tkonolige opened a new pull request #6790:
URL: https://github.com/apache/incubator-tvm/pull/6790


   Like #6671 this PR fixes autotvm when using the spawn start method for 
multiprocessing. I've added some tests to make sure that things work with spawn 
in the CI. I've also added notices to the tutorials letting users know that 
they will not run on windows or macOS.
   
   Here are performance results for `tutorials/autotvm/tune_conv2d_cuda.py` 
with n_trials = 100.
   ```
   This PR
   
   XGB iter:   0        tr-a-recall@64: 0.661270        tr-map: 0.100000
   XGB iter:  25        tr-a-recall@64: 0.754100        tr-map: 1.000000
   XGB stopped. Best iteration: [29] tr-a-recall@64:0.75971     tr-map:1.00000
   XGB train: 1.08      obs: 64 error: 43       n_cache: 64
   SA iter: 50  last_update: 49 max-0: 5.39     max-1: 6.08     temp: 0.90      
elapsed: 3.41
   SA iter: 100 last_update: 98 max-0: 5.72     max-1: 6.25     temp: 0.80      
elapsed: 6.82
   SA iter: 150 last_update: 148        max-0: 5.89     max-1: 6.51     temp: 
0.70      elapsed: 10.38
   SA iter: 200 last_update: 197        max-0: 5.97     max-1: 6.51     temp: 
0.60      elapsed: 14.18
   SA iter: 250 last_update: 248        max-0: 6.09     max-1: 6.51     temp: 
0.50      elapsed: 18.02
   SA iter: 300 last_update: 298        max-0: 6.15     max-1: 6.51     temp: 
0.40      elapsed: 21.72
   SA iter: 350 last_update: 348        max-0: 6.17     max-1: 6.51     temp: 
0.30      elapsed: 25.30
   SA iter: 400 last_update: 398        max-0: 6.21     max-1: 6.51     temp: 
0.20      elapsed: 28.79
   SA iter: 450 last_update: 447        max-0: 6.24     max-1: 6.51     temp: 
0.10      elapsed: 32.20
   SA iter: 500 last_update: 493        max-0: 6.25     max-1: 6.51     temp: 
0.00      elapsed: 35.35
   SA iter: 500 last_update: 493        elapsed: 35.35
   
   
   main
   
   XGB iter:   0        tr-a-recall@64: 0.610293        tr-map: 0.500000
   XGB iter:  25        tr-a-recall@64: 0.671535        tr-map: 1.000000
   XGB stopped. Best iteration: [29] tr-a-recall@64:0.67466     tr-map:1.00000
   XGB train: 1.02      obs: 64 error: 50       n_cache: 64
   SA iter: 50  last_update: 49 max-0: 5.41     max-1: 6.23     temp: 0.90      
elapsed: 3.41
   SA iter: 100 last_update: 98 max-0: 5.58     max-1: 6.23     temp: 0.80      
elapsed: 6.94
   SA iter: 150 last_update: 145        max-0: 5.72     max-1: 6.37     temp: 
0.70      elapsed: 10.61
   SA iter: 200 last_update: 198        max-0: 5.76     max-1: 6.37     temp: 
0.60      elapsed: 14.25
   SA iter: 250 last_update: 247        max-0: 5.83     max-1: 6.45     temp: 
0.50      elapsed: 17.87
   SA iter: 300 last_update: 298        max-0: 6.03     max-1: 6.45     temp: 
0.40      elapsed: 21.81
   SA iter: 350 last_update: 349        max-0: 6.07     max-1: 6.45     temp: 
0.30      elapsed: 25.57
   SA iter: 400 last_update: 397        max-0: 6.11     max-1: 6.45     temp: 
0.20      elapsed: 28.99
   SA iter: 450 last_update: 444        max-0: 6.13     max-1: 6.45     temp: 
0.10      elapsed: 32.60
   SA iter: 500 last_update: 489        max-0: 6.14     max-1: 6.45     temp: 
0.00      elapsed: 35.76
   SA iter: 500 last_update: 489        elapsed: 35.76
   ```
   
   This is on a 64-core x86 ubuntu system with python 3.6.9. Performance is the 
same.


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