seamless-config

seamless-config is the configuration and infrastructure-selection layer for Seamless projects.

Seamless models work as pipelines of cacheable steps. seamless-config answers the question "where does this pipeline run, and against which storage?" — locally in-process, in local worker processes, or on a remote cluster — without touching the step code. It reads plain YAML files, resolves cluster topology, and wires the right remote backends (buffer store, database, jobserver, dask scheduler) into the Seamless runtime before the first transformation runs.

Installation

pip install seamless-config

Quick start

import seamless.config

seamless.config.init()          # reads seamless.yaml / seamless.profile.yaml
                                # from the caller's directory upward
# … build and run your workflow …

From the command line, seamless-init performs the same initialisation and verifies that the configured remote services are reachable:

seamless-init                   # default stage
seamless-init --stage prod      # named stage
seamless-init --stage prod:gpu  # named stage + substage

Configuration files

seamless-config discovers two optional YAML files in the work directory (and optionally its parents):

File Commit to VCS? Purpose
seamless.yaml Yes Project-wide, deterministic defaults (project name, stage name, inherit_from_parent)
seamless.profile.yaml No (add to .gitignore) Developer-specific overrides — cluster hostnames, experimental settings, local credentials

Both files use the same command language. When a file contains inherit_from_parent, the loader also reads the parent directory and prepends its commands, repeating until a directory without that flag is reached (or the filesystem root). Parent defaults always run before child overrides.

Command language

Each file must be a YAML list. Every item is either a bare string command or a single-key mapping:

# seamless.yaml
- project: my-project
- inherit_from_parent
Command Argument Effect
project string Sets the project name (used as the storage path component)
subproject string Sets an optional sub-path inside the project
cluster string Selects the active cluster by name
execution process / spawn / remote Sets the execution mode
queue string Selects a named queue on the current cluster
remote null / daskserver / jobserver Pins the remote backend when a cluster exposes both
persistent boolean Forces persistent storage on or off; defaults to true when a cluster is set
clusters mapping Defines cluster objects inline (runs before other commands)
inherit_from_parent — Also reads commands from the parent directory, prepended
stage <name> list of commands Runs the nested commands only when the current stage matches <name>

If no execution command is encountered, the loader defaults to remote when a cluster is selected and process otherwise.

See COMMAND_LANGUAGE.md for the full specification.

Stage blocks

Use a stage <name>: key to activate commands only in a specific stage:

# seamless.profile.yaml
- clusters:
    local:
      tunnel: false
      type: local
      frontends:
        - hostname: localhost
          hashserver:
            bufferdir: /data/buffers
            conda: hashserver
            network_interface: 127.0.0.1
            port_start: 55100
            port_end: 55199
          database:
            database_dir: /data/db
            conda: seamless-database
            network_interface: 127.0.0.1
            port_start: 55200
            port_end: 55299
          jobserver:
            conda: seamless-jobserver
            network_interface: 127.0.0.1
            port_start: 55300
            port_end: 55399

- cluster: local

- stage prod:
    - cluster: hpc-cluster
    - execution: remote
    - remote: daskserver

Cluster definitions

Clusters are defined in ~/.seamless/clusters.yaml and/or individual files under ~/.seamless/clusters/*.yaml. They can also be inlined in seamless.profile.yaml via the clusters command (useful for portable projects).

A cluster definition describes the topology of its frontend nodes and the services each one can host:

# ~/.seamless/clusters.yaml

mycluster:
  tunnel: true                # connect via SSH tunnel
  type: slurm                 # local | slurm | oar
  workers: 4                  # for 'spawn' / 'jobserver' mode
  frontends:
    - hostname: login.mycluster.example
      ssh_hostname: login.mycluster.example   # optional SSH override
      hashserver:
        bufferdir: /scratch/seamless/buffers
        conda: hashserver
        network_interface: 0.0.0.0
        port_start: 60100
        port_end: 60199
      database:
        database_dir: /scratch/seamless/db
        conda: seamless-database
        network_interface: 0.0.0.0
        port_start: 60200
        port_end: 60299
      daskserver:
        network_interface: 0.0.0.0
        port_start: 60300
        port_end: 60399

  default_queue: default
  queues:
    default:
      conda: seamless-dask
      walltime: "01:00:00"
      cores: 16
      memory: 32000MB
      tmpdir: /tmp
      maximum_jobs: 20
      unknown_task_duration: 1m
      target_duration: 10m
      lifetime_stagger: 4m

    highmem:
      TEMPLATE: default         # inherit all fields from 'default', then override
      cores: 4
      memory: 128000MB

Frontend services

Each frontend entry can expose any subset of:

Service Role
hashserver Stores and serves content-addressed buffers (the raw bytes of cell values)
database Stores transformation results (maps input checksum → result checksum, backed by SQLite)
jobserver HTTP job dispatch — accepts serialised transformations and returns results
daskserver Dask-backed HPC scheduler — submits jobs to SLURM/OAR via dask-jobqueue

When both jobserver and daskserver are present on the same cluster, remote: jobserver or remote: daskserver must be specified explicitly in seamless.profile.yaml.

