junrushao commented on code in PR #12866:
URL: https://github.com/apache/tvm/pull/12866#discussion_r979183312
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src/meta_schedule/task_scheduler/gradient_based.cc:
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@@ -61,22 +61,43 @@ class GradientBasedNode final : public TaskSchedulerNode {
int total_trials = 0;
double total_latency = 0.0;
support::TablePrinter p;
- p.Row() << "ID"
- << "Name"
- << "FLOP"
- << "Weight"
- << "Speed (GFLOPS)"
- << "Latency (us)"
- << "Weighted Latency (us)"
- << "Trials"
- << "Terminated";
+
+ if (using_ipython()) {
+ p.Row() << "ID"
+ << "Name"
+ << "FLOP"
+ << "Weight"
+ << "GFLOPS"
+ << "Latency (us)"
+ << "Wtd. Latency"
+ << "Trials"
+ << "Terminated";
+ } else {
+ p.Row() << "ID"
+ << "Name"
+ << "FLOP"
+ << "Weight"
+ << "Speed (GFLOPS)"
+ << "Latency (us)"
+ << "Weighted Latency (us)"
+ << "Trials"
+ << "Terminated";
+ }
+
p.Separator();
+
for (int i = 0; i < n_tasks; ++i) {
const TaskRecord& record = task_records_[i];
auto row = p.Row();
int trials = record.trials;
+ String task_name = record.task->task_name.value();
+ if (using_ipython() && task_name.length() > 23) {
+ std::string temp = task_name.c_str();
+ temp = temp.substr(0, 20) + "...";
+ task_name = String(temp);
+ }
Review Comment:
This is indeed not ideal. We may have an extra API that allows us to
assemble a pandas dataframe so that it’s more organically integrated with
jupyter. I have an idea and will communicate with Xiyou next week
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