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TensorFlow 的 JupyterLab 环境

TensorFlow 准备 JupyterLab 交互式笔记本环境,方便我们边写代码、边做笔记。

基础环境#

以下是本文的基础环境,不详述安装过程了。

Ubuntu#

CUDA#

  • CUDA 11.2.2
    • cuda_11.2.2_460.32.03_linux.run
  • cuDNN 8.1.1
    • libcudnn8_8.1.1.33-1+cuda11.2_amd64.deb
    • libcudnn8-dev_8.1.1.33-1+cuda11.2_amd64.deb
    • libcudnn8-samples_8.1.1.33-1+cuda11.2_amd64.deb

Anaconda#

conda activate base

安装 JupyterLab#

Anaconda 环境里已有,如下查看版本:

jupyter --version

不然,如下进行安装:

conda install -c conda-forge jupyterlab

安装 TensorFlow#

创建虚拟环境 tf,再 pip 安装 TensorFlow:

# create virtual environmentconda create -n tf python=3.8 -yconda activate tf
# install tensorflowpip install --upgrade pippip install tensorflow

测试:

$ python - <<EOFimport tensorflow as tfprint(tf.__version__, tf.test.is_built_with_gpu_support())print(tf.config.list_physical_devices('GPU'))EOF
2021-04-01 11:18:17.719061: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.02.4.1 True2021-04-01 11:18:18.437590: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set2021-04-01 11:18:18.437998: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.12021-04-01 11:18:18.458471: 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 zero2021-04-01 11:18:18.458996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:pciBusID: 0000:01:00.0 name: GeForce RTX 2060 computeCapability: 7.5coreClock: 1.35GHz coreCount: 30 deviceMemorySize: 5.79GiB deviceMemoryBandwidth: 245.91GiB/s2021-04-01 11:18:18.459034: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.02021-04-01 11:18:18.461332: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.112021-04-01 11:18:18.461362: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.112021-04-01 11:18:18.462072: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.102021-04-01 11:18:18.462200: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.102021-04-01 11:18:18.462745: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.102021-04-01 11:18:18.463241: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.112021-04-01 11:18:18.463353: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.82021-04-01 11:18:18.463415: 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 zero2021-04-01 11:18:18.463854: 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 zero2021-04-01 11:18:18.464170: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Solution: Could not load dynamic library 'libcusolver.so.10'#

cd /usr/local/cuda/lib64sudo ln -sf libcusolver.so.11 libcusolver.so.10

安装 IPython kernel#

在虚拟环境 tf 里,安装 ipykernel 与 Jupyter 交互。

# install ipykernel (conda new environment)conda activate tfconda install ipykernel -ypython -m ipykernel install --user --name tf --display-name "Python TF"
# run JupyterLab (conda base environment with JupyterLab)conda activate basejupyter lab

另一种方式,可用 nb_conda 扩展,其于笔记里会激活 Conda 环境:

# install ipykernel (conda new environment)conda activate tfconda install ipykernel -y
# install nb_conda (conda base environment with JupyterLab)conda activate baseconda install nb_conda -y# run JupyterLabjupyter lab

最后,访问 http://localhost:8888/ :

参考#