How to use the esper.prelude.Timer function in esper

from esper.kube import cluster_config, worker_config, make_cluster
from esper.scanner_bench import ScannerJobConfig, bench
import attr
import scannerpy
import json
import pickle


# db = scannerpy.Database()
# print('making trace')
# db.profiler(665).write_trace('shots.tar.gz')
# print('done')
# exit()


with Timer('Loading videos'):
    videos = list(Video.objects.filter(threeyears_dataset=False).order_by('id'))
    #videos = videos[:1]
    print('Found {} videos'.format(len(videos)))

def run_pipeline(db, videos, **kwargs):
    return shot_detection.compute_histograms(
        db,
        videos=[v.for_scannertools() for v in videos],
        **kwargs)

if False:
    with Timer('Benchmarking histograms'):
        # configs = [
        #     (attr.evolve(cluster_config, worker=attr.evolve(
        #         worker_config, type=kube.MachineTypeName(name='n1-standard-4'))),
        #      [ScannerJobConfig(

# exit()


with Timer('Loading videos'):
    videos = list(Video.objects.filter(threeyears_dataset=False).order_by('id'))
    #videos = videos[:1]
    print('Found {} videos'.format(len(videos)))

def run_pipeline(db, videos, **kwargs):
    return shot_detection.compute_histograms(
        db,
        videos=[v.for_scannertools() for v in videos],
        **kwargs)

if False:
    with Timer('Benchmarking histograms'):
        # configs = [
        #     (attr.evolve(cluster_config, worker=attr.evolve(
        #         worker_config, type=kube.MachineTypeName(name='n1-standard-4'))),
        #      [ScannerJobConfig(
        #          io_packet_size=1000,
        #          work_packet_size=20,
        #          batch=20)]),
        #     (attr.evolve(cluster_config, worker=attr.evolve(
        #         worker_config, type=kube.MachineTypeName(name='n1-standard-8'))),
        #      [ScannerJobConfig(
        #          io_packet_size=1000,
        #          work_packet_size=20,
        #          batch=20)]),
        #     (attr.evolve(cluster_config, worker=attr.evolve(
        #         worker_config, type=kube.MachineTypeName(name='n1-standard-32'))),
        #      [ScannerJobConfig(

model_dir=self._model_dir,
                device=DeviceType.GPU if self._db.has_gpu() else DeviceType.CPU)
        }


embed_faces = FaceEmbeddingPipeline.make_runner()

def frames_for_video(video):
    return [f['number'] for f in
            Frame.objects.filter(video=video, shot_boundary=False).annotate(
                c=Subquery(Face.objects.filter(frame=OuterRef('pk')).values('frame').annotate(c=Count('*')).values('c')))
            .filter(c__gte=1)
            .values('number').order_by('number')]

if False:
    with Timer('benchmark'):
        videos = videos[:30]
        def run_pipeline(db, videos, frames, **kwargs):
            return embed_faces(
                db,
                videos=[v.for_scannertools() for v in videos],
                frames=frames,
                faces=[ScannerSQLTable(Face, v) #num_elements=len(f))
                       for v, f in zip(videos, frames)],
                cache=False,
                **kwargs)

        cfg = cluster_config(
            num_workers=5, worker=worker_config('n1-standard-32'), pipelines=[face_embedding.FaceEmbeddingPipeline])
        configs = [(cfg, [
            ScannerJobConfig(io_packet_size=500, work_packet_size=20, pipelines_per_worker=4),
            ScannerJobConfig(io_packet_size=1000, work_packet_size=20, pipelines_per_worker=4),

compute_shot_boundaries = ShotBoundaryPipeline.make_runner()

# with Timer('Histogram'):
#     cfg = cluster_config(
#         num_workers=300,
#         worker=worker_config('n1-standard-16'))
#     with make_cluster(cfg, no_delete=True) as db_wrapper:

# videos = videos
#videos = list(Video.objects.filter(id__gte=91250, id__lte=91350))
# videos = [Video.objects.get(id=63970)]
videos = videos

with Timer('Shot boundaries'):
    cfg = cluster_config(
        num_workers=60,
        worker=worker_config('n1-highmem-16'),
        workers_per_node=2,
        num_load_workers=1,
        num_save_workers=2)
    with make_cluster(cfg, no_delete=True) as db_wrapper:

