:                           # Optional
      timeout:
        execute: <duration>
      retry:
        attempts: <number>
    requires: [<job1>, <job2>, ...]   # Optional (deprecated, use depends-on)
    depends-on:                       # Optional
      - name: <job-name>
        status: <RUNNING|FINISHED>
        description: <text>           # Optional
    multinode:                        # Optional (mutually exclusive with task-array, network)
      nodes: <number>
      implementation: <ompi|openmpi|mpich|gasnet>
      procs-per-node: <number>        # Optional
    network:                          # Optional (mutually exclusive with multinode, task-array)
      isolated: <boolean>             # Required (must be true)
      expose-tcp: [<number>, <number>, ...] # Optional
      expose-udp: [<number>, <number>, ...] # Optional
    task-array:                       # Optional (mutually exclusive with multinode, network)
      start: <number>
      end: <number>
      concurrency: <number>           # Optional

Fields:

<job-name>: Arbitrary name identifying this job (e.g., preprocess-data, train-model). Must be a valid DNS subdomain and must be unique within a workflow. This name appears in fuzzball workflow status and is used in dependency specifications
image (required): Container image specification
- uri: Image location. Supported schemes: docker:// for OCI containers, oras:// for SIF images (e.g., oras://depot.ciq.com/fuzzball/fuzzball-applications/curl.sif:latest)
- secret (optional): Credentials for private registries (format: secret://<scope>/<name>)
- decryption-secret (optional): Secret to decrypt encrypted SIF images
command (required, mutually exclusive with script): List of arguments for the container entrypoint (e.g., [python3, script.py, --input, /data])
script (required, mutually exclusive with command): Multi-line shell script to execute. Must start with a shebang line (e.g., #!/bin/bash)
args (optional): Additional arguments passed to command or script
env (optional): List of environment variables in KEY=VALUE format
cwd (optional): Working directory for the job. Must be an absolute path. Defaults to the image's working directory or /
mounts (optional): Map of volume names (from the volumes section) to mount specifications
- location: Absolute path in the container where the volume will be mounted
files (optional): Map of container paths to inline file URIs. Bind mounts inline files directly into the container (e.g., /etc/config.ini: file://my-config)
resource (optional): Hardware resource requirements for scheduling
- cpu (required if resource specified): CPU requirements
  - cores: Number of CPU cores (must be > 0)
  - affinity: Binding strategy - CORE (any cores), SOCKET (same socket), or NUMA (same NUMA domain). Defaults to CORE
  - sockets (optional): Number of physical CPU sockets
  - threads (optional): Whether to expose hardware threads (hyperthreading)
- memory (required if resource specified): Memory requirements
  - size: Amount of RAM with units (e.g., 4GB, 512MB, 2GiB)
  - by-core (optional): If true, size is per CPU core, i.e. total size is cores * size
- devices (optional): Map of device types to counts (e.g., nvidia.com/gpu: 2)
- exclusive (optional): If true, job gets exclusive node access
- annotations (optional): Custom key-value pairs for advanced node selection (e.g., CPU architecture, GPU model)
policy (optional): Execution policies
- timeout (optional): Time limits
  - execute: Maximum job duration (e.g., 2h, 30m)
- retry (optional): Retry behavior
  - attempts: Number of retries on failure
requires (optional, deprecated): List of job names that must complete before this job starts. Use depends-on instead
depends-on (optional): List of concrete dependencies with status requirements. Note that depending on a job array to finish will wait for all tasks to finish.
- name: Job or service name to depend on
- status: Required status - FINISHED (job/service completed) or RUNNING (job/service is running)
- description (optional): Human-readable explanation of the dependency
multinode (optional, mutually exclusive with task-array/network): Multi-node parallel execution
- nodes: Number of nodes to allocate
- implementation: MPI/communication implementation - ompi, openmpi, mpich, or gasnet
- procs-per-node (optional): Processes per node. Defaults to number of allocated CPUs
network (optional, mutually exclusive with multinode and task-array): If present, job is run inside its own (isolated) network namespace.
- isolated: Must be true when the network section is specified.
- expose-tcp (optional): List of container TCP ports to expose
- expose-udp (optional): List of container UDP ports to expose
task-array (optional, mutually exclusive with multinode and network): Embarrassingly parallel execution
- start: Starting task ID (inclusive, must be > 0)
- end: Ending task ID (inclusive, must be >= start)
- concurrency (optional): Maximum parallel tasks (max 200). Each task receives $FB_TASK_ID

