here is a shorter config that should work:

config_nhits = {
    "input_size": tune.choice([4, 8]),              # Length of input window
    "start_padding_enabled": True,
    "stack_types": 3*['identity'],
    "n_blocks": 3*[1],                                           # Length of input window
    "mlp_units": 3 * [[128, 128]],                                  # Length of input window
    "n_pool_kernel_size": tune.choice([3*[1], 3*[2], 3*[4]]),            # MaxPooling Kernel size
    "n_freq_downsample": tune.choice([[4, 2, 1],
                                      [1, 1, 1]]),            # Interpolation expressivity ratios
    "learning_rate": tune.loguniform(1e-4, 1e-2),                   # Initial Learning rate
    "scaler_type": tune.choice(['robust', 'standard']),                             # Scaler type
    "max_steps": tune.choice([500, 1000]),                               # Max number of training iterations
    "batch_size": tune.choice([32,64,128]),                          # Number of series in batch
    "windows_batch_size": tune.choice([64, 128, 256]),             # Number of windows in batch
    "random_seed": tune.randint(1, 20),                             # Random seed
}

Some other tips:

mlp_units

of 3 is extremely small, I changed it to 128.

batch_size

and

windows_batch_size

of 1 are also too small. Let me know if the config above works. Try with

num_samples=2

while debugging.

can you try with the config I sent you?

Also I am getting the same issue for LSTM Config as well. A quick note: I am training a very small dataset of 101 time samples of 'weekly' data. Hence I was keeping the mlp_units and other config small. My LSTM Config: config_lstm = { "input_size": tune.choice([4,8]), # Length of input window "encoder_hidden_size": tune.choice([4,8,16,32,64,128]), # Hidden size of LSTM cells "encoder_activation": tune.choice(['relu']), "encoder_n_layers": tune.choice([1,2,3]), # Number of layers in LSTM "learning_rate": tune.loguniform(1e-4, 1e-2), # Initial Learning rate "scaler_type": tune.choice(['robust', 'standard']), # Scaler type "max_steps": tune.choice([100, 200]), # Max number of training iterations "batch_size": tune.choice([2,4,8,16,32,64,128]), # Number of series in batch "random_seed": tune.randint(1, 20), # Random seed "hist_exog_list": ['VOICE_TRAFFIC','SUCCESSFULL_CALLS'] }