xwjiang2010 75027eb479 [air/benchmarks] train/tune benchmark (#26564) ... Making sure that tuning multiple trials in parallel is not significantly slower than training each individual trials. Some overhead is expected. Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com> Signed-off-by: Richard Liaw <rliaw@berkeley.edu> Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: Jimmy Yao <jiahaoyao.math@gmail.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Kai Fricke <kai@anyscale.com>		2022-07-19 18:24:39 +01:00
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workloads	[air/benchmarks] train/tune benchmark (#26564)	2022-07-19 18:24:39 +01:00
app_config.yaml	[air] Add AIR distributed training benchmark for Torch FashionMNIST (#26436)	2022-07-13 10:53:24 +01:00
compute_cpu_1.yaml	[air] Add AIR distributed training benchmark for Torch FashionMNIST (#26436)	2022-07-13 10:53:24 +01:00
compute_cpu_4.yaml	[air] Add AIR distributed training benchmark for Torch FashionMNIST (#26436)	2022-07-13 10:53:24 +01:00
compute_cpu_8.yaml	[air/benchmarks] train/tune benchmark (#26564)	2022-07-19 18:24:39 +01:00
compute_gpu_1.yaml	[AIR][CUJ] Make distributing training benchmark at silver tier (#26640)	2022-07-17 22:07:09 -07:00
compute_gpu_2x2.yaml	[air/benchmark] Torch benchmarks for 4x4 (#26692)	2022-07-19 17:06:37 +01:00
compute_gpu_4_g4_12xl.yaml	[air/benchmarks] train/tune benchmark (#26564)	2022-07-19 18:24:39 +01:00
compute_gpu_4x4.yaml	[air/benchmark] Torch benchmarks for 4x4 (#26692)	2022-07-19 17:06:37 +01:00
compute_gpu_16.yaml	[AIR][CUJ] Make distributing training benchmark at silver tier (#26640)	2022-07-17 22:07:09 -07:00
data_20_nodes.yaml	[air] add bulk ingest benchmarks (#26618)	2022-07-15 22:01:23 -07:00
xgboost_app_config.yaml	[air] Add xgboost release test for silver tier(10-node case). (#26460)	2022-07-15 13:21:10 -07:00
xgboost_compute_tpl.yaml	[air] Add xgboost release test for silver tier(10-node case). (#26460)	2022-07-15 13:21:10 -07:00