Example:


jobs:
  preprocess:
    image:
      uri: docker://python:3.11
    script: |
      #!/bin/sh
      python3 preprocess.py --input /data/raw/${FB_TASK_ID} --output /data/processed/${FB_TASK_ID}
    env:
      - PYTHONUNBUFFERED=1
    mounts:
      data:
        location: /data
    resource:
      cpu:
        cores: 4
        affinity: NUMA
      memory:
        size: 8GB
    policy:
      timeout:
        execute: 30m
      retry:
        attempts: 2
    task-array:
      start: 1
      end: 1000
      concurrency: 100

  train-multinode:
    image:
      uri: docker://nvcr.io/nvidia/pytorch:24.01-py3
      secret: secret://user/NGC_API_KEY
    script: |
      #!/bin/bash
      python -m torch.distributed.run train.py --input /data/processed
    depends-on:
      - name: preprocess
        status: FINISHED
    resource:
      cpu:
        cores: 32
        affinity: SOCKET
      memory:
        size: 128GB
      devices:
        nvidia.com/gpu: 4
      annotations:
        nvidia.com/gpu.model:: NVIDIA L40
    multinode:
      nodes: 4
      implementation: openmpi
      procs-per-node: 4

This example demonstrates:

preprocess: Basic job task array with resource requests and retry policy
train-multinode: Multi-node MPI job with GPUs and explicit dependencies

services

Optional (but at least one job or service is required). Defines long-running container services that support jobs or provide external endpoints. Unlike jobs which complete and exit, services run continuously for the workflow duration (or until dependent jobs finish). Services are useful for databases, web servers, message queues, interactive computing (e.g. jupyter or Rstudio), AI inference servers, or any persistent service that jobs need to access.

Structure:


services:
  <service-name>:
    image:                         # Required
      uri: <image-uri>
      secret: <secret-ref>         # Optional
      decryption-secret: <secret-ref> # Optional
    command: [<arg1>, <arg2>, ...] # One of command or script required (mutually exclusive with script)
    script: |                      # One of command or script required (mutually exclusive with command)
      <script-content>
    args: [<arg1>, <arg2>, ...]    # Optional
    env:                           # Optional
      - <VAR>=<value>
    cwd: <working-directory>       # Optional
    mounts:                        # Optional
      <volume-name>:
        location: <mount-path>
    files:                         # Optional
      <container-path>: <file-uri>
    resource:                      # Optional
      cpu:
        cores: <number>
        affinity: <CORE|SOCKET|NUMA>
        sockets: <number>          # Optional
        threads: <boolean>         # Optional
      memory:
        size: <size>
        by-core: <boolean>         # Optional
      devices:                     # Optional
        <device-type>: <count>
      exclusive: <boolean>         # Optional
      annotations:                 # Optional
        <key>: <value>
    requires: [<job1>, <svc1>, ...] # Optional (deprecated, use depends-on)
    depends-on:                     # Optional
      - name: <job-or-service-name>
        status: <RUNNING|FINISHED>
        description: <text>        # Optional
    multinode:                     # Optional
      nodes: <number>
      implementation: <ompi|openmpi|mpich|gasnet>
      procs-per-node: <number>     # Optional
    network:                       # Optional (mutually exclusive with multinode)
      ports:
        - name: <port-name>
          port: <port-number>
          protocol: <tcp|udp>      # Optional
      endpoints:                   # Optional
        - name: <endpoint-name>
          port-name: <port-name>   # References port name above
          protocol: <http|https|grpc|grpcs|tcp|tls>
          type: <subdomain|path>
          scope: <endpoint-scope>  # One of: user, group, organization, public
    persist: <boolean>             # Optional
    readiness-probe:                # Optional
      exec:                        # One of: exec, http-get, tcp-socket, grpc
        command: [<arg1>, <arg2>]
      http-get:
        path: <path>
        port: <port-number>
        scheme: <HTTP|HTTPS>       # Optional
        http-headers:               # Optional
          - name: <header-name>
            value: <header-value>
      tcp-socket:
        port: <port-number>
      grpc:
        port: <port-number>
        service: <service-name>    # Optional
      initial-delay-seconds: <seconds>  # Optional
      period-seconds: <seconds>        # Optional
      timeout-seconds: <seconds>       # Optional
      success-threshold: <number>      # Optional
      failure-threshold: <number>      # Optional

