Downloading the Modulus Docker container is preferred over a manual pip installation for several reasons:
To actually use Modulus, you need to launch the container with GPU support enabled. Use the --gpus all flag to give the container access to your hardware.
By following these steps, you’ve successfully bypassed the headache of manual environment setup. With the Modulus container running, you can now dive into the included examples or begin training your own physics-informed models. download the modulus docker container
Architecture like Ampere (A100/RTX 30-series), Hopper (H100), or newer. Docker: Installed and running.
--rm : Automatically removes the container once you exit (keeps your system clean). Downloading the Modulus Docker container is preferred over
Once inside the container, you can verify that Modulus is ready by checking the installed version or running a basic example. python -c "import modulus; print(modulus.__version__)" Use code with caution.
(Note: Replace 24.01 with the most recent version tag found on the NGC Modulus page.) Step 3: Run the Container With the Modulus container running, you can now
If you are working with Physics-Informed Neural Networks (PINNs) or AI-driven simulation, you are likely looking for NVIDIA Modulus. To ensure a stable environment with all dependencies pre-configured, using a Docker container is the industry-standard approach.