From 8f5fd796a25a3f966ca4747f2b065945f2a154d5 Mon Sep 17 00:00:00 2001 From: Alice Date: Thu, 17 Oct 2024 00:02:18 +0200 Subject: [PATCH] Update the registry with configs + example with LSTM --- configs/{ => cifar10_cnn}/client_1.yml | 0 configs/{ => cifar10_cnn}/client_2.yml | 0 configs/{ => cifar10_cnn}/local_train.yml | 0 configs/{ => cifar10_cnn}/server.yml | 4 +- configs/turbofan_lstm/client_1.yml | 24 + configs/turbofan_lstm/client_2.yml | 23 + configs/turbofan_lstm/local_train.yml | 22 + configs/turbofan_lstm/server.yml | 33 + docs/how-to.md | 8 +- poetry.lock | 108 +- pyproject.toml | 1 + src/flower/client_fabric.py | 35 +- src/flower/server_fabric.py | 13 +- src/ml/data/turbofan/__init__.py | 0 src/ml/data/turbofan/lit_turbofan_data.py | 96 ++ src/ml/data/turbofan/turbofan.txt | 1326 +++++++++++++++++++++ src/ml/data/turbofan/turbofan_data.py | 36 + src/ml/models/lstm/__init__.py | 0 src/ml/models/lstm/lit_lstm_regressor.py | 99 ++ src/ml/models/lstm/lstm_regressor.py | 45 + src/ml/registry.py | 35 +- 21 files changed, 1883 insertions(+), 25 deletions(-) rename configs/{ => cifar10_cnn}/client_1.yml (100%) rename configs/{ => cifar10_cnn}/client_2.yml (100%) rename configs/{ => cifar10_cnn}/local_train.yml (100%) rename configs/{ => cifar10_cnn}/server.yml (83%) create mode 100644 configs/turbofan_lstm/client_1.yml create mode 100644 configs/turbofan_lstm/client_2.yml create mode 100644 configs/turbofan_lstm/local_train.yml create mode 100644 configs/turbofan_lstm/server.yml create mode 100644 src/ml/data/turbofan/__init__.py create mode 100644 src/ml/data/turbofan/lit_turbofan_data.py create mode 100644 src/ml/data/turbofan/turbofan.txt create mode 100644 src/ml/data/turbofan/turbofan_data.py create mode 100644 src/ml/models/lstm/__init__.py create mode 100644 src/ml/models/lstm/lit_lstm_regressor.py create mode 100644 src/ml/models/lstm/lstm_regressor.py diff --git a/configs/client_1.yml b/configs/cifar10_cnn/client_1.yml similarity index 100% rename from configs/client_1.yml rename to configs/cifar10_cnn/client_1.yml diff --git a/configs/client_2.yml b/configs/cifar10_cnn/client_2.yml similarity index 100% rename from configs/client_2.yml rename to configs/cifar10_cnn/client_2.yml diff --git a/configs/local_train.yml b/configs/cifar10_cnn/local_train.yml similarity index 100% rename from configs/local_train.yml rename to configs/cifar10_cnn/local_train.yml diff --git a/configs/server.yml b/configs/cifar10_cnn/server.yml similarity index 83% rename from configs/server.yml rename to configs/cifar10_cnn/server.yml index a9c7d9c..d2c89e5 100644 --- a/configs/server.yml +++ b/configs/cifar10_cnn/server.yml @@ -26,6 +26,6 @@ data: server_adress: "[::]:22222" num_rounds: 2 client_configs: - - ${root_dir}/configs/client_1.yml - - ${root_dir}/configs/client_2.yml + - ${root_dir}/configs/cifar10_cnn/client_1.yml + - ${root_dir}/configs/cifar10_cnn/client_2.yml save_on_train_end: true diff --git a/configs/turbofan_lstm/client_1.yml b/configs/turbofan_lstm/client_1.yml new file mode 100644 index 0000000..a18aa46 --- /dev/null +++ b/configs/turbofan_lstm/client_1.yml @@ -0,0 +1,24 @@ +cid: 1 +pre_train_val: true +fabric: + accelerator: gpu + devices: + - 0 +root_dir: ${oc.env:PWD} +model: + name: lstm + config: + n_features: 24 + hidden_units: 12 + lr: 0.001 +data: + name: turbofan + config: + data_path: ${root_dir}/src/ml/data/turbofan/turbofan.txt + engines_train_list: [52,62] + engines_val_list: [64] + engines_test_list: [69] + window: 20 + batch_size: 8 + num_workers: 0 +server_adress: localhost:22222 diff --git a/configs/turbofan_lstm/client_2.yml b/configs/turbofan_lstm/client_2.yml new file mode 100644 index 0000000..9fc907e --- /dev/null +++ b/configs/turbofan_lstm/client_2.yml @@ -0,0 +1,23 @@ +cid: 2 +fabric: + accelerator: gpu + devices: + - 0 +root_dir: ${oc.env:PWD} +model: + name: lstm + config: + n_features: 24 + hidden_units: 12 + lr: 0.001 +data: + name: turbofan + config: + data_path: ${root_dir}/src/ml/data/turbofan/turbofan.txt + engines_train_list: [2] + engines_val_list: [64] + engines_test_list: [69] + window: 20 + batch_size: 8 + num_workers: 0 +server_adress: localhost:22222 diff --git a/configs/turbofan_lstm/local_train.yml b/configs/turbofan_lstm/local_train.yml new file mode 100644 index 0000000..9cfaa80 --- /dev/null +++ b/configs/turbofan_lstm/local_train.yml @@ -0,0 +1,22 @@ +trainer: + max_epochs: 10 + accelerator: gpu + devices: + - 0 +root_dir: ${oc.env:PWD} +model: + name: lstm + config: + n_features: 24 + hidden_units: 12 + lr: 0.001 +data: + name: turbofan + config: + data_path: ${root_dir}/src/ml/data/turbofan/turbofan.txt + engines_train_list: [52,62,2] + engines_val_list: [64] + engines_test_list: [69] + window: 20 + batch_size: 8 + num_workers: 0 diff --git a/configs/turbofan_lstm/server.yml b/configs/turbofan_lstm/server.yml new file mode 100644 index 0000000..b03ffc2 --- /dev/null +++ b/configs/turbofan_lstm/server.yml @@ -0,0 +1,33 @@ +fabric: + accelerator: gpu + devices: + - 0 +root_dir: ${oc.env:PWD} +logger: + subdir: /experiments/federated/test_1/ +strategy: + name: "fabric" + config: + min_fit_clients: 2 +model: + name: lstm + config: + n_features: 24 + hidden_units: 12 + lr: 0.001 +data: + name: turbofan + config: + data_path: ${root_dir}/src/ml/data/turbofan/turbofan.txt + engines_train_list: [52,62] + engines_val_list: [64] + engines_test_list: [69] + window: 20 + batch_size: 8 + num_workers: 0 +server_adress: "[::]:22222" +num_rounds: 2 +client_configs: + - ${root_dir}/configs/turbofan_lstm/client_1.yml + - ${root_dir}/configs/turbofan_lstm/client_2.yml +save_on_train_end: true diff --git a/docs/how-to.md b/docs/how-to.md index 23a492e..bea66a9 100644 --- a/docs/how-to.md +++ b/docs/how-to.md @@ -12,8 +12,8 @@ Available models are located in `ml/models/`. To add a new model, follow the few - the second one contains the LightningModule based on the classical nn.module. * if needed, add another directory `ml/models/my-model/all-things-needed/` which would contain all things necessary for your model to work properly: specific losses and metrics, dedicated torch modules, and so on. * update the file `ml/registry.py`: - - import your LightningModule; - - update `model_registry` by adding a new key linking to your LightingModule. + - import your LightningModule and its config; + - update `model_registry` and `ModelConfig` by adding a new key linking to your LightingModule. ## How to add datasets in Pybiscus @@ -25,5 +25,5 @@ Available datasets are located in `ml/data/`. To add a new model, follow the few - the second one contains the LightningDataModule based on the classical torch.dataset. * if needed, add another directory `ml/data/my-data/all-things-needed/` which would contain all things necessary for your dataset to work properly, in particular preprocessing. * update the file `ml/registry.py`: - - import your LightningDataModule; - - update `datamodule_registry` by adding a new key linking to your LightingDataModule. + - import your LightningDataModule and its config; + - update `datamodule_registry` and `DataConfig` by adding a new key linking to your LightingDataModule. diff --git a/poetry.lock b/poetry.lock index 6282109..3d7072f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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"sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"}, +] + [[package]] name = "pyyaml" version = "6.0.1" @@ -3252,6 +3345,17 @@ files = [ {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"}, ] +[[package]] +name = "tzdata" +version = "2024.2" +description = "Provider of IANA time zone data" +optional = false +python-versions = ">=2" +files = [ + {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"}, + {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"}, +] + [[package]] name = "uc-micro-py" version = "1.0.2" @@ -3490,4 +3594,4 @@ test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-it [metadata] lock-version = "2.0" python-versions = ">=3.9, <3.10, !=3.9.7" -content-hash = "39e8c3dd5db111f2bd89f0222d82e0f2bcf4791f92ad24807239a0fdc6bf0330" +content-hash = "23b7c2f6401e7f8a05ff330b6852552c2af651896420440382a20ebde17a5aa0" diff --git a/pyproject.toml b/pyproject.toml index 33e1e77..c797b54 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ einops = "^0.6.1" tensorboard = "^2.14.1" pydantic = "2.1.1" trogon = "^0.5.0" +pandas = "^2.2.3" [tool.poetry.group.dev-dependencies.dependencies] black = "<25.0" diff --git a/src/flower/client_fabric.py b/src/flower/client_fabric.py index 31e08e8..52428ba 100644 --- a/src/flower/client_fabric.py +++ b/src/flower/client_fabric.py @@ -1,5 +1,7 @@ from collections import OrderedDict +from collections.abc import Mapping from typing import Union +from typing_extensions import Annotated import flwr as fl import torch @@ -10,7 +12,7 @@ from src.console import console from src.ml.data.cifar10.cifar10_datamodule import ConfigData_Cifar10 from src.ml.loops_fabric import test_loop, train_loop -from src.ml.models.cnn.lit_cnn import ConfigModel_Cifar10 +from src.ml.registry import ModelConfig, DataConfig torch.backends.cudnn.enabled = True @@ -33,6 +35,7 @@ class ConfigFabric(BaseModel): accelerator: str devices: Union[int, list[int], str] = "auto" +ConfigModel = Annotated[int, lambda x: x > 0] class ConfigClient(BaseModel): """A Pydantic Model to validate the Client configuration given by the user. @@ -62,8 +65,8 @@ class ConfigClient(BaseModel): server_adress: str root_dir: str fabric: ConfigFabric - model: ConfigModel_Cifar10 - data: ConfigData_Cifar10 + model: ModelConfig + data: DataConfig # Below is used when several models and/or datasets are available. # model: Union[ConfigModel_Cifar10, ...] = Field(discriminator="name") @@ -71,6 +74,26 @@ class ConfigClient(BaseModel): model_config = ConfigDict(extra="forbid") +def parse_optimizers(lightning_optimizers): + """ + Parse the output of lightning configure_optimizers + https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.core.LightningModule.html#lightning.pytorch.core.LightningModule.configure_optimizers + To extract only the optimizers (and not the lr_schedulers) + """ + optimizers = [] + if lightning_optimizers: + if isinstance(lightning_optimizers, Mapping): + optimizers.append(lightning_optimizers['optimizer']) + elif isinstance(lightning_optimizers, torch.optim.Optimizer): + optimizers.append(lightning_optimizers) + else: + for optmizers_conf in lightning_optimizers: + if isinstance(optmizers_conf, dict): + optimizers.append(lightning_optimizers) + else: + optimizers.append(optmizers_conf) + return optimizers + class FlowerClient(fl.client.NumPyClient): """A Fabric-based, modular Flower Client. @@ -116,13 +139,13 @@ def __init__( self.num_examples = num_examples self.pre_train_val = pre_train_val - self.optimizer = self.model.configure_optimizers() + self.optimizers = parse_optimizers(self.model.configure_optimizers()) self.fabric = Fabric(**self.conf_fabric) def initialize(self): self.fabric.launch() - self.model, self.optimizer = self.fabric.setup(self.model, self.optimizer) + self.model, self.optimizers = self.fabric.setup(self.model, *self.optimizers) ( self._train_dataloader, self._validation_dataloader, @@ -160,7 +183,7 @@ def fit(self, parameters, config): self.fabric, self.model, self._train_dataloader, - self.optimizer, + self.optimizers, # Alice TODO extend this to multiple optimizers ?? epochs=config["local_epochs"], ) console.log(f"Training Finished! Loss is {results_train['loss']}") diff --git a/src/flower/server_fabric.py b/src/flower/server_fabric.py index a5e896b..f83e1e6 100644 --- a/src/flower/server_fabric.py +++ b/src/flower/server_fabric.py @@ -1,5 +1,5 @@ from collections import OrderedDict -from typing import Callable, Optional +from typing import Callable, Optional, Union import flwr as fl import numpy as np @@ -12,9 +12,10 @@ from src.console import console from src.flower.client_fabric import ConfigFabric from src.flower.strategies import ConfigFabricStrategy -from src.ml.data.cifar10.cifar10_datamodule import ConfigData_Cifar10 from src.ml.loops_fabric import test_loop -from src.ml.models.cnn.lit_cnn import ConfigModel_Cifar10 +from src.ml.registry import ModelConfig, DataConfig + + class ConfigStrategy(BaseModel): @@ -58,10 +59,8 @@ class ConfigServer(BaseModel): logger: dict strategy: ConfigStrategy fabric: ConfigFabric - model: ConfigModel_Cifar10 - data: ConfigData_Cifar10 - # model: Union[ConfigModel_Cifar10] = Field(discriminator="name") - # data: Union[ConfigData_Cifar10] = Field(discriminator="name") + model: ModelConfig + data: DataConfig client_configs: list[str] = Field(default=None) save_on_train_end: bool = Field(default=False) diff --git a/src/ml/data/turbofan/__init__.py b/src/ml/data/turbofan/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml/data/turbofan/lit_turbofan_data.py b/src/ml/data/turbofan/lit_turbofan_data.py new file mode 100644 index 0000000..bf5653a --- /dev/null +++ b/src/ml/data/turbofan/lit_turbofan_data.py @@ -0,0 +1,96 @@ +from typing import Literal, Optional + +import lightning.pytorch as pl +from pydantic import BaseModel, ConfigDict +from torch.utils.data import DataLoader +from src.ml.data.turbofan.turbofan_data import turbofan_dataset + + +class ConfigTurbofanData(BaseModel): + """A Pydantic Model to validate the LitTurbofanDataModule config givent by the user. + + Attributes + ---------- + data_path: str + path to the directory holding the training data + engines_list: list of int (among [52, 62, 2, 64, 69]) + list of engines to be used + window : int + window of data to feed the model + batch_size: int, optional + the batch size (default to 32) + num_workers: int, optional + the number of workers for the DataLoaders (default to 0) + """ + + data_path: str = "turbofan.txt" + engines_train_list: list[int] = [52] + engines_val_list: list[int] = [64] + engines_test_list: list[int] = [69] + window: int = 20 + batch_size: int = 8 + num_workers: int = 0 + + model_config = ConfigDict(extra="forbid") + + +class ConfigData_TurbofanData(BaseModel): + name: Literal["turbofan"] + config: ConfigTurbofanData + + model_config = ConfigDict(extra="forbid") + + +class LitTurbofanDataModule(pl.LightningDataModule): + def __init__( + self, + data_path, + engines_train_list, + engines_val_list, + engines_test_list, + window, + batch_size, + num_workers, + ): + super().