Python tensorboard_logger.configure() Examples

The following are 7 code examples of tensorboard_logger.configure(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorboard_logger , or try the search function .
Example #1
Source File: LogMetric.py    From nmp_qc with MIT License 5 votes vote down vote up 
def __init__(self, log_dir):
        if not os.path.isdir(log_dir):
            # if the directory does not exist we create the directory
            os.makedirs(log_dir)
        else:                      
            # clean previous logged data under the same directory name
            self._remove(log_dir)

        # configure the project
        configure(log_dir)

        self.global_step = 0
Example #2
Source File: fit_harness.py    From ibeis with Apache License 2.0 5 votes vote down vote up 
def run(harn):
        harn.log('Begin training')

        if False:
            # TODO: can we run this as a subprocess that dies when we die?
            # or do we need to run externally?
            # tensorboard --logdir runs
            # http://aretha:6006
            pass

        if tensorboard_logger:
            harn.log('Initializing tensorboard')
            tensorboard_logger.configure("runs/ibeis", flush_secs=2)

        if harn.use_cuda:
            harn.log('Fitting model on GPU({})'.format(harn.gpu_num))
            harn.model.cuda(harn.gpu_num)
        else:
            harn.log('Fitting model on the CPU')

        if harn.class_weights is not None:
            harn.class_weights, = harn._to_xpu(harn.class_weights)

        lr = harn.lr_scheduler(harn.epoch)
        harn.optimizer = harn.optimizer_cls(harn.model.parameters(), lr=lr)

        # train loop

        while not harn.check_termination():
            harn.train_epoch()

            if harn.vali_loader:
                harn.validation_epoch()

            harn.save_snapshot()

            harn.epoch += 1
Example #3
Source File: logger.py    From DeepSpeaker-pytorch with MIT License 5 votes vote down vote up 
def __init__(self, log_dir):
        # clean previous logged data under the same directory name
        self._remove(log_dir)

        # configure the project
        configure(log_dir)

        self.global_step = 0
Example #4
Source File: logger.py    From skiprnn_pytorch with MIT License 5 votes vote down vote up 
def __init__(self, log_dir, remove_previous_files = False):
        # clean previous logged data under the same directory name
        if remove_previous_files:
            self._remove(log_dir)

        # configure the project
        configure(log_dir)

        self.global_step = 0
Example #5
Source File: Loggers.py    From hardnet with MIT License 5 votes vote down vote up 
def __init__(self, log_dir):
        # clean previous logged data under the same directory name
        self._remove(log_dir)

        # configure the project
        configure(log_dir)

        self.global_step = 0
Example #6
Source File: logger.py    From FewShotLearning with MIT License 5 votes vote down vote up 
def __init__(self, log_dir):
        # clean previous logged data under the same directory name
        self._remove(log_dir)

        # configure the project
        configure(log_dir)

        self.global_step = 0
Example #7
Source File: train_node.py    From Auto-PyTorch with Apache License 2.0 5 votes vote down vote up 
def tensorboard_log(self, budget, epoch, log, logdir):
        import tensorboard_logger as tl
        worker_path = 'Train/'
        try:
            tl.log_value(worker_path + 'budget', float(budget), int(time.time()))
        except:
            tl.configure(logdir)
            tl.log_value(worker_path + 'budget', float(budget), int(time.time()))
        tl.log_value(worker_path + 'epoch', float(epoch + 1), int(time.time()))
        for name, value in log.items():
            tl.log_value(worker_path + name, float(value), int(time.time()))