Queue templates

A queue entry with a TEMPLATE key inherits all fields from the named queue (which must be defined earlier in the same cluster), then overrides only the explicitly provided fields:

queues:
  base:
    conda: seamless-dask
    walltime: "02:00:00"
    cores: 8
    memory: 16000MB
    maximum_jobs: 10
    tmpdir: /tmp
    unknown_task_duration: 1m
    target_duration: 10m

  gpu:
    TEMPLATE: base
    memory: 32000MB
    job_extra_directives: ["--gres=gpu:1"]
    dask_resources: {GPU: 1}

Execution modes

Mode execution: value Description
In-process process Transformations run in the client Python process. Default when no cluster is defined.
Local workers spawn Transformations are dispatched to a local worker pool (uses workers from the cluster definition).
Remote (jobserver) remote + remote: jobserver Lightweight HTTP jobserver with a fixed worker pool. Typically used as a test setup or for simple local-cluster scenarios.
Remote (daskserver) remote + remote: daskserver Delegates to a Dask cluster with persistent storage. Any Dask Cluster subclass is supported; current cluster definitions cover SLURM and OAR via dask-jobqueue.
Pure Dask remote + remote: daskserver + persistent: false Dask execution without Seamless persistence (no hashserver/database). For batch jobs that don't need incremental caching.

The three remote backends (jobserver, daskserver, pure Dask) are mutually exclusive — a cluster frontend must expose exactly one, or you must pin the choice with remote: jobserver or remote: daskserver in seamless.profile.yaml.

remote_http_launcher handles two scheduler-placement topologies automatically: when hostname is set it SSHes into the configured frontend and runs the Dask wrapper as a daemon there; when hostname is absent it runs the wrapper as a local daemon on the client. This is independent of cluster type: a cluster with type: local still uses distributed.LocalCluster, but that LocalCluster may live either on the client or on a remote frontend reached over SSH. The configuration schema currently covers local, slurm, and oar; cloud provider support would require extending the cluster definition vocabulary, not the launcher or wrapper.

Persistence

When a cluster is selected and persistent: true (the default), seamless-config activates:

  • seamless_remote.buffer_remote — writes/reads buffers via the cluster's hashserver
  • seamless_remote.database_remote — checks and records transformation results via the cluster's database

In execution: remote mode it also activates the chosen job delegation backend (jobserver_remote or daskserver_remote).

Stages and substages

A stage is an independent execution and storage context. Each stage gets its own subdirectory under the project path:

<bufferdir>/<project>[/<subproject>][/STAGE-<stage>]
<database_dir>/<project>[/<subproject>][/STAGE-<stage>]

Stages are useful when the same project has multiple phases — e.g. build, test, prod — that must not share cached results.

A substage further subdivides the job-dispatch scope (one jobserver/daskserver per substage) without splitting storage. Substages are useful when different substages within the same stage need different hardware (CPU vs GPU queues).

Python API

import seamless.config

# Simple initialization (no named stage)
seamless.config.init()

# Named stage (re-evaluates config with 'stage prod:' blocks active)
seamless.config.set_stage("prod")

# Named stage + substage
seamless.config.set_stage("prod", "gpu")

# Change substage without changing stage
seamless.config.set_substage("cpu")

# Override the directory used for config file lookup
seamless.config.set_workdir("/path/to/project")

init() is a no-op if already initialised. set_stage() deactivates any previously active remote clients before re-configuring.

Forwarding remote clients to worker processes

When a job runs inside the cluster, it may need to connect back to the same remote services. collect_remote_clients / set_remote_clients serialise and restore the active client configuration.

Note: this API is already used by the daskserver. There is no need to do this from user code.

# On the client, before submitting a job:
clients = seamless_config.collect_remote_clients("mycluster")
# Pass 'clients' to the worker via job parameters or environment variable

# Inside the worker (or set SEAMLESS_REMOTE_CLIENTS=<json> in the environment):
seamless_config.set_remote_clients(clients, in_remote=True)

set_remote_clients must be called before init(). Worker bootstrap code can also call set_remote_clients_from_env() to pick up the JSON from the SEAMLESS_REMOTE_CLIENTS environment variable automatically.

seamless-init CLI

seamless-init is a convenience script that calls seamless_config.init() (or set_stage), then calls ensure_initialized() on each active remote backend to verify that the servers are reachable before the workflow starts.

usage: seamless-init [--stage STAGE[:SUBSTAGE]]

It exits immediately (success) when the SEAMLESS_REMOTE_CLIENTS environment variable is present, so that worker processes that bootstrap themselves with set_remote_clients are not affected.

Tool launch configuration

seamless-config ships an internal tools.yaml that describes how to construct the launch parameters for each server type (hashserver, database, jobserver, daskserver, pure_daskserver). These are consumed by remote-http-launcher, which starts the servers on the cluster frontend (via SSH tunnel if needed) and returns a live port.

The configure_hashserver, configure_database, configure_jobserver, configure_daskserver, and configure_pure_daskserver functions in seamless_config.tools assemble the final launch dict from the cluster definition and the current project/stage context. These functions are called internally by seamless-config and by launcher scripts in other packages; direct use is only needed when writing custom launch tooling.

Relation to the Seamless ecosystem

seamless-core          ← the computation engine (cells, transformers, caching)
    ↑
seamless-config        ← this package: reads YAML, wires backends, provides CLI
    ↑                 ↗ seamless-remote    (buffer + database + job clients)
    └── activates   ─── seamless-dask      (Dask scheduler integration)
                     ↘ seamless-jobserver  (HTTP jobserver)
                      ↘ seamless-database  (SQLite result store)
                       ↘ hashserver        (content-addressed buffer server)
                        ↘ remote-http-launcher (SSH + process launcher)

seamless-config is the only package in this stack that a Seamless workflow script typically imports directly (besides seamless itself). All other packages are implementation details activated by init() / set_stage().