Raster Vision PyTorch/fastai Plugin
Setup and Requirements
docker (preferably version 18 or above) installed. After cloning this repo, to build the Docker images, run the following command:
Before running the container, set an environment variable to a local directory in which to store data.
> export RASTER_VISION_DATA_DIR="/path/to/data"
To run a Bash console in the Docker container, invoke:
This will mount the following local directories to directories inside the container:
$RASTER_VISION_DATA_DIR -> /opt/data/
fastai_plugin/ -> /opt/src/fastai_plugin/
examples/ -> /opt/src/examples/
scripts/ -> /opt/src/scripts/
This script also has options for forwarding AWS credentials (
--aws), running Jupyter notebooks (
--jupyter), running on a GPU (
--gpu), and others.
docker/run --help for more details.
For debugging, it can be helpful to use a local copy of the Raster Vision source code rather than the version baked into the Docker image. To do this, you can set the
RASTER_VISION_REPO environment variable to the location of the main repo on your local filesystem. If this is set,
docker/run will mount
/opt/src/rastervision inside the container. You can then modify your local copy of Raster Vision in order to debug experiments running inside the container.
(Optional) Setup AWS Batch
This assumes that a Batch stack was created using the Raster Vision AWS Batch setup.
To use this plugin, you will need to add a job definition which points to a new tag on the ECR repo, and then publish the image to that tag.
You can do this by editing scripts/cpu_job_def.json, [scripts/gpu_job_def.json](scripts/gpu_job_def.json], and [docker/publish_image], and then running
docker/publish_image outside the container, and
scripts/add_job_defs inside the container.
Using the plugin requires making a Raster Vision profile which points to the location of the plugin module. You can make such a profile by creating a file at
~/.rastervision/fastai containing something like the following. If using Batch, the
AWS_BATCH section should point to the resources created above.
[AWS_BATCH] job_queue=lewfishRasterVisionGpuJobQueue job_definition=lewfishFastaiPluginGpuJobDef cpu_job_queue=lewfishRasterVisionCpuJobQueue cpu_job_definition=lewfishFastaiPluginCpuJobDef attempts=5 [AWS_S3] requester_pays=False [PLUGINS] files= modules=["fastai_plugin.semantic_segmentation_backend_config"]
Running an experiment
To test the plugin, you can run an experiment using the ISPRS Potsdam dataset. Info on setting up the data and experiments in general can be found in the examples repo. A test run can be executed locally using something like the following. The
-p fastai flag says to use the
fastai profile created above.
export RAW_URI="/opt/data/raw-data/isprs-potsdam" export PROCESSED_URI="/opt/data/fastai/potsdam/processed-data" export ROOT_URI="/opt/data/fastai/potsdam/local-output" rastervision -p fastai run local -e examples.semantic_segmentation.potsdam -m *exp_resnet18* \ -a raw_uri $RAW_URI -a processed_uri $PROCESSED_URI -a root_uri $ROOT_URI \ -a test True --splits 2
A full experiment can be run on AWS Batch using something like:
export RAW_URI="s3://raster-vision-raw-data/isprs-potsdam" export PROCESSED_URI="s3://raster-vision-lf-dev/fastai/potsdam/processed-data" export ROOT_URI="s3://raster-vision-lf-dev/fastai/potsdam/remote-output" rastervision -p fastai run aws_batch -e examples.semantic_segmentation.potsdam -m *exp_resnet18* \ -a raw_uri $RAW_URI -a processed_uri $PROCESSED_URI -a root_uri $ROOT_URI \ -a test False --splits 4
This gets to an average F1 score of 0.87 after 15 minutes of training.