become-yukarin-docker

NVIDIA-Docker image for become-yukarin-docker with gpu computing with Python 3.6.6

Warning!! This Dockerfile use auto installation

• This distoribution made at 2018.10.22
• If you build from Dockerfile, the versions will be change.

Installed distribution, tool & package versions

• OS: Ubuntu 16.04.5 LTS (Xenial Xerus)
• CUDA: 9.0.176
• cudnn: 7.3.1
• pyenv 1.2.7
• Python: 3.6.6
• pip: 10.0.1
• Anaconda 4.5.11
• tensorflow: 1.11.0
• tensorflow-gpu: 1.11.0
• keras-gpu: 2.2.4
• cupy-cuda90(cupy alternative): 4.5.0
• chaienr: 4.5.0
• jupyter notebook: 4.4.0

Development host Envirinmenet (For info, it's not prerequrests)

• OS: Ubuntu 18.04.1 LTS (Bionic Beaver)
• NVIDIA Driver: 410.48
• CUDA: 9.0.176
• Docker: 18.06.1
• NVIDIA-Docker: 2.0.3
• GNU Make: 4.1
• Git: 2.17.1

How to use (with Makefile)

If you see this README from outside GitHub, see here https://github.com/YUzushio/become-yukarin-docker

Clone source

git clone https://github.com/YUzushio/become-yukarin-docker

Go to directory

cd become-yukarin-docker

Build

make build

Run

Default: run /bin/bash in container

make run

Chainer GPU test

1. Run into contaienr

make run

2. Go to workspace

cd /workspace

3. run Chainer GPU test

python chinergputest.py

4. Exit

exit

Get become-yukarin source into container

Before clone, you should visit repository and read README on GitHub https://github.com/Hiroshiba/become-yukarin

1. (On host machine) Go to files

   cd files

2. Clone source

git clone https://github.com/Hiroshiba/become-yukarin

3. Go to above directory

cd ../

4. Run into contaienr

make run

5. Go to workspace

cd workspace

6. Run jupyter notebook

   Waning!! Jupyter use port 80

./jupyter.sh

7. Visit your workspace from jupyter notebook page with browser

http://localhost:80