TensorFlow 2.15 Is Faster, Brings New One-Shot Installer for NVIDIA CUDA Acceleration on Linux
The latest release of TensorFlow, version 2.15, is out now — and brings with it a much easier way to get started with CUDA-based accelerated machine learning on NVIDIA hardware under Linux.
"The tensorflow pip package has a new, optional installation method for Linux that installs necessary NVIDIA CUDA libraries through pip," the TensorFlow team writes of its new software release. "As long as the NVIDIA driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's NVIDIA CUDA library dependencies in the Python environment. Aside from the NVIDIA driver, no other pre-existing NVIDIA CUDA packages are necessary."
Built as the successor to Google Brain's DistBelief. TensorFlow was first released in 2017 as a free and open source framework for machine learning and artificial intelligence workloads. In recent years the framework has been extended, now capable of running on everything from high performance computing clusters to microcontrollers — the latter using a special variant dubbed TensorFlow Lite for Microcontrollers and capable of performing on-device machine learning in resource-constrained environments.