CLI Commands

CellMap Flow provides several CLI interfaces to run different model configurations and modes of execution. This section outlines the supported entrypoints, command syntax, and examples.

Available Entry points

The following CLI entrypoints are available:

cellmap_flow — the main interface for executing a single model using a script, Dacapo run, or Bioimage model.
cellmap_flow_multiple — supports executing multiple models in one command, all on the same dataset.
cellmap_flow_yaml — supports loading model configuration from a structured YAML file.

Note

All CLI tools support –queue and –charge_group options for specifying compute resources.

Basic Syntax

Main CLI Entrypoint (cellmap_flow)

cellmap_flow <subcommand> [OPTIONS]

Supported subcommands:

dacapo — run using a Dacapo training run.
script — run using a Python script model definition.
bioimage — run using a Bioimage.IO model.
cellmap-model — run using a structured CellMap model config folder.

Examples:

# Using a Dacapo run
cellmap_flow dacapo -r run_name -i 60 -d /path/to/data -q gpu_h100 -P cellmap

# Using a Python script
cellmap_flow script -s /path/to/script.py -d /path/to/data -q gpu_h100 -P cellmap

# Using a Bioimage.IO model
cellmap_flow bioimage -m /path/to/model.bioimage.io -d /path/to/data -q gpu_h100 -P cellmap

# Using a CellMap model folder
cellmap_flow cellmap-model -f /path/to/model_dir -n model_name -d /path/to/data -q gpu_h100 -P cellmap

Chaining Multiple Models (cellmap_flow_multiple)

This CLI allows running multiple models sequentially on the same dataset. Each subcommand must be prefixed with –.

Example:

cellmap_flow_multiple \
    --data-path /nrs/path/zarr/volume.zarr \
    --project cellmap \
    --queue gpu_h100 \
    --dacapo -r mito_run -i 40000 \
    --script -s /path/to/script.py \
    --cellmap-model -f /path/to/model_dir -n mito_model

YAML-Based Configuration (cellmap_flow_yaml)

Run multiple models defined in a YAML file with structured metadata. This allows batch processing and version-controlled configuration.

YAML Example:

data_path: "/nrs/cellmap/data/jrc_mus-salivary-1/.../fibsem-uint8"
charge_group: "cellmap"
queue: "gpu_h100"

models:
- type: cellmap-model
   config_folder: "/groups/cellmap/cellmap/zouinkhim/salivary/train/binary/mito_16"
   name: "b_mito_16"
   scale: "s1"

- type: cellmap-model
   config_folder: "/groups/cellmap/cellmap/zouinkhim/salivary/train/binary/lyso_16"
   name: "b_lyso_16"
   scale: "s1"

- type: dacapo
   run_name: "nuclei_run"
   iteration: 36000
   name: "nuclei_model"

- type: script
   script_path: "/groups/cellmap/scripts/mito_script.py"
   name: "mito_script_model"
   scale: "s2"

- type: bioimage
   model_path: "/groups/cellmap/models/mito.bioimage.io"
   name: "bio_mito"

To execute:

cellmap_flow_yaml /path/to/config.yaml

Advanced Options

All CLI variants support the following flags (unless overridden):

--data-path/-d: Path to the input dataset (required).
--queue/-q: Compute queue to submit the job (default: gpu_h100).
--project/-P: Charge group for billing (required).
--name/-n: Custom model name (optional).
--scale/-r: Input scale (optional for structured models).
--edge_length_to_process/-e: For 2D models, desired chunk edge size (bioimage only).

Server Check Utility

Validate a script-based model before submitting:

cellmap_flow script-server-check -s /path/to/script.py -d /path/to/data

This will initialize the inference server and simulate a 2x2x2 chunk to confirm configuration validity.