Jupyterlab environment of tensorflow

Time:2021-4-21

Tensorflow prepares jupyterlab interactive notebook environment, which is convenient for us to write code and take notes at the same time.

Basic environment

The following is the basic environment of this article, do not detail the installation process.

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

Install jupyterlab

The version is available in Anaconda environment as follows:

jupyter --version

Otherwise, install as follows:

conda install -c conda-forge jupyterlab

Install tensorflow

Create virtual environmenttf, againpipTo install tensorflow:

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

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

Test:

$ 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

Install IPython kernel

In a virtual environmenttfInside, installipykernelInteract 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

<!–
jupyter kernelspec list
jupyter kernelspec remove tf
–>

Another way, availablenb_condaExtension, which activates the CONDA environment in the note:

# 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

Finally, visithttp://localhost:8888/

Jupyterlab environment of tensorflow

reference resources

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