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# Building a Development Container | ||
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First, download any dependencies required in `ci/install_deps.sh`. | ||
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Build a test image | ||
``` | ||
docker build -f amd64_10-1.dockerfile -t npt-dev . | ||
``` | ||
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Run an image | ||
``` | ||
docker run -it --rm npt-dev | ||
``` | ||
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Delete all unused docker data: | ||
``` | ||
docker prune system | ||
``` | ||
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## Travis | ||
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CI is done through travis-ci.com. | ||
Travis builds the example code, as well as all docker images. | ||
Carl Pearson's docker hub account is used to push images up to [cwpearson/nvidia-performance-tools on Docker Hub](https://hub.docker.com/repository/docker/cwpearson/nvidia-performance-tools). | ||
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## Resources | ||
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* [Nvidia Docker Image Definitions](https://gitlab.com/nvidia/container-images/cuda/) | ||
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## Roadmap | ||
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* [ ] Using Nsight Compute and Nsight Systems on EWS | ||
* [ ] Instructions for remote profiling | ||
* [ ] Nsight Systems: How to load missing source file | ||
* [ ] Definitions for Various Performance Terms | ||
* [ ] Occupancy | ||
* [ ] Memory Hierarchy | ||
* [ ] Scheduling | ||
* [ ] Stall reasons | ||
* [ ] cudaStreams, cudaEvents | ||
* [ ] CUDA Event and Stream timing examples | ||
* [ ] single-device | ||
* [ ] multi-device |
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