Среда TensorFlow JupyterLab

TensorFlow

TensorFlow готовит интерактивную среду для ноутбуков JupyterLab, в которой нам удобно писать код и делать заметки.

основная среда

Ниже приведена базовая среда этой статьи, и процесс установки подробно описываться не будет.

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

Установить JupyterLab

Он уже есть в среде Anaconda, проверьте версию следующим образом:

jupyter --version

В противном случае установите его следующим образом:

conda install -c conda-forge jupyterlab

Установить ТензорФлоу

Создайте виртуальную средуtf,СноваpipУстановите ТензорФлоу:

# create virtual environment
conda create -n tf python=3.8 -y
conda activate tf

# install tensorflow
pip install --upgrade pip
pip install tensorflow

контрольная работа:

$ python - <<EOF
import tensorflow as tf
print(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.11.0
2.4.1 True
2021-04-01 11:18:18.437590: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-01 11:18:18.437998: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-04-01 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 zero
2021-04-01 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: 7.5
coreClock: 1.35GHz coreCount: 30 deviceMemorySize: 5.79GiB deviceMemoryBandwidth: 245.91GiB/s
2021-04-01 11:18:18.459034: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-01 11:18:18.461332: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-04-01 11:18:18.461362: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-04-01 11:18:18.462072: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-04-01 11:18:18.462200: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-04-01 11:18:18.462745: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-04-01 11:18:18.463241: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-04-01 11:18:18.463353: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-04-01 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 zero
2021-04-01 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 zero
2021-04-01 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/lib64
sudo ln -sf libcusolver.so.11 libcusolver.so.10

Установите ядро ​​IPython

в виртуальной средеtf, установитьipykernelВзаимодействуйте с Юпитером.

# install ipykernel (conda new environment)
conda activate tf
conda install ipykernel -y
python -m ipykernel install --user --name tf --display-name "Python TF"

# run JupyterLab (conda base environment with JupyterLab)
conda activate base
jupyter lab

Другой способ, доступныйnb_condaРасширение, которое активирует среду Conda в заметке:

# install ipykernel (conda new environment)
conda activate tf
conda install ipykernel -y

# install nb_conda (conda base environment with JupyterLab)
conda activate base
conda install nb_conda -y
# run JupyterLab
jupyter lab

Наконец, посетитеhttp://localhost:8888/:

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