Populating Algorithms: Mogpr from Fusets #80
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| { | ||
| "id": "fusets_mogpr", | ||
| "type": "Feature", | ||
| "conformsTo": [ | ||
| "http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core" | ||
| ], | ||
| "geometry": null, | ||
| "properties": { | ||
| "created": "2025-01-093T00:00:00Z", | ||
| "updated": "2025-01-09T00:00:00Z", | ||
| "type": "apex_algorithm", | ||
| "title": "Multi output gaussian process regression", | ||
| "description": "Integrates timeseries in data cube using multi-output gaussian process regression. The service is designed to enable multi-output regression analysis using Gaussian Process Regression (GPR) on geospatial data. It provides a powerful tool for understanding and predicting spatiotemporal phenomena by filling gaps based on other indicators that are correlated with each other.", | ||
| "cost_estimate": 12, | ||
| "cost_unit": "platform credits per km²", | ||
| "keywords": [ | ||
| "timeseries", | ||
| "Gaussian Process Regression (GPR)" | ||
| ], | ||
| "language": { | ||
| "code": "en-US", | ||
| "name": "English (United States)" | ||
| }, | ||
| "languages": [ | ||
| { | ||
| "code": "en-US", | ||
| "name": "English (United States)" | ||
| } | ||
| ], | ||
| "contacts": [ | ||
| { | ||
| "name": "Bram Janssen", | ||
| "position": "Researcher", | ||
| "organization": "VITO", | ||
| "links": [ | ||
| { | ||
| "href": "https://www.vito.be/", | ||
| "rel": "about", | ||
| "type": "text/html" | ||
| }, | ||
| { | ||
| "href": "https://github.com/JanssenBrm", | ||
| "rel": "about", | ||
| "type": "text/html" | ||
| } | ||
| ], | ||
| "contactInstructions": "Contact via VITO", | ||
| "roles": [ | ||
| "principal investigator" | ||
| ] | ||
| }, | ||
| { | ||
| "name": "Pratichhya Sharma", | ||
| "position": "Researcher", | ||
| "organization": "VITO", | ||
| "links": [ | ||
| { | ||
| "href": "https://www.vito.be/", | ||
| "rel": "about", | ||
| "type": "text/html" | ||
| }, | ||
| { | ||
| "href": "https://github.com/Pratichhya", | ||
| "rel": "about", | ||
| "type": "text/html" | ||
| } | ||
| ], | ||
| "contactInstructions": "Contact via VITO", | ||
| "roles": [ | ||
| "service provider" | ||
| ] | ||
| }, | ||
| { | ||
| "name": "VITO", | ||
| "links": [ | ||
| { | ||
| "href": "https://www.vito.be/", | ||
| "rel": "about", | ||
| "type": "text/html" | ||
| } | ||
| ], | ||
| "contactInstructions": "SEE WEBSITE", | ||
| "roles": [ | ||
| "processor" | ||
| ] | ||
| } | ||
| ], | ||
| "themes": [ | ||
| { | ||
| "concepts": [ | ||
| { | ||
| "id": "Normalised vegetation difference index (NDVI)" | ||
| }, | ||
| { | ||
| "id": "Radar Vegetation Index (RVI)" | ||
| }, | ||
| { | ||
| "id": "Multi-output Gaussian Process Regression (MOGPR)" | ||
| } | ||
| ], | ||
| "scheme": "https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords" | ||
| } | ||
| ], | ||
| "formats": [ | ||
| { | ||
| "name": "JSON" | ||
| } | ||
| ], | ||
| "license": "other" | ||
| }, | ||
| "linkTemplates": [], | ||
| "links": [ | ||
| { | ||
| "rel": "openeo-process", | ||
| "type": "application/json", | ||
| "title": "openEO Process Definition", | ||
| "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/mogpr_v1/openeo_udp/fusets_mogpr/fusets_mogpr.json" | ||
|
There was a problem hiding this comment. this branch will probably be deleted afterwards, so make sure to update once merged |
||
| }, | ||
| { | ||
| "rel": "service", | ||
| "type": "application/json", | ||
| "title": "CDSE openEO federation", | ||
| "href": "https://openeofed.dataspace.copernicus.eu" | ||
| }, | ||
| { | ||
| "rel": "license", | ||
| "href": "https://apex.esa.int/license" | ||
| }, | ||
| { | ||
| "rel": "example", | ||
| "type": "application/json", | ||
| "title": "Example output", | ||
| "href": "https://s3.waw3-1.cloudferro.com/swift/v1/apex-examples/fusets_mogpr/timeseries.json" | ||
| } | ||
| ] | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| [ | ||
| { | ||
| "id": "fusets_mogpr", | ||
| "type": "openeo", | ||
| "description": "Multi output gaussian process regression example on NDVI timeseries", | ||
| "backend": "openeofed.dataspace.copernicus.eu", | ||
| "process_graph": { | ||
| "aggregatespatial1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "fusetsmogpr1" | ||
| }, | ||
| "geometries": { | ||
