[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-07-11 (世界標準時間)。"],[[["NVIDIA Multi-Process Service (MPS) improves GPU efficiency and utilization when running multiple SDK processes on a shared Dataflow GPU by enabling concurrent processing and resource sharing."],["Enabling MPS enhances parallel processing and throughput for GPU pipelines, particularly for workloads with low GPU resource usage, potentially reducing overall costs."],["MPS is supported on Dataflow workers with a single GPU and requires specific pipeline configurations, including appending `use_nvidia_mps` to the `worker_accelerator` parameter with a count of 1 and avoiding the `--experiments=no_use_multiple_sdk_containers` option."],["When using TensorFlow with MPS, you must enable dynamic memory allocation on the GPU and use logical devices with memory limits to optimize performance."],["MPS is not compatible with Dataflow Prime."]]],[]]