Profile Earth Engine computation#
The Earth Engine API provides tools for profiling the performance of your computations but they are not always the easiest to use to get the number you are looking for. The geetools library supercharge the original profiler to make any computation evaluation the easiest possible.
Set up environment#
Install all the requireed libs if necessary. and perform the import satements upstream.
# uncomment if installation of libs is necessary
# !pip install earthengine-api geetools
import ee
import geetools
import pandas as pd
# uncomment if authetication to GEE is needed
# ee.Authenticate()
# ee.Intialize(project="<your_project>")
Example data#
The following examples rely on a ee.FeatureCollection composed of three ecoregion features that define regions by which to reduce image data. The Image data are PRISM climate normals, where bands describe climate variables per month; e.g., July precipitation or January mean temperature.
ecoregions = (
ee.FeatureCollection("projects/google/charts_feature_example")
.select(["label", "value","warm"])
)
normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands()
default profiler#
The default profiler from Earth Engine can be called as a context manager, it will print at the end of the cell the extensive description of your computation.
with ee.profilePrinting():
normClim.geetools.byBands(
regions = ecoregions,
reducer = "mean",
scale = 500,
regionId = "label",
bands = [f"{i:02d}_tmean" for i in range(1,13)],
).getInfo()
EECU·s PeakMem Count Description
0.332 59k 6 Algorithm Image.reduceRegions
0.232 566k 85 Loading assets: projects/google/charts_feature_example
0.171 378k 827 (plumbing)
0.027 617k 86 no description available
0.012 202k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/07@1662732032798195
0.012 209k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/06@1662731651724226
0.011 200k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/08@1662731245955723
0.011 106k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/11@1662731554688435
0.011 205k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/02@1662731486284455
0.011 211k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/05@1662731334196830
0.011 109k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/10@1662731228874571
0.011 198k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/01@1662731626359925
0.010 199k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/09@1662730604988590
0.010 210k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/03@1662731338127317
0.010 198k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/12@1662731114457874
0.010 111k 11 Loading assets: OREGONSTATE/PRISM/Norm91m/04@1662730567169297
0.006 312 72 Reprojecting pixels from GEOGCS["GCS_North_American_1983",DATUM["North_American_Datum_1983",SPHEROID[...] to GEOGCS["GCS_North_American_1983",DATUM["North_American_Datum_1983",SPHEROID[...]
0.001 9.6k 15 Algorithm ImageCollection.toBands
0.001 3.2k 13 Algorithm Collection.reduceColumns with reducer Reducer.toList
0.001 4.0k 15 Algorithm Image.select
0.001 5.8k 15 Algorithm Image.rename
0.001 3.0k 14 Algorithm ReduceRegions.AggregationContainer
0.000 61k 51 Loading assets: OREGONSTATE/PRISM/Norm91m
0.000 424 3 Listing collection
0.000 113k 3 Computing image mask from geometry
- 89k 26 Algorithm Collection.reduceColumns
- 44k 19 Algorithm List.map
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/01
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/12
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/11
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/10
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/09
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/08
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/07
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/06
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/05
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/04
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/03
- 31k 5 Loading assets: OREGONSTATE/PRISM/Norm91m/02
- 28k 14 Algorithm Dictionary.fromLists
- 23k 40 Algorithm AggregateFeatureCollection.array
- 11k 15 Algorithm Collection.loadTable
- 10k 15 Algorithm ImageCollection.load
- 8.8k 4 Algorithm Projection
- 8.6k 1 Algorithm (user-defined function)
- 6.9k 4 Algorithm ReduceRegions.ReduceRegionsEnumerator
- 5.2k 15 Algorithm Collection.map
- 3.4k 14 Algorithm Feature.select
- 3.4k 10 Algorithm If
- 3.3k 10 Algorithm Number.eq
- 3.2k 4 Algorithm String.compareTo
- 3.1k 10 Algorithm ObjectType
- 3.1k 4 Algorithm Reducer.forEach
- 3.1k 37 Algorithm String
- 2.9k 20 Loading assets: OREGONSTATE/PRISM
- 2.8k 18 Loading assets: projects/google
- 1.9k 1 Algorithm Number.format
- 664 5 Algorithm Reducer.mean
- 432 7 Expression evaluation
- 264 72 Algorithm Image.load computing pixels
This result is extremely useful but cannot be further explored in the notebook.
geetools profiler#
The geetools profiler is a context manager object that fill a dictionary member (profile) with the content of the string profile. This dictionary can be transformed into a table easily.
# example with a simple function
with ee.geetools.Profiler() as p:
ee.Number(3.14).add(0.00159).getInfo()
p.profile
{'EECU-s': [0.0, None],
'PeakMem': [4560, 3100],
'Count': [3, 3],
'Description': ['(plumbing)', 'Algorithm']}
With a bigger method we can valorized the results as a pandas dataframe and extract key informations.
with ee.geetools.Profiler() as p:
normClim.geetools.byBands(
regions = ecoregions,
reducer = "mean",
scale = 500,
regionId = "label",
bands = [f"{i:02d}_tmean" for i in range(1,13)],
).getInfo()
df = pd.DataFrame(p.profile)
df.head()
| EECU-s | PeakMem | Count | Description | |
|---|---|---|---|---|
| 0 | 0.289 | 59000 | 6 | Algorithm |
| 1 | 0.218 | 567000 | 87 | Loading |
| 2 | 0.137 | 378000 | 831 | (plumbing) |
| 3 | 0.023 | 640000 | 86 | no |
| 4 | 0.010 | 211000 | 11 | Loading |
# total EECU cost of the computation
float(df["EECU-s"].sum())
0.7850000000000001