nvidia/fourcastnet

RUN ANYWHERE

FourCastNet predicts global atmospheric dynamics of various weather / climate variables.

By running the below commands, you accept the NVIDIA AI Enterprise Terms of Use and the NVIDIA Community Models License.

Pull and run nvidia/fourcastnet using Docker (this will download the full model and run it in your local environment)

$ docker login nvcr.io Username: $oauthtoken Password: <PASTE_API_KEY_HERE>

Pull and run the NVIDIA Earth-2 FourCastNet NIM with the command below.

docker pull nvcr.io/nim/nvidia/fourcastnet:1.0.0

This will download the optimized model for your infrastructure.

export NGC_API_KEY=<NGC API Key> docker run --rm --runtime=nvidia --gpus all --shm-size 4g \ -p 8000:8000 \ -e NGC_API_KEY \ nvcr.io/nim/nvidia/fourcastnet:1.0.0

Check the health of the NIM with the following curl command:

curl -X 'GET' \ 'http://localhost:8000/v1/health/ready' \ -H 'accept: application/json'

Generate an input numpy array for the model using the following Python script with Earth2Studio:

import numpy as np from datetime import datetime from earth2studio.data import ARCO from earth2studio.models.px.sfno import VARIABLES ds = ARCO() da = ds(time=datetime(2023, 1, 1), variable=VARIABLES) np.save("fcn_inputs.npy", da.to_numpy()[None].astype('float32'))

You can now make a local API call using this curl command:

curl -X POST \ -F "input_array=@fcn_inputs.npy" \ -F "input_time=2023-01-01T00:00:00Z" \ -F "simulation_length=4" \ -o output.tar \ http://localhost:8000/v1/infer

For more details on getting started with this NIM, visit the NVIDIA NIM Docs. For more details on the model and its input / output tensors see the FourCastNet SFNO Model Card.