Intro
Please check Installing TensorFlow 2 with GPU support on Ubuntu 20.04 LTS for the intro.
According to https://www.tensorflow.org/install/docker and https://github.com/NVIDIA/nvidia-docker the following has to be installed:
- docker:
> 19.03 - NVIDIA GPU drivers
- NVIDIA Container Toolkit
Docker in Ubuntu 20.04 LTS
There are number of docker packages available
docker-cepackage from docker.comdocker.iopackage provided by Canonicaldockerpackage provided by Red Hat
Docker versions > 19.03 are supported by NVIDIA Container Toolkit.
At time of writing the official Ubuntu’s docker.io is the best option to use. Just run
> sudo apt install docker-compose
Check docker version
> docker version
Client:
Version: 19.03.8
API version: 1.40
...
Server:
Engine:
Version: 19.03.8
API version: 1.40 (minimum version 1.12)
...
NVIDIA Container Toolkit
Add the package repositories
> distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
> curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
> curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
At this point I had to clean up /etc/apt/sources.list.d/ folder.
> cd /etc/apt/sources.list.d/; rm docker.list*
Continue installation
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
Validating installation
Run nvidia-smi in a docker
> docker run --gpus all --rm nvidia/cuda nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 107... Off | 00000000:09:00.0 Off | N/A |
| 0% 50C P0 35W / 180W | 0MiB / 8118MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Run TensorFlow in a docker
> docker run -it --rm --gpus all tensorflow/tensorflow:latest-gpu python -c "import tensorflow as tf; tf.config.list_physical_devices('GPU')"
Run benchmark in a docker
> docker run --gpus all -it --rm -v /home/ubuntu/benchmarks:/benchmarks tensorflow/tensorflow:latest-gpu python benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --num_gpus=1 --model resnet50 --batch_size 64
Fix the path /home/ubuntu/benchmarks above to your local folder.
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