    # from esper.scannerutil import ScannerWrapper
    # if True:
    #     db_wrapper = ScannerWrapper.create()

        db = db_wrapper.db

        job_config = ScannerJobConfig(io_packet_size=10000, work_packet_size=400, batch=400)
        hists = run_pipeline(db, videos, batch=job_config.batch, run_opts={

class FaceDetectionPipeline(ScannerSQLPipeline, face_detection.FaceDetectionPipeline):
    db_class = Face
    json_kernel = 'FacesToJson'
    additional_sources = ['frame_ids']

    def build_pipeline(self):
        output_ops = super(FaceDetectionPipeline, self).build_pipeline()
        output_ops['frame_ids'] = self._sources['frame_ids'].op
        return output_ops

detect_faces = FaceDetectionPipeline.make_runner()

videos = list(Video.objects.filter(threeyears_dataset=True).order_by('id'))

if False:
    with Timer('benchmark'):
        videos = videos[:50]
        def run_pipeline(db, videos, frames, **kwargs):
            return face_detection.detect_faces(db, videos=[v.for_scannertools() for v in videos], frames=frames, cache=False, **kwargs)

        cfg = cluster_config(num_workers=5, worker=worker_config('n1-standard-32'))
        configs = [(cfg, [ScannerJobConfig(io_packet_size=1000, work_packet_size=20, batch=1)])]
        bench('face', {'videos': videos, 'frames': [[f['number'] for f in Frame.objects.filter(video=v).values('number').order_by('number')] for v in videos]},
              run_pipeline, configs, no_delete=True, force=True)


with Timer('run'):

    print('Getting frames')
    def load_frames():
        return [[f['number'] for f in Frame.objects.filter(video=v, shot_boundary=False).values('number').order_by('number')]
                for v in tqdm(videos)]

videos = list(Video.objects.filter(threeyears_dataset=True).order_by('id'))

if False:
    with Timer('benchmark'):
        videos = videos[:50]
        def run_pipeline(db, videos, frames, **kwargs):
            return face_detection.detect_faces(db, videos=[v.for_scannertools() for v in videos], frames=frames, cache=False, **kwargs)

        cfg = cluster_config(num_workers=5, worker=worker_config('n1-standard-32'))
        configs = [(cfg, [ScannerJobConfig(io_packet_size=1000, work_packet_size=20, batch=1)])]
        bench('face', {'videos': videos, 'frames': [[f['number'] for f in Frame.objects.filter(video=v).values('number').order_by('number')] for v in videos]},
              run_pipeline, configs, no_delete=True, force=True)


with Timer('run'):

    print('Getting frames')
    def load_frames():
        return [[f['number'] for f in Frame.objects.filter(video=v, shot_boundary=False).values('number').order_by('number')]
                for v in tqdm(videos)]
    frames = pcache.get('face_frames', load_frames)

    cfg = cluster_config(
        num_workers=100,
        worker=worker_config('n1-standard-64'),
        num_load_workers=2,
        num_save_workers=2)
    with make_cluster(cfg, sql_pool=4, no_delete=True) as db_wrapper:

    # if True:
    #     db_wrapper = ScannerWrapper.create(enable_watchdog=False)

'faces': self._sources['faces'].op
        }

detect_genders = GenderDetectionPipeline.make_runner()

videos = list(Video.objects.filter(threeyears_dataset=True).order_by('id'))

def frames_for_video(video):
    return [f['number'] for f in
            Frame.objects.filter(video=video, shot_boundary=False).annotate(
                c=Subquery(Face.objects.filter(frame=OuterRef('pk')).values('frame').annotate(c=Count('*')).values('c')))
            .filter(c__gte=1)
            .values('number').order_by('number')]

if False:
    with Timer('benchmark'):
        videos = videos[:50]
        def run_pipeline(db, videos, frames, **kwargs):
            return detect_genders(
                db,
                db_videos=videos,
                videos=[v.for_scannertools() for v in videos],
                frames=frames,
                faces=[ScannerSQLTable(Face, v) #num_elements=len(f))
                       for v, f in zip(videos, frames)],
                cache=False,
                **kwargs)

        cfg = cluster_config(
            num_workers=5, worker=worker_config('n1-standard-32'), pipelines=[GenderDetectionPipeline])
        configs = [(cfg, [
            ScannerJobConfig(io_packet_size=1000, work_packet_size=20, pipelines_per_worker=4),