Deep learning’s dependency hell, bottled

What it does

This is a kitchen-sink Ubuntu 14.04 Docker image preloaded with the major deep learning frameworks—TensorFlow, Caffe, Theano, Torch, Keras, and Lasagne—alongside Jupyter and iTorch notebooks, OpenCV, and the standard Python scientific stack. It comes in CPU and GPU variants; the GPU version bundles CUDA 8.0 and cuDNN v5 so only host NVIDIA drivers are needed.

The interesting bit

The author openly admits this violates Docker best practice—purists prefer one container per framework—but argues that deep learning research often requires hopping between ecosystems, such as loading a pretrained Caffe model and converting it to TensorFlow. The GPU image cannot use Docker Hub automated builds due to timeout restrictions, forcing a local build that compiles libraries from scratch for an hour or two.

Key highlights

• Multi-framework monolith: TensorFlow, Caffe, Torch, Theano, Keras, and Lasagne coexist in one image
• GPU support included: CUDA 8.0 and cuDNN v5 are baked in; only host NVIDIA drivers required
• Preconfigured notebooks: Jupyter with both Python and iTorch kernels ready
• Cross-platform CPU support: runs on Linux, Windows, and OS X (GPU is Linux-only)
• Honest scope: the README explicitly warns that single-framework containers are cleaner if you don’t need the kitchen sink

Caveats

• GPU automated builds are missing due to Docker Hub timeout limits, so the GPU version requires a lengthy local build
• Based on Ubuntu 14.04 with CUDA 8.0 and cuDNN v5
• The README itself notes that cramming everything together increases the risk of dependency clashes compared to isolated containers

Verdict

Useful for researchers or students who need to experiment across multiple frameworks without local installation headaches. Skip it if you only need one framework or a modern, up-to-date base image.