TensorFlow has prepared the JupyterLab interactive notebook environment for writing code and taking notes at the same time.

Based on the environment

The following is the base environment for this article without going into details about the installation process.

Ubuntu

  • Ubuntu 18.04.5 Beaver (Bionic Beaver)
    • Ubuntu – 18.04.5 – desktop – amd64. Iso

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

  • Anaconda Python 3.8
    • Anaconda3-2020.11 – Linux – x86_64. Sh
conda activate base
Copy the code

Install JupyterLab

The Anaconda environment has the following version:

jupyter --version
Copy the code

Otherwise, install as follows:

conda install -c conda-forge jupyterlab
Copy the code

Install TensorFlow

Create tf virtual environment, PIP install TensorFlow:

# create virtual environmentConda create -n tf python=3.8 -y conda activate TF# install tensorflow
pip install --upgrade pip
pip install tensorflow
Copy the code

Testing:

$ python - <<EOF
import tensorflow as tf
print(tf.__version__, tf.test.is_built_with_gpu_support())
print(tf.config.list_physical_devices('GPU'))
EOF
Copy the code
The 2021-04-01 11:18:17. 719061: I tensorflow stream_executor/platform/default/dso_loader. Cc: 49] Successfully the opened the dynamic library libcudart. So. 11.0 Against 2.4.1 True 11:18:18 2021-04-01. 437590: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices notsetThe 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 nodereadfrom 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: GiB deviceMemoryBandwidth: 245.91GiB/s 2021-04-01 11:18:18.459034: I tensorflow stream_executor/platform/default/dso_loader. Cc: 49] Successfully the opened the dynamic library libcudart. So. 11.0 The 2021-04-01 11:18:18. 461332: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11 The 2021-04-01 11:18:18. 461362: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11 The 2021-04-01 11:18:18. 462072: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10 The 2021-04-01 11:18:18. 462200: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10 The 2021-04-01 11:18:18. 462745: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10 The 2021-04-01 11:18:18. 463241: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11 The 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 noderead 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')]
Copy the code

Solution: Could not load dynamic library ‘libcusolver.so.10’

cd /usr/local/cuda/lib64
sudo ln -sf libcusolver.so.11 libcusolver.so.10
Copy the code

Install IPython kernel

In the tf, ipykernel is installed to interact with Jupyter.

# 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
Copy the code

Alternatively, the nb_conda extension can be used, which activates the Conda environment in notes:

# 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
Copy the code

Finally, visit http://localhost:8888/ :

reference

  • Install TensorFlow 2
    • Build from source
    • GPU support
  • Install TensorFlow – Anaconda
    • anaconda / packages / tensorflow
  • Installing the IPython kernel

GoCoding personal practice experience sharing, please pay attention to the public account!