Fields:

Services share many fields with jobs (image, command, script, args, env, cwd, mounts, files, resource, depends-on, multinode). See the jobs section for details on these common fields. Service-specific fields are:

<service-name>: Arbitrary name identifying this service. Must be a valid DNS subdomain and must be unique within a workflow.
network (optional): Network configuration for service exposure. If present and the list of exposed ports is not empty, service is run inside its own (isolated) network namespace.
- ports: List of ports the service listens on
  - name: Identifier for this port (used in endpoints)
  - port: Port number (1-65535)
  - protocol (optional): tcp or udp. Defaults to tcp
- endpoints (optional): List of external endpoints to create
  - name: Endpoint identifier
  - port-name: References a port name from the ports list
  - protocol: Protocol - http, https, grpc, grpcs, tcp, or tls
  - type: Endpoint style - subdomain (creates <name>.<workflow-id>.<account>.fuzzball) or path (creates /endpoints/<account>/<workflow-id>/<name>)
  - scope: Determines who can access the endpoint. One of user (only the workflow creator), group (anyone in the same account), organization (anyone in the same organization), public (anyone without authentication). Defaults to group if not specified.
persist (optional): If true, service continues running even after all dependent jobs finish and continues until the workflow is cancelled. If false (default), service stops when no jobs/services depend on it.
readiness-probe (optional): Kubernetes-style health check to determine when service is ready. Service status transitions from STARTED to RUNNING only after probe succeeds
- exec: Run a command in the container. Success if exit code is 0
  - command: Command to execute
- http-get: HTTP GET request. Success if status code is 200-399
  - path: HTTP path
  - port: Port number
  - scheme (optional): HTTP or HTTPS
  - http-headers (optional): Custom HTTP headers
- tcp-socket: TCP connection attempt. Success if connection establishes
  - port: Port number
- grpc: gRPC health check. Success per gRPC health checking protocol
  - port: Port number
  - service (optional): gRPC service name
- initial-delay-seconds (optional): Delay before first probe
- period-seconds (optional): Frequency of probes
- timeout-seconds (optional): Probe timeout
- success-threshold (optional): Consecutive successes needed
- failure-threshold (optional): Consecutive failures before marking unhealthy

Example:


services:
  postgres:
    image:
      uri: docker://postgres:16
    env:
      - POSTGRES_PASSWORD=secret
      - POSTGRES_DB=myapp
    mounts:
      db:
        location: /var/lib/postgresql/data
    resource:
      cpu:
        cores: 4
      memory:
        size: 8GB
    network:
      ports:
        - name: postgres
          port: 5432
          protocol: tcp
    readiness-probe:
      tcp-socket:
        port: 5432
      initial-delay-seconds: 5
      period-seconds: 10
    persist: true

  api-server:
    image:
      uri: docker://mycompany/api:v1.2.3
      secret: secret://account/REGISTRY_CREDS
    env:
      - DATABASE_URL=postgresql://postgres:5432/myapp
    depends-on:
      - name: postgres
        status: RUNNING
        description: "API needs database connection"
    resource:
      cpu:
        cores: 2
      memory:
        size: 4GB
    network:
      ports:
        - name: http
          port: 8080
      endpoints:
        - name: api
          port-name: http
          protocol: https
          type: subdomain
          scope: group
    readiness-probe:
      http-get:
        path: /health
        port: 8080
      initial-delay-seconds: 10
      period-seconds: 5
      failure-threshold: 3

jobs:
  data-processor:
    image:
      uri: docker://mycompany/processor:latest
    script: |
      #!/bin/sh
      python process.py --api-url http://api-server:8080
    depends-on:
      - name: api-server
        status: RUNNING
    resource:
      cpu:
        cores: 8
      memory:
        size: 16GB

This example shows:

postgres: Persistent database service with readiness probe
api-server: REST API depending on postgres, exposed via HTTPS subdomain endpoint at account scope
data-processor: Job that depends on api-server being running before it starts

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