I have trained a PyTorch lightning model that looks like this:
In [16]: MLP
Out[16]:
DecoderMLP(
(loss): RMSE()
(logging_metrics): ModuleList(
(0): SMAPE()
(1): MAE()
(2): RMSE()
(3): MAPE()
(4): MASE()
)
(input_embeddings): MultiEmbedding(
(embeddings): ModuleDict(
(LCLid): Embedding(5, 4)
(sun): Embedding(5, 4)
(day_of_week): Embedding(7, 5)
(month): Embedding(12, 6)
(year): Embedding(3, 3)
(holidays): Embedding(2, 1)
(BusinessDay): Embedding(2, 1)
(day): Embedding(31, 11)
(hour): Embedding(24, 9)
)
)
(mlp): FullyConnectedModule(
(sequential): Sequential(
(0): Linear(in_features=60, out_features=435, bias=True)
(1): ReLU()
(2): Dropout(p=0.13371112461182535, inplace=False)
(3): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(4): Linear(in_features=435, out_features=435, bias=True)
(5): ReLU()
(6): Dropout(p=0.13371112461182535, inplace=False)
(7): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(8): Linear(in_features=435, out_features=435, bias=True)
(9): ReLU()
(10): Dropout(p=0.13371112461182535, inplace=False)
(11): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(12): Linear(in_features=435, out_features=435, bias=True)
(13): ReLU()
(14): Dropout(p=0.13371112461182535, inplace=False)
(15): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(16): Linear(in_features=435, out_features=435, bias=True)
(17): ReLU()
(18): Dropout(p=0.13371112461182535, inplace=False)
(19): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(20): Linear(in_features=435, out_features=435, bias=True)
(21): ReLU()
(22): Dropout(p=0.13371112461182535, inplace=False)
(23): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(24): Linear(in_features=435, out_features=435, bias=True)
(25): ReLU()
(26): Dropout(p=0.13371112461182535, inplace=False)
(27): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(28): Linear(in_features=435, out_features=435, bias=True)
(29): ReLU()
(30): Dropout(p=0.13371112461182535, inplace=False)
(31): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(32): Linear(in_features=435, out_features=435, bias=True)
(33): ReLU()
(34): Dropout(p=0.13371112461182535, inplace=False)
(35): LayerNorm((435,), eps=1e-05, elementwise_affine=True)
(36): Linear(in_features=435, out_features=1, bias=True)
)
)
)
I need the corresponding PyTorch model to use in one of my other applications.
Is there a simple way to do that?
I thought of saving the checkpoint but then I don’t know how to do it.
Can you please help?
Thanks