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介绍
- Ubuntu 18.04.5 LTS
- GTX 1070
- TensorFlow 2.4.1
所需软件
安装前
GCC
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| $ gcc --version
Command 'gcc' not found, but can be installed with:
sudo apt install gcc
$ sudo apt install gcc
$ gcc --version
gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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NVIDIA package repositories
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| $ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
$ sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
$ sudo apt-get update
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NVIDIA machine learning
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| $ wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt-get update
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NVIDIA GPU driver
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| $ sudo apt-get install --no-install-recommends nvidia-driver-460
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注:这里需要使用 460 版本,TensorFlow 官网写的是 450,实测失败。
重启并使用以下命令检查 GPU 是否可见。
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| $ nvidia-smi
Mon Apr 5 16:17:17 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce GTX 1070 On | 00000000:01:00.0 On | N/A |
| 0% 48C P8 9W / 180W | 351MiB / 8111MiB | 1% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 997 G /usr/lib/xorg/Xorg 18MiB |
| 0 N/A N/A 1145 G /usr/bin/gnome-shell 53MiB |
| 0 N/A N/A 1353 G /usr/lib/xorg/Xorg 108MiB |
| 0 N/A N/A 1495 G /usr/bin/gnome-shell 83MiB |
| 0 N/A N/A 1862 G ...AAAAAAAAA= --shared-files 82MiB |
+-----------------------------------------------------------------------------+
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| $ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
$ sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
$ sudo apt-get update
# Install development and runtime libraries (~4GB)
$ sudo apt-get install --no-install-recommends \
cuda-11-0 \
libcudnn8=8.0.4.30-1+cuda11.0 \
libcudnn8-dev=8.0.4.30-1+cuda11.0
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TensorRT
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| $ sudo apt-get install -y --no-install-recommends libnvinfer7=7.1.3-1+cuda11.0 \
libnvinfer-dev=7.1.3-1+cuda11.0 \
libnvinfer-plugin7=7.1.3-1+cuda11.0
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Miniconda
从 https://docs.conda.io/en/latest/miniconda.html 下载 Python 3.8 安装脚本。

增加可执行权限
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| $ chmod +x Miniconda3-latest-Linux-x86_64.sh
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执行安装脚本
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| $ ./Miniconda3-latest-Linux-x86_64.sh
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重启终端,激活 conda。
虚拟环境
创建一个名称为 tensorflow
的虚拟环境。
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| $ conda create -n tensorflow python=3.8.5
$ conda activate tensorflow
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安装 TensorFlow
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| $ pip install tensorflow
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验证安装
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| $ python -c "import tensorflow as tf;print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))"
2021-04-05 16:20:00.426536: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-05 16:20:01.170305: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-05 16:20:01.170830: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-04-05 16:20:01.198917: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 16:20:01.199497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7845GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB/s
2021-04-05 16:20:01.199519: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-05 16:20:01.201250: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-04-05 16:20:01.201278: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-04-05 16:20:01.201995: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-04-05 16:20:01.202159: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-04-05 16:20:01.203993: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-04-05 16:20:01.204412: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-04-05 16:20:01.204499: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-04-05 16:20:01.204566: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 16:20:01.204897: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-05 16:20:01.205168: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
Num GPUs Available: 1
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安装 JupyterLab 和 matplotlib
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| $ pip install jupyterlab matplotlib
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在 JupyterLab 中运行 TensorFlow
JupyterLab 将自动在浏览器打开。
从 https://www.tensorflow.org/tutorials/images/cnn 下载并导入 CNN notebook。

执行 Restart Kernel and Run All Cells
。

当训练开始, 检查 GPU 进程,可以看到 ...nvs/tensorflow/bin/python
表示正在使用 GPU 训练模型。

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| $ nvidia-smi
Mon Apr 5 16:36:28 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce GTX 1070 On | 00000000:01:00.0 On | N/A |
| 23% 54C P2 72W / 180W | 7896MiB / 8111MiB | 55% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 997 G /usr/lib/xorg/Xorg 18MiB |
| 0 N/A N/A 1145 G /usr/bin/gnome-shell 73MiB |
| 0 N/A N/A 1353 G /usr/lib/xorg/Xorg 136MiB |
| 0 N/A N/A 1495 G /usr/bin/gnome-shell 53MiB |
| 0 N/A N/A 1862 G ...AAAAAAAAA= --shared-files 99MiB |
| 0 N/A N/A 3181 C ...nvs/tensorflow/bin/python 7507MiB |
+-----------------------------------------------------------------------------+
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延伸阅读
参考链接