osml-model-runner 2.3.0

Application to run large scale imagery against AI/ML models

OSML Model Runner

This package contains an application used to orchestrate the execution of ML models on large satellite images. The application monitors an input queue for processing requests, decomposes the image into a set of smaller regions and tiles, invokes an ML model endpoint with each tile, and finally aggregates all the results into a single output. The application itself has been containerized and is designed to run on a distributed cluster of machines collaborating across instances to process images as quickly as possible.

Table of Contents

• Getting Started
  • Key Design Concepts
    • Image Tiling
    • Geolocation
    • Merging Results from Overlap Regions
  • Package Layout
  • Prerequisites
  • Development Environment
  • Running ModelRunner
  • Infrastructure
    • S3
  • Documentation
• Support & Feedback
• Security
• License

Getting Started

Key Design Concepts

The Guidance for Model Developers document contains details of how the OversightML ModelRunner applications interacts with containerized computer vision (CV) models and examples of the GeoJSON formatted inputs it expects and generates. At a high level this application provides the following functions:

Image Tiling

The images to be processed by this application are expected to range anywhere from 500MB to 500GB in size. The upper bound is consistently growing as sensors become increasingly capable of collecting larger swaths of high resolution data. To handle these images the application applies two levels of tiling. The first is region based tiling in which the application breaks the full image up into pieces that are small enough for a single machine to handle. All regions after the first are placed on a second queue so other model runners can start processing those regions in parallel. The second tiling phase is to break each region up into individual chunks that will be sent to the ML models. Many ML model containers are configured to process images that are between 512 and 2048 pixels in size so the full processing of a large 200,000 x 200,000 satellite image can result in >10,000 requests to those model endpoints.

The images themselves are assumed to reside in S3 and are assumed to be compressed and encoded in such a way as to facilitate piecewise access to tiles without downloading the entire image. The GDAL library, a frequently used open source implementation of GIS data tools, has the ability to read images directly from S3 making use of partial range reads to only download the part of the overall image necessary to process the region.

Geolocation

Most ML models do not contain the photogrammetry libraries needed to geolocate objects detected in an image. ModelRunner will convert these detections into geospatial features by using sensor models described in an image metadata. The details of the photogrammetry operations are in the osml-imagery-toolkit library.

Merging Results from Overlap Regions

Many of the ML algorithms we expect to run will involve object detection or feature extraction. It is possible that features of interest would fall on the tile boundaries and therefore be missed by the ML models because they are only seeing a fractional object. This application mitigates that by allowing requests to specify an overlap region size that should be tuned to the expected size of the objects. Each tile sent to the ML model will be cut from the full image overlapping the previous by the specified amount. Then the results from each tile are aggregated with the aid of a Non-Maximal Suppression algorithm used to eliminate duplicates in cases where an object in an overlap region was picked up by multiple model runs.

Metrics and Logs

As the application runs key performance metrics and detailed logging information are output to CloudWatch. A detailed description of what information is tracked along with example dashboards can be found in METRICS_AND_DASHBOARDS.md.

Package Layout

• /src: This is the Python implementation of this application.
• /test: Unit tests have been implemented using pytest.
• /bin: The entry point for the containerized application.
• /scripts: Utility scripts that are not part of the main application frequently used in development / testing.

Prerequisites

First, ensure you have installed the following tools locally

• docker
• tox
• osml cdk deployed into your aws account

Development Environment

To run the container in a build/test mode and work inside it.

docker run -it -v `pwd`/:/home/ --entrypoint /bin/bash .

Running ModelRunner

To start a job, place an ImageRequest on the ImageRequestQueue.

Sample ImageRequest:

{
    "jobName": "<job_name>",
    "jobId": "<job_id>",
    "imageUrls": ["<image_url>"],
    "outputs": [
        {"type": "S3", "bucket": "<result_bucket_arn>", "prefix": "<job_name>/"},
        {"type": "Kinesis", "stream": "<result_stream_arn>", "batchSize": 1000}
    ],
    "imageProcessor": {"name": "<sagemaker_endpoint>", "type": "SM_ENDPOINT"},
    "imageProcessorTileSize": 2048,
    "imageProcessorTileOverlap": 50,
    "imageProcessorTileFormat": "< NITF | JPEG | PNG | GTIFF >",
    "imageProcessorTileCompression": "< NONE | JPEG | J2K | LZW >"
}

Infrastructure

S3

When configuring S3 buckets for images and results, be sure to follow S3 Security Best Practices.

Code Documentation

You can find documentation for this library in the ./doc directory. Sphinx is used to construct a searchable HTML version of the API documents.

tox -e docs

Support & Feedback

To post feedback, submit feature ideas, or report bugs, please use the Issues section of this GitHub repo.

If you are interested in contributing to OversightML Model Runner, see the CONTRIBUTING guide.

Security

See CONTRIBUTING for more information.

License

MIT No Attribution Licensed. See LICENSE.