thepycoder

# report every 1 minute, this is way too often, but we are testing here
optimizer.set_report_period(1)
# start the optimization process
# this function returns immediately
optimizer.start()
# set the time limit for the optimization process (2 hours)
optimizer.set_time_limit(in_minutes=120.0)
# wait until process is done (notice we are controlling the optimization process in the background)
optimizer.wait()
# optimization is completed, print the top performing experiments id

thepycoder

from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
from clearml.automation import HyperParameterOptimizer
from clearml.automation import GridSearch
 
from clearml import Task
 
task = Task.init(project_name='HPO Code',
                task_name='basic dict optimizer',
                task_type=Task.TaskTypes.optimizer,
                reuse_last_task_id=False)

thepycoder

logger = task.get_logger()
 
logger.report_scalar('Optimization Metric', 'model_output', iteration=0, value=model_output)

thepycoder

class Model:
   def run():
       return example_configuration['epochs'] * 10
 
model_output = Model().run()

thepycoder

example_configuration = {
   'epochs': 3,
   'lr': 0.001,
   'comments': 'I like apple juice'
}
 
task.connect(example_configuration)

thepycoder

from clearml import Task
task = Task.init(project_name="HPO Example", task_name="basic dict example")

thepycoder

from clearml import Task
task = Task.init(project_name="HPO Example", task_name="basic dict example")
 
example_configuration = {
   'epochs': 3,
   'lr': 0.001,
   'comments': 'I like apple juice'
}
 
task.connect(example_configuration)

thepycoder

def pipe_to_virtual_webcam(self):
    with pyvirtualcam.Camera(width=854, height=480, fps=30) as cam:
        print(f'Using virtual camera: {cam.device}')
        while self.is_running:
            frame = self.webcam_queue.get()
            cam.send(frame)
            cam.sleep_until_next_frame()

thepycoder

def run(self):
    pipeline = self.create_pipeline()
    # Pipeline defined, now the device is connected to
    with dai.Device(pipeline) as device:
        # Start pipeline
        device.startPipeline()

        # Set queues to be read from later
        self.high_quality_out = device.getOutputQueue(name="high_quality_out", maxSize=4, blocking=False)
        self.detection_out = device.getOutputQueue(name="detection_out", maxSize=4, blocking=False)

thepycoder

def create_pipeline(self):
        # Create pipeline and set version
        pipeline = dai.Pipeline()
        pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_3)

        # Camera specific settings
        cam = pipeline.createColorCamera()
        cam.setPreviewSize(*self.nn_shape)
        cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
        cam.setInterleaved(False)