这是否意味着我有 Nvidia GPU？
Does this mean I have a Nvidia GPU?
abigail@abilina:~/nlp$ lspci
00:00.0 Host bridge: Intel Corporation Skylake Host Bridge/DRAM Registers (rev 07)
00:01.0 PCI bridge: Intel Corporation Skylake PCIe Controller (x16) (rev 07)
00:02.0 Display controller: Intel Corporation HD Graphics 530 (rev 06)
00:14.0 USB controller: Intel Corporation Sunrise Point-H USB 3.0 xHCI Controller (rev 31)
00:15.0 Signal processing controller: Intel Corporation Sunrise Point-H Serial IO I2C Controller #0 (rev 31)
00:15.1 Signal processing controller: Intel Corporation Sunrise Point-H Serial IO I2C Controller #1 (rev 31)
00:16.0 Communication controller: Intel Corporation Sunrise Point-H CSME HECI #1 (rev 31)
00:17.0 RAID bus controller: Intel Corporation SATA Controller [RAID mode] (rev 31)
00:1c.0 PCI bridge: Intel Corporation Sunrise Point-H PCI Express Root Port #2 (rev f1)
00:1c.2 PCI bridge: Intel Corporation Sunrise Point-H PCI Express Root Port #3 (rev f1)
00:1c.3 PCI bridge: Intel Corporation Sunrise Point-H PCI Express Root Port #4 (rev f1)
00:1e.0 Signal processing controller: Intel Corporation Sunrise Point-H Serial IO UART #0 (rev 31)
00:1f.0 ISA bridge: Intel Corporation Sunrise Point-H LPC Controller (rev 31)
00:1f.2 Memory controller: Intel Corporation Sunrise Point-H PMC (rev 31)
00:1f.3 Audio device: Intel Corporation Sunrise Point-H HD Audio (rev 31)
00:1f.4 SMBus: Intel Corporation Sunrise Point-H SMBus (rev 31)
**01:00.0 VGA compatible controller: NVIDIA Corporation GM107 [GeForce GTX 750 Ti] (rev a2)**
01:00.1 Audio device: NVIDIA Corporation Device 0fbc (rev a1)
02:00.0 USB controller: ASMedia Technology Inc. ASM1142 USB 3.1 Host Controller
03:00.0 Network controller: Intel Corporation Wireless 3165 (rev 79)
04:00.0 Ethernet controller: Qualcomm Atheros QCA8171 Gigabit Ethernet (rev 10)
Nvidia GeForce GTX 750 Ti 是可以加速深度学习的 GPU 吗？
回答标题中的问题：是的，你有一块 NVIDIA GPU，一块 GeForce GTX 750 Ti。如果您安装了 CUDA，则可以将此 GPU 与 TensorFlow 一起使用（有关可与 CUDA 一起使用的 GPU 的完整列表，请参见此处：https://developer.nvidia.com/cuda-gpus）。可以在此处找到将 TensorFlow 与 GPU 一起使用的先决条件的详细列表：
https://www.tensorflow.org/install/install_linux
玩得开心！
abigail@abilina:~/nlp$ lspci
00:00.0 Host bridge: Intel Corporation Skylake Host Bridge/DRAM Registers (rev 07)
00:01.0 PCI bridge: Intel Corporation Skylake PCIe Controller (x16) (rev 07)
00:02.0 Display controller: Intel Corporation HD Graphics 530 (rev 06)
00:14.0 USB controller: Intel Corporation Sunrise Point-H USB 3.0 xHCI Controller (rev 31)
00:15.0 Signal processing controller: Intel Corporation Sunrise Point-H Serial IO I2C Controller #0 (rev 31)
00:15.1 Signal processing controller: Intel Corporation Sunrise Point-H Serial IO I2C Controller #1 (rev 31)
00:16.0 Communication controller: Intel Corporation Sunrise Point-H CSME HECI #1 (rev 31)
00:17.0 RAID bus controller: Intel Corporation SATA Controller [RAID mode] (rev 31)
00:1c.0 PCI bridge: Intel Corporation Sunrise Point-H PCI Express Root Port #2 (rev f1)
00:1c.2 PCI bridge: Intel Corporation Sunrise Point-H PCI Express Root Port #3 (rev f1)
00:1c.3 PCI bridge: Intel Corporation Sunrise Point-H PCI Express Root Port #4 (rev f1)
00:1e.0 Signal processing controller: Intel Corporation Sunrise Point-H Serial IO UART #0 (rev 31)
00:1f.0 ISA bridge: Intel Corporation Sunrise Point-H LPC Controller (rev 31)
00:1f.2 Memory controller: Intel Corporation Sunrise Point-H PMC (rev 31)
00:1f.3 Audio device: Intel Corporation Sunrise Point-H HD Audio (rev 31)
00:1f.4 SMBus: Intel Corporation Sunrise Point-H SMBus (rev 31)
**01:00.0 VGA compatible controller: NVIDIA Corporation GM107 [GeForce GTX 750 Ti] (rev a2)**
01:00.1 Audio device: NVIDIA Corporation Device 0fbc (rev a1)
02:00.0 USB controller: ASMedia Technology Inc. ASM1142 USB 3.1 Host Controller
03:00.0 Network controller: Intel Corporation Wireless 3165 (rev 79)
04:00.0 Ethernet controller: Qualcomm Atheros QCA8171 Gigabit Ethernet (rev 10)
Nvidia GeForce GTX 750 Ti 是可以加速深度学习的 GPU 吗？
回答标题中的问题：是的，你有一块 NVIDIA GPU，一块 GeForce GTX 750 Ti。如果您安装了 CUDA，则可以将此 GPU 与 TensorFlow 一起使用（有关可与 CUDA 一起使用的 GPU 的完整列表，请参见此处：https://developer.nvidia.com/cuda-gpus）。可以在此处找到将 TensorFlow 与 GPU 一起使用的先决条件的详细列表： https://www.tensorflow.org/install/install_linux 玩得开心！