Frequently Asked Questions
TensorRigs is a platform that provides deep learning enthusiasts, researchers, and professionals with system recommendations, GPU benchmarks, and optimized workflows for scalable ML workloads in high-performance computing environments.
TensorRigs benchmarks a variety of GPUs including NVIDIA RTX 30/40 series, A100, and professional workstation cards. Recommended systems depend on workload size, but the platform provides optimized configurations for single-GPU, multi-GPU, and HPC clusters.
TensorRigs supports native image formats, TFRecords, and WebDataset shards. Users can benchmark loading performance and optimize their data pipelines for PyTorch and TensorFlow workloads.
TensorRigs provides guidance on using DALI pipelines, optimizing data loading, and efficient GPU utilization. Users can benchmark I/O performance to minimize training bottlenecks and scale workflows across HPC clusters.
Benchmarks show throughput, latency, and I/O efficiency for different hardware and dataset formats. Use these metrics to select optimal GPUs, system configurations, and data pipelines for your deep learning projects.
Yes, TensorRigs workflows and benchmarks are designed for both PyTorch and TensorFlow. Users can switch frameworks without changing system configurations and still track I/O and training performance efficiently.
Yes, TensorRigs supports multi-GPU setups, including NVLink and PCIe configurations, as well as distributed HPC clusters. Benchmarking and optimization guidance is included for scaling large ML workloads.
Start by exploring the system recommendations and benchmark results for your desired workload. Install the necessary ML framework, set up the dataset, and follow TensorRigs optimization guides for maximum throughput.