To monitor the training process using Tensorboard, visit :6006 for the EC2 instance running the training job. Rastervision run batch _classification.spacenet_rio \įor instructions on setting up AWS Batch resources and configuring Raster Vision to use them, see Setting up AWS Batch. Note that all the URIs are on S3 since remote instances will not have access to your local file system.Įxport PROCESSED_URI="s3://mybucket/examples/spacenet/rio/processed-data"Įxport ROOT_URI="s3://mybucket/examples/spacenet/rio/remote-output"
#SPACENET 5 FULL#
To run the full experiment on GPUs using AWS Batch, use something like the following.
These “debug chips” for each of the data splits can be found in $ROOT_URI/train/dataloaders/. All of the examples that use big image files use this trick to make the experiment run faster in test mode.Īfter running this, the main thing to check is that it didn’t crash, and that the visualization of training and validation chips look correct. Note that when running with -a test True, some crops of the test scenes are created and stored in processed_uri/crops/. See rastervision -help and rastervision run -help for more usage information. This runs two parallel jobs for the chip and predict commands via -splits 2. The raw_uri directory is assumed to contain an AOIs/AOI_1_Rio subdirectory. The sample above assumes that the raw data is on S3, and the processed data and output are stored locally. a raw_uri $RAW_URI -a processed_uri $PROCESSED_URI -a root_uri $ROOT_URI \ Rastervision run local _classification.spacenet_rio \ Optional: to run this example with the data stored locally, first copy the data using something like the following inside the container.Įxport PROCESSED_URI="/opt/data/examples/spacenet/rio/processed-data"Įxport ROOT_URI="/opt/data/examples/spacenet/rio/local-output"
(To forward you AWS credentials into the container, use docker/run -aws). You will need an AWS account to access this dataset, but it will not be charged for accessing it. The dataset is stored on AWS S3 at s3://spacenet-dataset. This example performs chip classification to detect buildings on the Rio AOI of the SpaceNet dataset. Chip Classification: SpaceNet Rio Buildings ¶ For other examples, we only note example-specific details.
#SPACENET 5 HOW TO#
In the next section, we describe in detail how to run one of the examples, SpaceNet Rio Chip Classification. The raw_uri, processed_uri, and root_uri can each be local or remote (on S3), and don’t need to agree on whether they are local or remote.Įxperiments have a test argument which runs an abbreviated experiment for testing/debugging purposes. The output generated by the experiment is stored in the directory set by the root_uri argument. These two directories are set using the raw_uri and processed_uri arguments. The input data for each experiment is divided into two directories: the raw data which is publicly distributed, and the processed data which is derived from the raw data.
(Optional) Make predictions on new imageryĮach of the examples has several arguments that can be set on the command line: (Optional) Do an abbreviated test run of the experiment on a small subset of data locally. (Optional) Get processed dataset which is derived from the raw dataset, either using a Jupyter notebook, or by downloading the processed dataset. Running an example involves the following steps. There is a common structure across all of the examples which represents a best practice for defining experiments. See Docker Images for info on how to do this. Unless otherwise stated, all commands should be run inside the Raster Vision Docker container. This page contains examples of using Raster Vision on open datasets.
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