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• Docker
• Python3
• Optional: Nvidia-Docker (Ubuntu only, for GPU tests) 
• Optional: GPU with Cuda Compute Capability ≥ 3.0

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• Python packages: defined in ci/requirements.txt 

pip3 install -r ci/requirements.txt

• Disk space: at least 100GB (150GB recommended)

This part explains what commands to run in order to reproduce a failure at each stage.

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In case you run into any issues, please try the following steps:

Cleaning the workspace

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(including subrepos, be careful with data loss)

ci/docker/runtime_functions.sh clean_repo

Using signal handler to get stack traces:

Use -DUSE_SIGNAL_HANDLER=ON  and maybe also -DCMAKE_BUILD_TYPE=Debug as CMake arguments, you can edit ci/docker/runtime_functions.sh and change it to build with these options if they are not set.

Run make clean in the root of your MXNet workspace to remove all artefacts. Afterwards, continue with the build step to regenerate them. The result would look like follows:

Image Removed

Stepping into the container

It is possible to step into the container to run commands manually. This can be achieved by modifying the previously listed command in the style of

ci/build.py --nvidiadocker --platform ubuntu_gpu /work/runtime_functions.sh unittest_ubuntu_python2_gpu

to

ci/build.py --nvidiadocker --platform ubuntu_gpu -i

This is effectively done by removing everything related to the runtime function and replacing it with the command -i  In the output of the script, the docker command that is needed is printed which sets up all the needed docker options. You can replace the final script with /bin/bash or nothing to get a shell in the container.