| "coordinates": [ | ||
| [ | ||
| [ | ||
| 5.170012098271149, | ||
| 51.25062964728295 | ||
| ], | ||
| [ | ||
| 5.17085904378298, | ||
| 51.24882567194015 | ||
| ], | ||
| [ | ||
| 5.17857421368097, | ||
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| ], | ||
| [ | ||
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| ], | ||
| [ | ||
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| ] | ||
| ] | ||
| ], | ||
| "type": "Polygon" | ||
| }, | ||
| "reducer": { | ||
| "process_graph": { | ||
| "mean1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_parameter": "data" | ||
| } | ||
| }, | ||
| "process_id": "mean", | ||
| "result": true | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "process_id": "aggregate_spatial" | ||
| }, | ||
| "fusetsmogpr1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "ndvi1" | ||
| } | ||
| }, | ||
| "namespace": "https://openeo.dataspace.copernicus.eu/openeo/1.2/processes/u:3e24e251-2e9a-438f-90a9-d4500e576574/fusets_mogpr", | ||
| "process_id": "fusets_mogpr" | ||
| }, | ||
| "loadcollection1": { | ||
| "arguments": { | ||
| "bands": [ | ||
| "B04", | ||
| "B08" | ||
| ], | ||
| "id": "SENTINEL2_L2A", | ||
| "spatial_extent": { | ||
| "coordinates": [ | ||
| [ | ||
| [ | ||
| 5.170012098271149, | ||
| 51.25062964728295 | ||
| ], | ||
| [ | ||
| 5.17085904378298, | ||
| 51.24882567194015 | ||
| ], | ||
| [ | ||
| 5.17857421368097, | ||
| 51.2468515482926 | ||
| ], | ||
| [ | ||
| 5.178972704726344, | ||
| 51.24982704376254 | ||
| ], | ||
| [ | ||
| 5.170012098271149, | ||
| 51.25062964728295 | ||
| ] | ||
| ] | ||
| ], | ||
| "type": "Polygon" | ||
| }, | ||
| "temporal_extent": [ | ||
| "2022-05-01", | ||
| "2022-06-01" | ||
| ] | ||
| }, | ||
| "process_id": "load_collection" | ||
| }, | ||
| "loadcollection2": { | ||
| "arguments": { | ||
| "bands": [ | ||
| "SCL" | ||
| ], | ||
| "id": "SENTINEL2_L2A", | ||
| "spatial_extent": { | ||
| "coordinates": [ | ||
| [ | ||
| [ | ||
| 5.170012098271149, | ||
| 51.25062964728295 | ||
| ], | ||
| [ | ||
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| [ | ||
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| [ | ||
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| 51.24982704376254 | ||
| ], | ||
| [ | ||
| 5.170012098271149, | ||
| 51.25062964728295 | ||
| ] | ||
| ] | ||
| ], | ||
| "type": "Polygon" | ||
| }, | ||
| "temporal_extent": [ | ||
| "2022-05-01", | ||
| "2022-06-01" | ||
| ] | ||
| }, | ||
| "process_id": "load_collection" | ||
| }, | ||
| "mask1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "loadcollection1" | ||
| }, | ||
| "mask": { | ||
| "from_node": "toscldilationmask1" | ||
| } | ||
| }, | ||
| "process_id": "mask" | ||
| }, | ||
| "ndvi1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "mask1" | ||
| }, | ||
| "nir": "B08", | ||
| "red": "B04" | ||
| }, | ||
| "process_id": "ndvi" | ||
| }, | ||
| "saveresult1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "aggregatespatial1" | ||
| }, | ||
| "format": "JSON", | ||
| "options": {} | ||
| }, | ||
| "process_id": "save_result", | ||
| "result": true | ||
| }, | ||
| "toscldilationmask1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "loadcollection2" | ||
| } | ||
| }, | ||
| "process_id": "to_scl_dilation_mask" | ||
| } | ||
| }, | ||
| "reference_data": { | ||
| "job-results.json": "https://s3.waw3-1.cloudferro.com/swift/v1/apex-examples/fusets_mogpr/job-results.json", | ||
| "timeseries.json": "https://s3.waw3-1.cloudferro.com/swift/v1/apex-examples/fusets_mogpr/timeseries.json" | ||
| } | ||
| } | ||
| ] |
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was this calculated with the standard job options? or did you reevalute this cost?
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No this was when updating the job_option. As mentioned in the documentation, 'executor-memory': '7g' was set, however I used:
"executor-memory": "1G",
"executor-memoryOverhead": "500m",
"python-memory": "3G"
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Okay, so did this influence the cost estimate?
Also did you use the same memory settings in your benchmark?
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not much: by 4/5 credits.
Usually in my setting it is around 15 credits but with the executor-memory: 7g it is 19credits for a month and same aoi