A package for running predictions using fAIr
Project description
fAIr Predictor
Run your fAIr Model Predictions anywhere !
Prerequisites
fAIr Predictor has support for GPU , CPU and tflite based devices
- Install
tensorflow-cpuortflite-runtimeaccording to your requirements
tflite-runtime support is for having very light deployment in order to run inference &
tensorflow-cpu might require installation of efficientnet
Example on Collab
# Install
!pip install fairpredictor
# Import
from predictor import predict
# Parameters for your predictions
bbox=[100.56228021333352,13.685230854641182,100.56383321235313,13.685961853747969]
model_path='checkpoint.h5'
zoom_level=20
tms_url='https://tiles.openaerialmap.org/6501a65c0906de000167e64d/0/6501a65c0906de000167e64e/{z}/{x}/{y}'
# Run your prediction
my_predictions=predict(bbox,model_path,zoom_level,tms_url)
print(my_predictions)
## Visualize your predictions
import geopandas as gpd
import matplotlib.pyplot as plt
gdf = gpd.GeoDataFrame.from_features(my_predictions)
gdf.plot()
plt.show()
Works on CPU ! Can work on serverless functions, No other dependencies to run predictions
Use raster2polygon
There is another postprocessing option that supports distance threshold between polygon for merging them , If it is useful for you install raster2polygon by :
pip install raster2polygon
Load Testing
CAUTION : Always take permission of server admin before you perform load test
In order to perform load testing we use Locust , To enable this hit following command within the root dir
-
Install locust
pip install locust -
Run locust script
locust -f locust.py
Populate your HOST and replace it with BASE URL of the Predictor URL
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file fairpredictor-0.0.34.tar.gz.
File metadata
- Download URL: fairpredictor-0.0.34.tar.gz
- Upload date:
- Size: 10.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a8cc58bccfa621a04a1007279d4ea2b815eb5c66a66ce2714c237ddf8d7cc7a4 |
|
| MD5 | e8a9245fc2ae359f17a2773ae26bd4c2 |
|
| BLAKE2b-256 | a59c708e111b802f3864bcf8eaa7454f032090de0bfc8a28843c1f9b7c3a1390 |
File details
Details for the file fairpredictor-0.0.34-py3-none-any.whl.
File metadata
- Download URL: fairpredictor-0.0.34-py3-none-any.whl
- Upload date:
- Size: 12.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 9de235ef9e5454dd280dc53c1ad2aaf10d5280a661afa750daeac9d44b79cec1 |
|
| MD5 | 2c14eb9588bf5fb7fe003c712532bb35 |
|
| BLAKE2b-256 | 9cb99d8163a5267dca4285c18787c63b7f128f5f9867f9d6898e773dbc9c1be2 |
