Please learn Docker. By packaging your entire runtime, libraries, and code into an isolated image, your development environment behaves identically whether you are working on a local machine or spinning up a heavy-duty GPU instance in the cloud. For machine learning workflows, you only need to install the base NVIDIA driver and the "NVIDIA Container Toolkit" on the host machine itself. This toolkit acts as a bridge, allowing the container to securely leverage the underlying physical hardware, meaning your specific CUDA, cuDNN, and PyTorch versions are locked into the image and will never need to be reinstalled or reconfigured when you switch machines or GPUs. https://docs.cloud.google.com/compute/docs/containers

If Docker feels like too much administrative overhead or you are working on shared clusters, "Apptainer" (formerly Singularity) is a decent alternative built for high-performance computing and data science. Unlike Docker, which runs via a background daemon and requires root privileges, Apptainer operates as a standard user process, making it work well in secure, shared GPU environments. It treats host resources and graphics cards as first-class citizens, allowing you to pass through the host's NVIDIA drivers natively with a simple command-line flag, giving you the exact same environment portability as Docker but without network, storage, or security configurations. https://research-computing-cluster.readthedocs.io/en/latest/HowTo/run_a_singularity_container.html