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p513817 / ampere-tf

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Install Tensorflow in Docker Container For Ampere Arch

Reference

Requirement

  • NVIDIA Driver
  • Docker
  • nvidia-container

Docker Image

nvcr.io/nvidia/cuda:11.2.0-cudnn8-devel-ubuntu18.04

  • Run docker container 
    $ docker run -it --name nv-tf --gpus all \
-v `pwd`:/workspace -w /workspace \
nvcr.io/nvidia/cuda:11.2.0-cudnn8-devel-ubuntu18.04 bash

# Output
Unable to find image 'nvcr.io/nvidia/cuda:11.2.0-cudnn8-devel-ubuntu18.04' locally
11.2.0-cudnn8-devel-ubuntu18.04: Pulling from nvidia/cuda
09db6f815738: Pull complete 
d79696845ef2: Pull complete 
9cace1db9258: Pull complete 
5093a1370488: Pull complete 
affabbc9735b: Pull complete 
839e92906efc: Pull complete 
36d15b49ae4c: Pull complete 
be6750df422d: Pull complete 
02a4c72adbe9: Pull complete 
cc3c4b345c51: Pull complete 
Digest: sha256:ba15e2c49bc81211a26f42e9f21374cb2e9e56a5d7b6ce710cf0291ed880327b
Status: Downloaded newer image for nvcr.io/nvidia/cuda:11.2.0-cudnn8-devel-ubuntu18.04
  • Check CUDA and cuDNN 
    nvcc -V

# Output
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_14_21:12:58_PST_2021
Cuda compilation tools, release 11.2, V11.2.152
Build cuda_11.2.r11.2/compiler.29618528_0

Install NVIDIA Tensorflow

  • Workflow

    apt update
apt install -y python3-dev python3-pip git
pip3 install --upgrade pip setuptools requests

pip3 install nvidia-pyindex

pip3 install nvidia-tensorflow[horovod]

# Output
Successfully installed absl-py-1.2.0 astor-0.8.1 cached-property-1.5.2 cloudpickle-2.1.0 dataclasses-0.8 gast-0.2.2 google-pasta-0.2.0 grpcio-1.47.0 h5py-3.1.0 importlib-metadata-4.8.3 keras-applications-1.0.8 keras-preprocessing-1.1.2 markdown-3.3.7 numpy-1.18.5 nvidia-cublas-11.2.1.74 nvidia-cuda-cupti-11.1.69 nvidia-cuda-nvcc-11.1.74 nvidia-cuda-nvrtc-11.1.105 nvidia-cuda-runtime-11.1.74 nvidia-cudnn-8.0.4.30 nvidia-cufft-10.3.0.74 nvidia-curand-10.2.2.74 nvidia-cusolver-11.0.0.74 nvidia-cusparse-11.2.0.275 nvidia-dali-cuda110-0.27.0 nvidia-dali-nvtf-plugin-0.27.0+nv20.11 nvidia-horovod-0.20.2+nv20.11 nvidia-nccl-2.8.2 nvidia-tensorboard-1.15.0+nv20.11 nvidia-tensorflow-1.15.4+nv20.11 nvidia-tensorrt-7.2.1.6 opt-einsum-3.3.0 protobuf-3.19.4 psutil-5.9.1 pyyaml-6.0 tensorboard-1.15.0 tensorflow-estimator-1.15.1 termcolor-1.1.0 typing-extensions-4.1.1 webencodings-0.5.1 werkzeug-2.0.3 wrapt-1.14.1 zipp-3.6.0

  • Check

    • Tensorflow Version 
      $ python3 -c 'import tensorflow as tf; print(tf.__version__)'

# Output
2022-07-27 03:45:17.524269: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
1.15.4
    • Check GPU could capture in Tensorflow 
      $ python3 -c "import tensorflow as tf; print('Num GPUs Available: ', len(tf.config.experimental.list_physical_devices('GPU')))"

# Output
2022-07-27 03:45:36.465661: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
2022-07-27 03:45:37.229005: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2022-07-27 03:45:37.285247: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1082] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-27 03:45:37.285355: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1665] Found device 0 with properties: 
name: NVIDIA GeForce RTX 3080 Ti major: 8 minor: 6 memoryClockRate(GHz): 1.665
pciBusID: 0000:01:00.0
2022-07-27 03:45:37.285372: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2022-07-27 03:45:37.287393: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2022-07-27 03:45:37.288186: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2022-07-27 03:45:37.288461: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2022-07-27 03:45:37.290611: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11
2022-07-27 03:45:37.291171: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2022-07-27 03:45:37.291294: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2022-07-27 03:45:37.291398: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1082] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-27 03:45:37.291544: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1082] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-27 03:45:37.291616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1793] Adding visible gpu devices: 0
Num GPUs Available:  1

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License:MIT License