The NMCP Precomputed service generates reconstruction skeletons in Neuroglancer's precomputed format. It periodically polls the API service for reconstructions that are ready for new or updated skeletons.
Given the current behavior or Neuroglancer layers and the NMCP viewer feature requirements, the service currently generated separate data sources for the full reconstruction, as well as axon-only and dendrite-only versions.
The service uses the chunked API for acquiring reconstruction data and should be compatible with dense reconstruction sources.
A local S3-compatible service may be used during local development to avoid AWS or other cloud service storage charges.
Note that at this time, cloud-files only appears to support this option from Linux, WSL, or MacOS.
One functional option is to run a local MinIO Docker container. The following steps describe the process (and assume the default configuration of port 9000 for MinIO). For additional details see either the MinIO documentation, cloud-files, and cloud-volume.
- Install the MinIO command line tool,
mc - Use
mcor the web interface at http://localhost:9001 to create a bucket - Use
mcto set the bucket to public access (this portion is for Neuroglancer viewing, not required for precomputed generation) - With the Python environment for this project active (which should mean the cloud-files command line interface is available):
- Add an alias for your instance:
cloudfiles alias add minio s3://http://127.0.0.1:9000/
- Add an alias for your instance:
- When running the precomputed worker, pass the alias as the precomputed output argument, e.g.,
-o minio://<your_bucket>/ngv01
Here the current standard path is used as the base location (ngv01), however anything can used so long as it is also
used in the NMCP_PRECOMPUTED environment variable for nmcp-client.