Prerequisites for NVIDIA Earth-2 FourCastNet NIM#
Use this documentation to learn about the prerequisites for using NVIDIA Earth-2 FourCastNet NIM.
Support Matrix#
Model Support#
Model Name |
Publisher |
|---|---|
FourCastNet v2 SFNO ERA5 73ch Fine-tuned |
NVIDIA |
FourCastNet v2 SFNO ERA5 73ch |
NVIDIA |
Hardware Support#
Optimized Configuration#
The GPU Memory and Disk Space values are in GB.
Disk Space is for both the container and the model.
Profile is for what the model is optimized.
GPU |
GPU Memory (GB) |
Precision |
# of GPUs |
Disk Space |
|---|---|---|---|---|
H100 |
80 |
FP32 |
1 |
64 |
A100 |
40 & 80 |
FP32 |
1 |
64 |
RTX A6000 |
48 |
FP32 |
1 |
64 |
L40S |
48 |
FP32 |
1 |
64 |
Non-optimized Configuration#
The GPU Memory and Disk Space values are in Gb.
Disk Space is for both the container and the model.
GPU |
GPU Memory (GB) |
Precision |
# of GPUs |
Disk Space |
|---|---|---|---|---|
Any NVIDIA GPU with sufficient GPU memory and compute capability ≥ 8.0 |
40 |
FP32 |
1 |
64 |
Software#
The following are the software prerequisites for using FourCastNet NIM:
With a Docker-supported operating system, install Docker - minimum version: 23.0.1
Install NVIDIA Drivers - minimum version: 545. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).
Install and configure the NVIDIA Container Toolkit - minimum version: 1.13.5
Verify that your container runtime supports NVIDIA GPUs by running
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
Example output:
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-PCIE-40GB Off | 00000000:41:00.0 Off | 0 |
| N/A 37C P0 35W / 250W | 40423MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
For more information on enumerating multi-GPU systems, see the NVIDIA Container Toolkit’s GPU Enumeration Docs.
NGC Account#
You must have an authenticated NGC (NVIDIA GPU Cloud) account with access to both the respective NIM and model checkpoints. Use the following procedure to log in to NGC and set the NGC_API_KEY environment variable, so that you can pull images.
Create an account on NGC.
Generate an API Key. The following steps require your NGC API key.
Authenticate local Docker with NGC by running the following code. For more details, see the NGC authentication documentation.
docker login nvcr.io Username: $oauthtoken Password: <NGC API key>
Set the NGC API key environment variable in your shell. You need to set the
NGC_API_KEYvariable.export NGC_API_KEY=<NGC API key>
Client Side Python Dependencies#
This NIM requires an input containing several weather fields at the desired time-stamp in the form of a NumPy array. This input data can be fetched by using the Earth2Studio package as shown in the quickstart guide. For this, setup your Python environment (Python ≥ 3.10, < 3.12) and install the required dependencies using:
pip install --upgrade pip
pip install earth2studio
Important
This NIM requires Earth2Studio version 0.3.0 or later.
In the provided guide, several other packages are used to interact with the NIM and post process the results:
pip install requests
pip install matplotlib
Next Steps#
After you install all prerequisites, to get started, see the quickstart guide.