Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Setup and customize deep learning environment in seconds.
| Date | Stars |
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[](https://hub.docker.com/r/ufoym/deepo)
---
***Deepo*** is an open framework for painlessly assembling specialized [*Docker*](http://www.docker.com/) images for deep learning research. It provides a "Lego set" of dozens of standard components for preparing deep learning tools, along with a framework for composing them into custom Docker images.
At the core of Deepo is a Dockerfile generator that
- lets you [customize your deep learning environment](#Build) with Lego-like modules
- describe your environment in a single command line
- Deepo generates Dockerfiles following best practices
- and handles all the configuration for you
- automatically resolves dependencies
- Deepo knows which combinations of CUDA, cuDNN, Python, PyTorch, TensorFlow, etc. are compatible
- picks the right versions on your behalf
- and determines the correct installation order via [topological sorting](https://en.wikipedia.org/wiki/Topological_sorting)
We also provide a series of pre-built Docker images that
- let you instantly set up common deep learning research environments
- support widely used [deep learning frameworks](#Available-tags)
- support [GPU acceleration](#GPU) (CUDA and cuDNN included) and also work in [CPU-only mode](#CPU)
- run on Linux ([CPU](#CPU)/[GPU](#GPU)), Windows ([CPU](#CPU)), and macOS ([CPU](#CPU))
---
# Table of contents
- [Quick Start](#Quick-Start)
- [GPU Version](#GPU)
- [Installation](#Installation)
- [Usage](#Usage)
- [CPU Version](#CPU)
- [Installation](#Installation-cpu)
- [Usage](#Usage-cpu)
- [Customization](#Customization)
- [Unhappy with all-in-one solution?](#One)
- [Jupyter support](#Jupyter)
- [Build your own customized image](#Build)
- [Comparison to Alternatives](#Comparison)
- [Tags](#Tags)
- [Available Tags](#Available-tags)
- [Deprecated Tags](#Deprecated-tags)
- [Citation](#Citation)
- [Contributing](#Contributing)
- [Licensing](#Licensing)
---
<a name="Quick-Start"/>
# Quick Start
<a name="GPU"/>
## GPU Version
<a name="Installation"/>
### Installation
#### 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)
```bash
docker pull ufoym/deepo
```
<a name="Usage"/>
### Usage
Verify that GPU access works inside a container:
```bash
docker run --gpus all --rm ufoym/deepo nvidia-smi
```
If this does not work, check [the issues section of the NVIDIA Container Toolkit GitHub](https://github.com/NVIDIA/nvidia-container-toolkit/issues) — many solutions are already documented. To launch an interactive shell in a persistent container:
```bash
docker run --gpus all -it ufoym/deepo bash
```
To share data and configuration between the host (your machine or VM) and the container, use the `-v` option:
```bash
docker run --gpus all -it -v /host/data:/data -v /host/config:/config ufoym/deepo 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`:
```bash
docker run --gpus all -it --ipc=host ufoym/deepo bash
```
<a name="CPU"/>
## CPU Version
<a name="Installation-cpu"/>
### Installation
#### Step 1. Install [Docker](Excerpt of 16,835 characters
Read on GitHub
Ming · CVTE Research
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Keith Mattix II · @solo-io · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8fa4969d89bb9907, topic:deep-learning, topic:pytorch, topic:tensorflow