Command Line Interface#
The Raster Vision command line utility, rastervision
, is installed with a pip install
of
rastervision
, which is installed by default in the Docker Images.
It has a main command, with some top level options, and several subcommands.
> rastervision --help
Usage: python -m rastervision.pipeline.cli [OPTIONS] COMMAND [ARGS]...
The main click command.
Sets the profile, verbosity, and tmp_dir in RVConfig.
Options:
-p, --profile TEXT Sets the configuration profile name to use.
-v, --verbose Increment the verbosity level.
--tmpdir TEXT Root of temporary directories to use.
--help Show this message and exit.
Commands:
predict Use a model bundle to predict on new images.
predict_scene Use a model bundle to predict on a new scene.
run Run sequence of commands within pipeline(s).
run_command Run an individual command within a pipeline.
Subcommands#
run
#
Run is the main interface into running pipelines.
> rastervision run --help
Usage: rastervision run [OPTIONS] RUNNER CFG_MODULE [COMMANDS]...
Run COMMANDS within pipelines in CFG_MODULE using RUNNER.
RUNNER: name of the Runner to use
CFG_MODULE: the module with `get_configs` function that returns
PipelineConfigs. This can either be a Python module path or a local path
to a .py file.
COMMANDS: space separated sequence of commands to run within pipeline. The
order in which to run them is based on the Pipeline.commands attribute. If
this is omitted, all commands will be run.
Options:
-a, --arg KEY VALUE Arguments to pass to get_config function
-s, --splits INTEGER Number of processes to run in parallel for splittable
commands
--pipeline-run-name TEXT The name for this run of the pipeline.
--help Show this message and exit.
Some specific parameters to call out:
--splits
#
Use -s N
or --splits N
, where N
is the number of splits to create, to parallelize commands that can be split into parallelizable chunks. See Running Commands in Parallel for more information.
run_command
#
The run_command
is used to run a specific command from a serialized PipelineConfig
JSON file.
This is likely only interesting to people writing custom runners.
> rastervision run_command --help
Usage: python -m rastervision.pipeline.cli run_command [OPTIONS] CFG_JSON_URI
COMMAND
Run a single COMMAND using a serialized PipelineConfig in CFG_JSON_URI.
Options:
--split-ind INTEGER The process index of a split command
--num-splits INTEGER The number of processes to use for running splittable
commands
--runner TEXT Name of runner to use
--help Show this message and exit.
predict
#
Use predict
to make predictions on new imagery given a model bundle.
> rastervision predict --help
Usage: python -m rastervision.pipeline.cli predict [OPTIONS] MODEL_BUNDLE
IMAGE_URI LABEL_URI
Make predictions on the images at IMAGE_URI using MODEL_BUNDLE and store the
prediction output at LABEL_URI.
Options:
-a, --update-stats Run an analysis on this individual image, as opposed
to using any analysis like statistics that exist in
the prediction package
--channel-order LIST List of indices comprising channel_order. Example: 2 1
0
--scene-group TEXT Name of the scene group whose stats will be used by
the StatsTransformer. Requires the stats for this
scene group to be present inside the bundle.
--help Show this message and exit.
predict_scene
#
Similar to predict
but allows greater control by allowing the user to specify a full SceneConfig
and PredictOptions
.
> rastervision predict_scene --help
Usage: python -m rastervision.pipeline.cli predict_scene [OPTIONS]
MODEL_BUNDLE_URI
SCENE_CONFIG_URI
Use a model-bundle to make predictions on a scene.
MODEL_BUNDLE_URI URI to a serialized Raster Vision model-bundle.
SCENE_CONFIG_URI URI to a serialized Raster Vision SceneConfig.
Options:
--predict_options_uri TEXT Optional URI to serialized Raster Vision
PredictOptions config.
--help Show this message and exit.