__init__() + self.data_path = data_path + self.engines_train_list = engines_train_list + self.engines_val_list = engines_val_list + self.engines_test_list = engines_test_list + self.window = window + self.num_workers = num_workers + self.batch_size = batch_size + + def setup(self, stage: Optional[str] = None): + if stage == "fit" or stage is None: + self.data_train = turbofan_dataset( + engines_list=self.engines_train_list, + window=self.window, + datapath=self.data_path, + ) + self.data_val = turbofan_dataset( + engines_list=self.engines_val_list, + window=self.window, + datapath=self.data_path, + ) + if stage == "test" or stage is None: + self.data_test = turbofan_dataset( + engines_list=self.engines_test_list, + window=self.window, + datapath=self.data_path, + ) + + def train_dataloader(self): + return DataLoader( + self.data_train, batch_size=self.batch_size, shuffle=True, pin_memory=True + ) + + def val_dataloader(self): + return DataLoader( + self.data_val, batch_size=self.batch_size, shuffle=True, pin_memory=True + ) + + def test_dataloader(self): + return DataLoader( + self.data_test, batch_size=self.batch_size, shuffle=True, pin_memory=True + ) diff --git a/src/ml/data/turbofan/turbofan.txt b/src/ml/data/turbofan/turbofan.txt new file mode 100644 index 0000000..04a5028 --- /dev/null +++ b/src/ml/data/turbofan/turbofan.txt @@ -0,0 +1,1326 @@ +engine_no,time_in_cycles,operational_setting_1,operational_setting_2,operational_setting_3,sensor_measurement_1,sensor_measurement_2,sensor_measurement_3,sensor_measurement_4,sensor_measurement_5,sensor_measurement_6,sensor_measurement_7,sensor_measurement_8,sensor_measurement_9,sensor_measurement_10,sensor_measurement_11,sensor_measurement_12,sensor_measurement_13,sensor_measurement_14,sensor_measurement_15,sensor_measurement_16,sensor_measurement_17,sensor_measurement_18,sensor_measurement_19,sensor_measurement_20,sensor_measurement_21,sensor_measurement_22,sensor_measurement_23 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+ df = pd.read_csv(datapath, sep=",") + df = df.dropna(axis=1) + self.df = df[df.engine_no.isin(engines_list)].__deepcopy__() + self.add_rul() + self.window = window + + def __len__(self): + return len(self.df) - self.window + + def __getitem__(self, idx): + X = ( + self.df.iloc[idx : idx + self.window, :] + .drop(["time_in_cycles", "engine_no", "rul"], axis=1) + .copy() + .to_numpy() + ) + y = self.df.iloc[idx + self.window - 1]["rul"] + return X, y + + def add_rul(self): + """Add remaining useful lifetime values, the value we want to be able to predict""" + tot_time = {} + for engine in self.df.engine_no.unique(): + df_engine = self.df[self.df.engine_no == engine] + tot_time[engine] = len(df_engine) + self.df["rul"] = self.df[["engine_no", "time_in_cycles"]].apply( + lambda x: tot_time[x.iloc[0]] - x.iloc[1], axis=1 + ) \ No newline at end of file diff --git a/src/ml/models/lstm/__init__.py b/src/ml/models/lstm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml/models/lstm/lit_lstm_regressor.py b/src/ml/models/lstm/lit_lstm_regressor.py new file mode 100644 index 0000000..3854902 --- /dev/null +++ b/src/ml/models/lstm/lit_lstm_regressor.py @@ -0,0 +1,99 @@ +from typing import Literal, TypedDict + +import lightning.pytorch as pl +import torch +import torch.nn as nn +from pydantic import BaseModel, ConfigDict + +from src.ml.models.lstm.lstm_regressor import LSTMRegressor + + +class ConfigLSTM(BaseModel): + """A Pydantic Model to validate the LitCNN config given by the user. + + Attributes + ---------- + n_features: + number of features of the input + hidden_units: int + number of hiddent units of the LSTM + lr: float + the learning rate + """ + + n_features: int = 24 + hidden_units: int = 12 + lr: float = 0.001 + + model_config = ConfigDict(extra="forbid") + + +class ConfigModel_LSTM(BaseModel): + name: Literal["lstm"] + config: ConfigLSTM + + model_config = ConfigDict(extra="forbid") + + +class LSTMSignature(TypedDict): + loss: torch.Tensor + + +class LitLSTMRegressor(pl.LightningModule): + def __init__( + self, + n_features: int, + hidden_units: int, + lr: float, + _logging: bool = False, + ): + super().