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Last pushed: 4 months ago
Short Description
Ubuntu Core 14.04 + CUDA + Pycaffe + DIGITS.
Full Description


Ubuntu Core 14.04 + CUDA 7.5 + cuDNN v5 + Caffe (NVIDIA fork) + DIGITS.



Use NVIDIA Docker: nvidia-docker run -dP kaixhin/cuda-digits.

For automatically mapping the DIGITS server port use nvidia-docker run -dP kaixhin/cuda-digits and docker port <id> to retrieve the port.
For specifying the port manually use nvidia-docker run -d -p <port>:5000 kaixhin/cuda-digits.
The shell can be entered as usual using nvidia-docker run -it kaixhin/cuda-digits bash.

For more information on CUDA on Docker, see the repo readme.


If you find this useful in research please consider citing this work.

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Comments (5)
4 months ago

I did the following to fix the issue people have had in previous comments [ERROR] Train Caffe Model: Check failed: error == cudaSuccess (8 vs. 0) invalid device function:

nvidia-docker run -it -p 5000:5000 kaixhin/cuda-digits bash

cd to ~/caffe/build
make test
make runtest

Should be good to go after that. I tested DIGITS on the mnist dataset. You can get that by doing:
cd ~/digits
python -m digits.download_data mnist ~/mnist


follow the DIGITS walkthrough from there and you should be good. I made a commit of the image and use that so I do not have to run the caffe test each time.

9 months ago


Thanks very much for this very valuable project! I am using your Digits CUDA Docker image for medical imaging research at Mayo Clinic.

However, I have run into the issue mentioned by ten2net earlier:

[ERROR] Train Caffe Model: Check failed: error == cudaSuccess (8 vs. 0) invalid device function

I did some research, and apparently this error indicates that code for my GPU architecture is not present in your Caffe build.

I am using a Pascal GPU, a GTX 1080. This device appears to have an arch code of 61, i.e. The following flags should be passed to nvcc when compiling:

-gencode arch=compute_61,code=compute_61

I checked the build logs for your latest Docker image, and saw the following entries:

-- Automatic GPU detection failed. Building for all known architectures.

-- Added CUDA NVCC flags for: sm_20 sm_21 sm_30 sm_35 sm_50

Would it be possible to add sm_61 to the list of known architectures for your build?

Any help appreciated.


Ross Mitchell

10 months ago

maybe json parse error. UI display raw json

{[ jobs = (jc.model_jobs | filter:search_text |; '' ]}

{[enabled = any_selected();'']} {[group_enabled = && enabled;'']}

10 months ago

[ERROR] Train Caffe Model: Check failed: error == cudaSuccess (8 vs. 0) invalid device function

10 months ago

not support multi-GPU :[ERROR] Train Caffe Model: USE_NCCL := 1 must be specified for multi-GPU