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fourcastnet

Downloadable

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

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API Reference
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Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Generate API Key

Pull and Run the NIM

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

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

Bash
docker pull nvcr.io/nim/nvidia/fourcastnet:latest

This will download the optimized model for your infrastructure.

Bash
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:latest

Test the NIM

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

Bash
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:

Python
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:

Bash
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.