dmlc--dgl
Da Zheng
b9e4a5b742
* add tests. * 111 * fix * Update asv.conf.json * fix. * benchmark partition with livejournal. * fix benchmark * fix. * fix. * remove ogb * Revert "Update asv.conf.json" This reverts commit dd327a5564f4ef01795e444e79b17265b9c8b391. * change branch * depend pandas * Revert "change branch" This reverts commit 1d4f93756492a93f2e3cde07229a59a31a8c380b. * Update README.md Co-authored-by: VoVAllen <jz1749@nyu.edu> Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
How to add test to regression
Official link to asv
Add test
DGL reuses the ci docker image for the regression test. There are four conda envs, base, mxnet-ci, pytorch-ci, and tensorflow-ci.
The basic use is execute a script, and get the needed results out of the printed results.
- Create a new file in the tests/regression/
- Follow the example
bench_gcn.pyor the official instruction- function name starts with
trackwill be used to generate the stats, by the return value - setup function would be execute every time before running track function
- Can use params to pass parameter into
setupandtrack_functions
- function name starts with
Run locally
The default regression branch in asv is master. If you need to run on other branch on your fork, please change the branches value in the asv.conf.json at the root of your repo.
bash ./publish.sh <repo> <branch>
The running result will be at ./asv_data/. You can use python -m http.server inside the html folder to start a server to see the result