deepo Documentation

Quick Start

GPU Version Copy Chapter

Installation Copy Section

Step 1. Install [Docker](https://docs.docker.com/engine/installation/) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).

Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)

docker pull ufoym/deepo

Usage Copy Section

Verify that GPU access works inside a container:

docker run --gpus all --rm ufoym/deepo nvidia-smi

CPU Version Copy Chapter

Installation Copy Section

Step 1. Install [Docker](https://docs.docker.com/engine/installation/).

Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)

docker pull ufoym/deepo:cpu

Usage Copy Section

Launch an interactive shell:

docker run -it ufoym/deepo:cpu bash

To share data and configuration between the host (your machine or VM) and the container, use the -v option:

docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash

This makes /host/data on the host visible as /data inside the container, and /host/config as /config. This isolation helps prevent containerized experiments from accidentally overwriting or reading the wrong data.

Note that some frameworks (e.g., PyTorch) use shared memory for inter-process communication. If you use multiprocessing, the container's default shared memory size may be insufficient. Increase it with --ipc=host or --shm-size:

docker run -it --ipc=host ufoym/deepo:cpu bash

You are now ready to begin your journey.

$ python

>>> import tensorflow
>>> import torch
>>> import keras
>>> import mxnet
>>> import chainer
>>> import paddle

$ darknet

usage: darknet <function>

Customization

Unhappy with all-in-one solution? Copy Chapter

If you prefer a single framework instead of the all-in-one image, simply append a tag with the framework name.
For example, to pull TensorFlow only:

docker pull ufoym/deepo:tensorflow

Jupyter Support Copy Chapter

Step 1. Pull the all-in-one image

docker pull ufoym/deepo

Step 2. Run the image

docker run --gpus all -it -p 8888:8888 -v /home/u:/root --ipc=host ufoym/deepo jupyter lab --no-browser --ip=0.0.0.0 --allow-root --LabApp.allow_origin='*' --LabApp.root_dir='/root'

Build Your Own Customized Image with Lego-like Modules Copy Chapter

Step 1. Set up the generator

git clone https://github.com/ufoym/deepo.git
cd deepo/generator

Step 2. Generate a customized Dockerfile

For example, to create an image with pytorch and keras:

python generate.py Dockerfile pytorch keras

Or with CUDA 11.3 and cuDNN 8:

python generate.py Dockerfile pytorch keras --cuda-ver 11.3.1 --cudnn-ver 8

This generates a Dockerfile with everything needed to build pytorch and keras. The generator automatically resolves dependencies and topologically sorts them, so you don't need to worry about missing packages or ordering.

You can also specify the Python version:

python generate.py Dockerfile pytorch keras python==3.8

Step 3. Build the Dockerfile

docker build -t my/deepo .

This may take several minutes, as some libraries are compiled from source.

Comparison to Alternatives

Tags

Available Tags Copy Chapter

Deprecated Tags Copy Chapter

Citation

@misc{ming2017deepo,
    author = {Ming Yang},
    title = {Deepo: Set up a deep learning environment with a single command line.},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/ufoym/deepo}}
}

Contributing

We appreciate all contributions. If you are planning to contribute bug fixes, please go ahead and open a pull request directly. If you plan to contribute new features, utility functions, or extensions, please open an issue first to discuss your idea with us.

Licensing

Quick Start

GPU Version Copy Chapter

Installation Copy Section

Step 1. Install [Docker](https://docs.docker.com/engine/installation/) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).

Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)

docker pull ufoym/deepo

Usage Copy Section

Verify that GPU access works inside a container:

docker run --gpus all --rm ufoym/deepo nvidia-smi

CPU Version Copy Chapter

Installation Copy Section

Step 1. Install [Docker](https://docs.docker.com/engine/installation/).

Step 2. Pull the all-in-one image from [Docker Hub](https://hub.docker.com/r/ufoym/deepo)

docker pull ufoym/deepo:cpu

Usage Copy Section

Launch an interactive shell:

docker run -it ufoym/deepo:cpu bash

To share data and configuration between the host (your machine or VM) and the container, use the -v option:

docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash

This makes /host/data on the host visible as /data inside the container, and /host/config as /config. This isolation helps prevent containerized experiments from accidentally overwriting or reading the wrong data.

Note that some frameworks (e.g., PyTorch) use shared memory for inter-process communication. If you use multiprocessing, the container's default shared memory size may be insufficient. Increase it with --ipc=host or --shm-size:

docker run -it --ipc=host ufoym/deepo:cpu bash

You are now ready to begin your journey.

$ python

>>> import tensorflow
>>> import torch
>>> import keras
>>> import mxnet
>>> import chainer
>>> import paddle

$ darknet

usage: darknet <function>

Customization

Unhappy with all-in-one solution? Copy Chapter

If you prefer a single framework instead of the all-in-one image, simply append a tag with the framework name.
For example, to pull TensorFlow only:

docker pull ufoym/deepo:tensorflow

Jupyter Support Copy Chapter

Step 1. Pull the all-in-one image

docker pull ufoym/deepo

Step 2. Run the image

docker run --gpus all -it -p 8888:8888 -v /home/u:/root --ipc=host ufoym/deepo jupyter lab --no-browser --ip=0.0.0.0 --allow-root --LabApp.allow_origin='*' --LabApp.root_dir='/root'

Build Your Own Customized Image with Lego-like Modules Copy Chapter

Step 1. Set up the generator

git clone https://github.com/ufoym/deepo.git
cd deepo/generator

Step 2. Generate a customized Dockerfile

For example, to create an image with pytorch and keras:

python generate.py Dockerfile pytorch keras

Or with CUDA 11.3 and cuDNN 8:

python generate.py Dockerfile pytorch keras --cuda-ver 11.3.1 --cudnn-ver 8

This generates a Dockerfile with everything needed to build pytorch and keras. The generator automatically resolves dependencies and topologically sorts them, so you don't need to worry about missing packages or ordering.

You can also specify the Python version:

python generate.py Dockerfile pytorch keras python==3.8

Step 3. Build the Dockerfile

docker build -t my/deepo .

This may take several minutes, as some libraries are compiled from source.

Comparison to Alternatives

Tags

Available Tags Copy Chapter

Deprecated Tags Copy Chapter

Citation

@misc{ming2017deepo,
    author = {Ming Yang},
    title = {Deepo: Set up a deep learning environment with a single command line.},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/ufoym/deepo}}
}

Contributing

We appreciate all contributions. If you are planning to contribute bug fixes, please go ahead and open a pull request directly. If you plan to contribute new features, utility functions, or extensions, please open an issue first to discuss your idea with us.

Licensing