Command line

CellMap Flow has one command, cellmap_flow, with a subcommand for each job. cellmap_flow <command> --help lists a command’s options, and --log-level before the command sets how much it logs (INFO by default):

cellmap_flow view -d /path/to/data.zarr
cellmap_flow --log-level DEBUG yaml config.yaml

Before 0.3.0 most of these were separate programs (cellmap_flow_yaml and the rest). Those names still work in 0.3.0; see Deprecated names.

Commands

Command	What it does
infer <type>	Start one model’s inference server, then open the viewer on its predictions and serve the dashboard. There is a subcommand for each model type (cellmap_flow models lists them) whose options are the type’s constructor arguments: cellmap_flow infer script -s model.py -d data.zarr/raw. Short flags go to the arguments in the order the constructor takes them, so an argument whose first letter an earlier one has has only its long flag (script’s --scale). -d, -q (queue), -P (billing project), --resample/--no-resample and --server-check are the command’s own. The data is read resampled to the model’s input voxel size when it has no level at that size; --no-resample reads a level as if it were at that size instead (Data at Another Voxel Size).
yaml CONFIG	The same for every model a YAML file lists, with its normalization and postprocessing (YAML Configuration). --validate-only checks the file; --list-types lists the model types.
add REF	Print the model entry for a path, URL, Hugging Face repo, zoo nickname or Cellpose model name, as YAML for a config’s models:; --run -d DATA serves it right away (An entry from what you have).
view -d DATA	Open a dataset in the viewer and serve the dashboard, where models are picked and submitted. -P bills them to a project; by default it is the launching job’s own (LSB_PROJECT_NAME).
dashboard	Serve the dashboard on its own, for a viewer already running (-n names the viewer the page embeds).
blockwise YAML...	Run a model over a whole volume, block by block, writing its predictions to disk (YAML Configuration). Several YAMLs run one after another; the command fails at the end if any left blocks unprocessed. --client runs one worker, which the run submits itself.
serve --model ENTRY -d DATA	Serve one model’s predictions. The launchers (infer, yaml, the dashboard) run it on a GPU node for you; see Starting a server.
finetune train, export-merged, build-corrections	The finetune tools (docs/finetuning.md in the repository). The dashboard’s finetune tab runs train for you; each takes the flags of python -m cellmap_flow.finetune.<module>, which --help lists.
models	List the model types and the arguments each takes.
plugins register FILE, unregister NAME, list	Manage plugins (Plugins).
doctor	Check the environment: what is installed, what is missing, and the command that fixes each gap. --core-only skips the finetune checks.

Starting a server

A launcher starts each model’s server as an LSF job (or a local process where there is no bsub) with:

$CELLMAP_FLOW_SERVER_COMMAND --model '<entry>' -d <data path>

The entry is the model as a YAML model entry, in JSON (ModelConfig.launch_entry), and the server builds the model from it as cellmap_flow yaml builds a YAML’s models:

cellmap_flow serve -d /path/to/data.zarr/raw \
  --model '{"type": "script", "script_path": "/path/to/model.py", "name": "mito"}'

A launcher always adds --resample or --no-resample (infer --no-resample, resample: false in a YAML, the dashboard’s box), so the server reads the data as its launcher was told to; see Data at Another Voxel Size.

CELLMAP_FLOW_SERVER_COMMAND is cellmap_flow serve by default. A deployment whose environment is not on the compute nodes’ PATH sets it: the pixi environment sets pixi run cellmap_flow serve (Installation). The value before 0.3.0, pixi run cellmap_flow_server, still works, since cellmap_flow_server takes --model too.

Plugins

Every command loads the registered plugins (~/.cellmap_flow/plugins/) when it starts, so a plugin’s model type has its infer and serve form and its normalizers and postprocessors are offered in the dashboard. Importing cellmap_flow does not load them: a script that uses a plugin’s classes calls cellmap_flow.plugins.load_plugins() first.

Deprecated names

These still work in 0.3.0 and go in the release after it. Each prints, on stderr, `<old>` is deprecated and goes in the release after 0.3.0; use `<new>`. and then does what <new> does.

Before 0.3.0	Since 0.3.0
cellmap_flow_yaml	cellmap_flow yaml
cellmap_flow_view	cellmap_flow view
cellmap_flow_blockwise	cellmap_flow blockwise
cellmap_flow_blockwise_multiple A.yaml B.yaml	cellmap_flow blockwise A.yaml B.yaml
cellmap_flow_app	cellmap_flow dashboard
cellmap_flow_server <type> ..., cellmap_flow_server --model	cellmap_flow serve --model (launchers before 0.3.0 still start servers with the per-type form)
cellmap_flow_server list-models, cellmap_flow list-models	cellmap_flow models
cellmap_flow <type> ...	cellmap_flow infer <type> .... The finetune type is the exception: cellmap_flow finetune is now the finetune tools, so a finetuned model is served with cellmap_flow infer finetune.
cellmap_flow run -m TYPE -c key=value	cellmap_flow infer TYPE --key value; run prints the exact command its arguments stand for.
cellmap_flow register, unregister, list-plugins	cellmap_flow plugins register, unregister, list

Reference