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How to use prepare_stepper method in yandex-tank

Best Python code snippet using yandex-tank

pipeline.py

Source:pipeline.py

...136        ds, g_snapshots = self.prepare_dataset_from_bn(bn_snapshots, dataset)137        means_1D = self.get_1D_means(g_snapshots)138        means_2D = self.get_2D_means(g_snapshots)139        return ds, means_1D, means_2D140    def prepare_stepper(141            self, train_bn_snapshots, train_dataset,142            val_bn_snapshots, val_dataset):143        train_ds, _ = \144            self.prepare_dataset_from_bn(train_bn_snapshots, train_dataset)145        val_ds, _ = \146            self.prepare_dataset_from_bn(val_bn_snapshots, val_dataset)147        return train_ds, val_ds148    def prepare_dataset_pickle(self, dataset):149        bn_snapshots = self.bound_normalize(dataset.snapshots)150        snapshots = self.reduce(bn_snapshots)151        g_snapshots = self.gridify(snapshots)152        _, pBs, _ = self.approximate(g_snapshots, dataset)...

plugin.py

Source:plugin.py

...46                raise RuntimeError("Could not install required deps")47            import site48            reload(site)49        self.log.info("BFG using ammo type %s", self.get_option("ammo_type"))50        self.stepper_wrapper.prepare_stepper()51        gun_type = self.get_option("gun_type")52        if gun_type in self.gun_classes:53            self.gun = self.gun_classes[gun_type](self.core)54        else:55            raise NotImplementedError(56                'No such gun type implemented: "%s"' % gun_type)57        cached_stpd_option = self.get_option("cached_stpd", '0')58        if cached_stpd_option == '1':59            cached_stpd = True60        else:61            cached_stpd = False62        if self.get_option("worker_type", "") == "green":63            BFG = BFGGreen64        else:...

stepper.py

Source:stepper.py

...23            losses = []24            for j, excludee in enumerate(used):25                print(f"Leaving out {excludee}")26                pipeline.const.used_variable_names = used[:j]+used[j+1:]27                train_ds, val_ds = pipeline.prepare_stepper(28                    train_bn_snapshots=train_bn_snapshots,29                    train_dataset=train_dataset,30                    val_bn_snapshots=val_bn_snapshots,31                    val_dataset=val_dataset)32                losses.append(Stepper.get_score(33                    train_ds, val_ds, repetitions, pipeline.const))34                print(f"  Mean label loss: {losses[-1]:.3f}")35            min_index = losses.index(min(losses))36            print(f"\nRemoved {used[min_index]}\t{len(used)-1} variables left\n")37            removed_variables.append(used[min_index])38            min_losses.append(min(losses))39            used = used[:min_index]+used[min_index+1:]40            print(f"{used}\n")41        print(f"\nFinal set: {used}\nFinal loss: {min(losses)}\nRemoved "...

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