75afcd766e0e45fdb8bd3007b3114257f11a7ec4,tutorials/auto_scheduler/tune_conv2d_layer_cuda.py,,,#,70

Before Change



// Rebuild the binary. This shows how you can apply the best schedule from a
// log file without reruning the search again.
sch, args = task.compute_dag.apply_steps_from_state(inp.state)
func = tvm.build(sch, args, target)

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

After Change


// file and apply it.

// Run auto-tuning (search)
task.tune(tune_option)
// Apply the best schedule
sch, args = task.apply_best(log_file)

// Kill the measurement process
Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 3

Instances


Project Name: apache/incubator-tvm
Commit Name: 75afcd766e0e45fdb8bd3007b3114257f11a7ec4
Time: 2020-12-04
Author: lianminzheng@gmail.com
File Name: tutorials/auto_scheduler/tune_conv2d_layer_cuda.py
Class Name:
Method Name:


Project Name: pymc-devs/pymc3
Commit Name: 82003ed0f58740b30ef314184b0ff3b9814e107c
Time: 2013-05-01
Author: chris.fonnesbeck@vanderbilt.edu
File Name: pymc/step_methods/metropolis.py
Class Name: Metropolis
Method Name: astep


Project Name: apache/incubator-tvm
Commit Name: 75afcd766e0e45fdb8bd3007b3114257f11a7ec4
Time: 2020-12-04
Author: lianminzheng@gmail.com
File Name: tutorials/auto_scheduler/tune_matmul_x86.py
Class Name:
Method Name: