查看cuda版本以及驅動安裝

!nvcc?-V

!dpkg?--list?|?grep?nvidia-*

運行結果如下:

nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation Built on Sat_Aug_25_21:08:01_CDT_2018 Cuda compilation tools, release 10.0, V10.0.130 ii libnvidia-cfg1-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-cfg1-430 ii libnvidia-cfg1-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVIDIA binary OpenGL/GLX configuration library ii libnvidia-common-418 430.50-0ubuntu0.18.04.1 all Transitional package for libnvidia-common-430 ii libnvidia-common-430 430.50-0ubuntu0.18.04.1 all Shared files used by the NVIDIA libraries ii libnvidia-compute-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-compute-430 ii libnvidia-compute-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVIDIA libcompute package ii libnvidia-decode-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-decode-430 ii libnvidia-decode-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVIDIA Video Decoding runtime libraries ii libnvidia-encode-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-encode-430 ii libnvidia-encode-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVENC Video Encoding runtime library ii libnvidia-fbc1-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-fbc1-430 ii libnvidia-fbc1-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVIDIA OpenGL-based Framebuffer Capture runtime library ii libnvidia-gl-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-gl-430 ii libnvidia-gl-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVIDIA OpenGL/GLX/EGL/GLES GLVND libraries and Vulkan ICD ii libnvidia-ifr1-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for libnvidia-ifr1-430 ii libnvidia-ifr1-430:amd64 430.50-0ubuntu0.18.04.1 amd64 NVIDIA OpenGL-based Inband Frame Readback runtime library ii nvidia-compute-utils-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for nvidia-compute-utils-430 ii nvidia-compute-utils-430 430.50-0ubuntu0.18.04.1 amd64 NVIDIA compute utilities ii nvidia-dkms-418 430.50-0ubuntu0.18.04.1 amd64 Transitional package for nvidia-dkms-430 ii nvidia-dkms-430 430.50-0ubuntu0.18.04.1 amd64 NVIDIA DKMS package ii nvidia-driver-418 430.50-0ubuntu0.18.04.1 amd64 Transitional package for nvidia-driver-430 ii nvidia-driver-430 430.50-0ubuntu0.18.04.1 amd64 NVIDIA driver metapackage ii nvidia-kernel-common-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for nvidia-kernel-common-430 ii nvidia-kernel-common-430 430.50-0ubuntu0.18.04.1 amd64 Shared files used with the kernel module ii nvidia-kernel-source-418 430.50-0ubuntu0.18.04.1 amd64 Transitional package for nvidia-kernel-source-430 ii nvidia-kernel-source-430 430.50-0ubuntu0.18.04.1 amd64 NVIDIA kernel source package ii nvidia-modprobe 418.87.01-0ubuntu1 amd64 Load the NVIDIA kernel driver and create device files ii nvidia-opencl-dev:amd64 9.1.85-3ubuntu1 amd64 NVIDIA OpenCL development files ii nvidia-settings 418.87.01-0ubuntu1 amd64 Tool for configuring the NVIDIA graphics driver ii nvidia-utils-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for nvidia-utils-430 ii nvidia-utils-430 430.50-0ubuntu0.18.04.1 amd64 NVIDIA driver support binaries ii xserver-xorg-video-nvidia-418:amd64 430.50-0ubuntu0.18.04.1 amd64 Transitional package for xserver-xorg-video-nvidia-430 ii xserver-xorg-video-nvidia-430 430.50-0ubuntu0.18.04.1 amd64 NVIDIA binary Xorg driver

###############################查看CPU##############################

!cat /proc/cpuinfo | grep "cpu cores" | uniq

!cat /proc/cpuinfo |grep "processor"|wc -l

運行后會發現是雙核四線程

?

###############################查看GPU##############################

1.代碼執行程序->更改運行時類型

2.

打開GPU以后再運行下述命令才會有運行結果.

?

命令:

!apt install lshw -y

!lshw -C display

結果:

*-displaydescription: 3D controllerproduct: GK210GL [Tesla K80]vendor: NVIDIA Corporationphysical id: 4bus info: pci@0000:00:04.0version: a1width: 64 bitsclock: 33MHzcapabilities: msi pm bus_master cap_listconfiguration: driver=nvidia latency=0resources: iomemory:80-7f iomemory:c0-bf irq:37 memory:fc000000-fcffffff memory:800000000-bffffffff memory:c00000000-c01ffffff ioport:c000(size=128)

------------------------------------------------------------------------------------

命令:

!lspci

結果:

00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] (rev 02) 00:01.0 ISA bridge: Intel Corporation 82371AB/EB/MB PIIX4 ISA (rev 03) 00:01.3 Bridge: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 03) 00:03.0 Non-VGA unclassified device: Red Hat, Inc. Virtio SCSI 00:04.0 3D controller: NVIDIA Corporation GK210GL [Tesla K80] (rev a1) 00:05.0 Ethernet controller: Red Hat, Inc. Virtio network device

所以根據文檔[1]可知,Google的colab是不支持lightgbm的加速的.

所以colab中,CPU版本的lightgbm運行速度比GPU版本的lightgbm運行速度更快

?

##############################開啟最大內存######################################

默認內存是12GB,下面是開啟25GB的方法:

先把內存耗盡,然后colab就會彈出提升內存的選項:

耗盡內存的代碼如下:

lists=True
while True:
? ? lists.append(1111111111111111111111)

另外:

如果想要300多G的硬盤,必須設置設備為GPU,python3

?

?

Reference:

[1]https://lightgbm.readthedocs.io/en/latest/GPU-Performance.html

總結

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