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kushaj/pytorch

By kushaj

•Updated almost 5 years ago

ubuntu + sudo + default user + conda + pytorch

Image
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67

kushaj/pytorch repository overview

Check out my blog post Complete tutorial on building images using Docker⁠ that covers everything you need to know to write Dockerfile and run containers using Docker. The base image ubuntu_conda_pytorch⁠ is used as an example in the above post and it contains the details of every command used in the above Dockerfile. Github⁠

⁠Table of Contents

⁠ubuntu_conda_pytorch

Features

  • Builds on top of Ubuntu
  • sudo and a default user
  • Miniconda setup
  • PyTorch and torchvision installed using conda
  • Extra utilities include git, curl, sudo
⁠Build from source Dockerfile

To build the image you can use the following command

> cd ubuntu_conda_pytorch
> docker build -t pytorch:1.9.0 --build-args \
    UBUNTU_VERSION=18.04 \
    USERNAME=default \
    PASSWORD=default \
    MINICONDA_DOWNLOAD_LINK=... \
    MINICONDA_INSTALL_PATH=/home/$USERNAME \
    .

Grab the link of the Linux installer⁠ of your choice from the Miniconda document page and use that link as value of MINICONDA_DOWNLOAD_LINK. You can also specify where you want to download Miniconda using MINICONDA_INSTALL_PATH. By default, it is installed in the home directory of the user.

⁠Pull from Dockerhub

The image is hosted at kushaj/pytorch⁠. To pull the image use the following command

> docker pull kushaj/pytorch:1.9.0
⁠Running a container

Use the following command to create a container

> docker run -it --gpus all --name temp kushaj/pytorch:1.9.0
  • -it will open a terminal connected to the container
  • --gpus all is used to specify which GPUs to give access to the container
  • --name temp name of the container
⁠(Usage) Become root

sudo su can be used to become root. The default credentials are

  • username = default
  • password = default
(base) default@a96e1cef1e4a:~$ sudo su
[sudo] password for default: 
root@a96e1cef1e4a:/home/default#
⁠(Usage) Nvidia GPU

nvidia-smi can be used to check the status of GPUs. This requires the host machine has Nvidia drivers set up.

(base) default@a96e1cef1e4a:~$ nvidia-smi
Thu Oct  7 02:28:18 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03    Driver Version: 460.91.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Quadro M1200        Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   40C    P0    N/A /  N/A |    356MiB /  4043MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
(base) default@a96e1cef1e4a:~$
⁠(Usage) PyTorch example
(base) default@7d9b75595a27:~$ python
Python 3.9.7 (default, Sep 16 2021, 13:09:58) 
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>> torch.backends.cudnn.version()
8005
>>> x = torch.tensor([1,2], device='cuda:0')
>>> x
tensor([1, 2], device='cuda:0')

⁠Customizing base image

You can customize ubuntu_conda_pytorch/Dockerfile⁠ as per your needs

⁠PyTorch CPU only install

You can modify line 41⁠ of the Dockerfile to install PyTorch. You can head to the PyTorch install page⁠ to grab the command to install PyTorch. To install PyTorch for CPU you can change the original command to

> conda install pytorch torchvision cpuonly -c pytorch
⁠fastai install

ubuntu_conda_fastai/Dockerfile⁠ contains the code to install fastai in the base repo. You can pull the image from kushaj/fastai⁠.

To run a container use the following command connecting to port 8888 of the container

> docker run -it --gpus all -p 8888:8888 --name temp kushaj/fastai:latest

⁠License

Apache License v2⁠

Tag summary

Content type

Image

Digest

Size

3.4 GB

Last updated

almost 5 years ago

docker pull kushaj/pytorch:1.9.0