AWS で docker から pytorch 1.5.0 (GPU) を設定する方法
Overview
- check host machine
- installation on nvidia-driver / docker with nvidia docker
check host machine
ubuntu@:~$ lspci | grep -i nvidia
00:1e.0 3D controller: NVIDIA Corporation GK210GL [Tesla K80] (rev a1)
ubuntu@:~$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 16.04.4 LTS
Release: 16.04
Codename: xenial
ubuntu@:~$ lspci | grep -i nvidia
00:1e.0 3D controller: NVIDIA Corporation GK210GL [Tesla K80] (rev a1)
ubuntu@:~$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 16.04.4 LTS
Release: 16.04
Codename: xenial
In my case, I need to change locale back to English
sudo update-locale LANG=C.UTF-8
installation on nvidia-driver / docker with nvidia docker
- nvidia-driver should be compatible with gpu impelented + latest version for pytorch + tensorflow version
- this case we'd like to install the driver for tesla k80 / pytorch 1.5.0
- docker was upgraded to include gpu connection natively. don't have to use nvidia-docke
- please follow this and install nvidia-driver. in my case, the version is 440
(https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html).
you can check whether it's installed by nvidia-smi.
ubuntu@:~/MyDockerMLenv$ nvidia-smi
Tue May 5 16:37:09 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 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 Tesla K80 Off | 00000000:00:1E.0 Off | 0 |
| N/A 47C P0 54W / 149W | 262MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
please install docker via apt-get (https://docs.docker.com/engine/install/ubuntu)
bash
ubuntu@:~/MyDockerMLenv$ docker --version
Docker version 19.03.8, build afacb8b7f0
then you can create some docker image and container for pytorch + tensorflow
we don't have the security access (8888 is opened by default)
i recommend you to open port 8888 and 6006 for jupyterlab and tensorboard
you can use --gpus option when you run docker container.
See the background here. (https://qiita.com/ksasaki/items/b20a785e1a0f610efa08)
ubuntu@:~/MyDockerMLenv$ nvidia-smi
Tue May 5 16:37:09 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 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 Tesla K80 Off | 00000000:00:1E.0 Off | 0 |
| N/A 47C P0 54W / 149W | 262MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
please install docker via apt-get (https://docs.docker.com/engine/install/ubuntu)
bash
ubuntu@:~/MyDockerMLenv$ docker --version
Docker version 19.03.8, build afacb8b7f0
then you can create some docker image and container for pytorch + tensorflow
we don't have the security access (8888 is opened by default)
i recommend you to open port 8888 and 6006 for jupyterlab and tensorboard
you can use --gpus option when you run docker container.
See the background here. (https://qiita.com/ksasaki/items/b20a785e1a0f610efa08)
Author And Source
この問題について(AWS で docker から pytorch 1.5.0 (GPU) を設定する方法), 我々は、より多くの情報をここで見つけました https://qiita.com/x37/items/c022931674f52b4ed8e0著者帰属：元の著者の情報は、元のURLに含まれています。著作権は原作者に属する。
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