Introduction

TensorFlow, an open-source machine learning framework developed by Google, can leverage GPUs to accelerate its computational tasks. This is particularly useful for handling large-scale machine learning tasks, such as training deep neural networks. However, compatibility between TensorFlow and the GPU hardware must be ensured to harness these benefits. In this article, we'll focus on running TensorFlow on GPUs with compute capability 2.x, providing insights and steps to optimize the usage of such older GPU hardware.

Understanding GPU Compute Capability

Compute capability is a property of an NVIDIA GPU that indicates its architecture and features. It is crucial as it defines the level of functionality that TensorFlow can exploit from the GPU. GPUs with compute capability 2.x belong to the Fermi architecture, which is quite dated but still relevant in certain legacy systems.

Key Points:

• Compute Capability: An attribute defining the features supported by the GPU.
• Fermi Architecture: GPUs with compute capability 2.x fall under this category.
• Relevance: Best suited for legacy systems where upgrading to a newer GPU is not feasible.

Setting Up TensorFlow on GPU with Compute Capability 2.x

1. Verifying GPU Compatibility

Before setting up TensorFlow to run on a GPU, verify if your GPU supports the desired compute capability. You can do this using the `nvidia-smi` command in the terminal, which provides details about the installed NVIDIA drivers and the GPU compute capability.

• Python Environment: Create a virtual environment for managing the TensorFlow version. Tools like `virtualenv` or `conda` can be used.
• Install TensorFlow 1.10 or Earlier: Ensure you install a compatible version of TensorFlow.
• CUDA: For TensorFlow 1.10, CUDA 9.0 is a recommended version.
• cuDNN: Install cuDNN version compatible with CUDA 9.0.
• Obsolete Technology: Running modern deep learning tasks may not be feasible.
• Lack of Support: Community and official support for such old versions are limited.
• Intermediate Layer: Use a setup tool like Docker with pre-configured images for older hardware.
• Optimize Models: Reduce model complexity and batch sizes to fit into the GPU memory constraints of Fermi architecture.