Running Pipelines#

Running pipelines in Raster Vision is done using the rastervision run command. This generates a pipeline configuration, serializes it, and then uses a runner to actually execute the commands, locally or remotely.

See also

Pipelines and Commands explains more of the details of how Pipelines are implemented.

Running locally#


A rastervision run local ... command will use the LocalRunner, which builds a Makefile based on the pipeline and executes it on the host machine. This will run multiple pipelines in parallel, as well as splittable commands in parallel, by spawning new processes for each command, where each process runs rastervision run_command ....


For debugging purposes, using rastervision run inprocess will run everything sequentially within a single process.

Running remotely#


Running rastervision run batch ... will submit a DAG (directed acyclic graph) of jobs to be run on AWS Batch, which will increase the instance count to meet the workload with low-cost spot instances, and terminate the instances when the queue of commands is finished. It can also run some commands on CPU instances (like chip), and others on GPU (like train), and will run multiple experiments in parallel, as well as splittable commands in parallel.

The AWSBatchRunner executes each command by submitting a job to Batch, which executes the rastervision run_command inside the Docker image configured in the Batch job definition. Commands that are dependent on an upstream command are submitted as a job after the upstream command’s job, with the jobId of the upstream command job as the parent jobId. This way Batch knows to wait to execute each command until all upstream commands are finished executing, and will fail the command if any upstream commands fail.

If you are running on AWS Batch or any other remote runner, you will not be able to use your local file system to store any of the data associated with an experiment.

Sample command:

rastervision run batch \
        "rastervision_pytorch_backend/rastervision/pytorch_backend/examples/semantic_segmentation/" \
        -a raw_uri "s3://raster-vision-raw-data/isprs-potsdam" \
        -a root_uri "s3://your-bucket/isprs_potsdam/"


To run on AWS Batch, you’ll need the proper setup. See Running on AWS Batch for instructions.


Running rastervision run sagemaker ... will submit a DAG (directed acyclic graph) of jobs represented as a SageMaker Pipeline to be run on AWS SageMaker.

Sample command:

        "rastervision_pytorch_backend/rastervision/pytorch_backend/examples/semantic_segmentation/" \
        -a raw_uri "s3://raster-vision-raw-data/isprs-potsdam" \
        -a root_uri "s3://your-bucket/isprs_potsdam/"


To run on AWS SageMaker, you’ll need the proper setup. See Running on AWS SageMaker for instructions.

Running Commands in Parallel#

Raster Vision can run certain commands in parallel, such as the CHIP and PREDICT commands. These commands are designated as split_commands in the corresponding Pipeline class. To run split commands in parallel, use the --split option to the run CLI command.

Splittable commands can be run in parallel, with each instance doing its share of the workload. For instance, using --splits 5 on a CHIP command over 50 training scenes and 25 validation scenes will result in 5 CHIP commands running in parallel, that will each create chips for 15 scenes.

The command DAG that is given to the runner is constructed such that each split command can be run in parallel if the runner supports parallelization, and that any command that is dependent on the output of the split command will be dependent on each of the splits. So that means, in the above example, a TRAIN command, which was dependent on a single CHIP command pre-split, will be dependent each of the 5 individual CHIP commands after the split.

Each runner will handle parallelization differently. For instance, the local runner will run each of the splits simultaneously, so be sure the split number is in relation to the number of CPUs available. The AWS Batch runner will use array jobs to run commands in parallel, and the Batch Compute Environment will determine how many resources are available to run jobs simultaneously.