examples/hpc_cpu_example.py
def train(hparams, *args):
    """Train your awesome model.

    :param hparams: The arguments to run the model with.
    """
    # Initialize experiments and track all the hyperparameters
    exp = Experiment(
        name=hparams.test_tube_exp_name,
        # Location to save the metrics.
        save_dir=hparams.log_path,
        # The experiment version is optional, but using the one 
        # from SLURM means the exp will not collide with other
        # versions if SLURM runs multiple at once.
        version=hparams.hpc_exp_number,
        autosave=False,
    )
    exp.argparse(hparams)

    # Pretend to train.
    x = hparams.x_val
    for train_step in range(0, 100):
        y = hparams.y_val
        out = x * y
        exp.log({'fake_err': out.item()})  # Log metrics.

    # Save exp when done.
    exp.save()
def train(hparams, *args):
    """Train your awesome model.

    :param hparams: The arguments to run the model with.
    """
    # Initialize experiments and track all the hyperparameters
    exp = Experiment(
        name=hparams.test_tube_exp_name,
        # Location to save the metrics.
        save_dir=hparams.log_path,
        # The experiment version is optional, but using the one 
        # from SLURM means the exp will not collide with other
        # versions if SLURM runs multiple at once.
        version=hparams.hpc_exp_number,
        autosave=False,
    )
    exp.argparse(hparams)

    # Pretend to train.
    x = hparams.x_val
    for train_step in range(0, 100):
        y = hparams.y_val
        out = x * y
        exp.log({'fake_err': out.item()})  # Log metrics.

    # Save exp when done.
    exp.save()
def train ( hparams , * args ) : # Initialize experiments and track all the hyperparameters exp = Experiment ( name = hparams . test_tube_exp_name , # Location to save the metrics. save_dir = hparams . log_path , # The experiment version is optional, but using the one  # from SLURM means the exp will not collide with other # versions if SLURM runs multiple at once. version = hparams . hpc_exp_number , autosave = False , ) exp . argparse ( hparams ) # Pretend to train. x = hparams . x_val for train_step in range ( 0 , 100 ) : y = hparams . y_val out = x * y exp . log ( { 'fake_err' : out . item ( ) } ) # Log metrics. # Save exp when done. exp . save ( )
Train your awesome model.

    :param hparams: The arguments to run the model with.
Train your awesome model .