__init__() + self.save_hyperparameters() + self.n_features = n_features + self.hidden_units = hidden_units + self.lr = lr + self._logging = _logging + self.model = LSTMRegressor( + n_features=self.n_features, + hidden_units=self.hidden_units, + ) + self.loss = nn.MSELoss() + self._signature = LSTMSignature + + @property + def signature(self): + return self._signature + + def forward(self, images): + return self.model(images) + + def training_step(self, batch: torch.Tensor, batch_idx) -> LSTMSignature: + signal, labels = batch + signal = signal.to(torch.float32) + labels = labels.to(torch.float32) + outputs = self.forward(signal) + loss = self.loss(outputs, labels) + if self._logging: + self.log("train_loss", loss, prog_bar=True) + return {"loss": loss} + + def validation_step(self, batch: torch.Tensor, batch_idx) -> LSTMSignature: + signal, labels = batch + signal = signal.to(torch.float32) + labels = labels.to(torch.float32) + outputs = self.forward(signal) + loss = self.loss(outputs, labels) + if self._logging: + self.log("val_loss", loss, prog_bar=True) + return {"loss": loss} + + def test_step(self, batch: torch.Tensor, batch_idx) -> torch.Tensor: + signal, labels = batch + signal = signal.to(torch.float32) + labels = labels.to(torch.float32) + outputs = self.forward(signal) + loss = self.loss(outputs, labels) + return loss + + def configure_optimizers(self) -> None: + return torch.optim.Adam(self.parameters(), lr=self.lr) diff --git a/src/ml/models/lstm/lstm_regressor.py b/src/ml/models/lstm/lstm_regressor.py new file mode 100644 index 0000000..a1b2c1b --- /dev/null +++ b/src/ml/models/lstm/lstm_regressor.py @@ -0,0 +1,45 @@ +import torch.nn as nn +import torch + +# Example taken from https://www.kaggle.com/code/jinsolkwon/rul-predictions-using-pytorch-lstm#1.-Data-Processing + + +class LSTMRegressor(nn.Module): + + def __init__(self, n_features, hidden_units): + super().__init__() + self.n_features = n_features + self.hidden_units = hidden_units + self.n_layers = 1 + self.lstm = nn.LSTM( + input_size=n_features, + hidden_size=self.hidden_units, + batch_first=True, + num_layers=self.n_layers, + ) + self.linear1 = nn.Linear(in_features=self.hidden_units, out_features=12) + self.relu1 = nn.ReLU() + self.linear2 = nn.Linear(in_features=12, out_features=12) + self.relu2 = nn.ReLU() + self.linear3 = nn.Linear(in_features=12, out_features=1) + + def forward(self, x): + batch_size = x.shape[0] + h0 = ( + torch.zeros(self.n_layers, batch_size, self.hidden_units) + .requires_grad_() + .to(x.get_device()) + ) + c0 = ( + torch.zeros(self.n_layers, batch_size, self.hidden_units) + .requires_grad_() + .to(x.get_device()) + ) + _, (hn, _) = self.lstm(x, (h0, c0)) + out = self.linear1(hn[0]) + out = self.relu1(out) + out = self.linear2(out) + out = self.relu2(out) + out = self.linear3(out).flatten() + + return out diff --git a/src/ml/registry.py b/src/ml/registry.py index 289b1b7..4473ba8 100644 --- a/src/ml/registry.py +++ b/src/ml/registry.py @@ -1,5 +1,32 @@ -from src.ml.data.cifar10.cifar10_datamodule import CifarLitDataModule -from src.ml.models.cnn.lit_cnn import LitCNN +from typing_extensions import Annotated, Union +from pydantic import Field -model_registry = {"cifar": LitCNN} -datamodule_registry = {"cifar": CifarLitDataModule} +from src.ml.data.cifar10.cifar10_datamodule import ( + CifarLitDataModule, + ConfigData_Cifar10, +) +from src.ml.models.cnn.lit_cnn import LitCNN, ConfigModel_Cifar10 + + +from src.ml.data.turbofan.lit_turbofan_data import ( + LitTurbofanDataModule, + ConfigData_TurbofanData, +) +from src.ml.models.lstm.lit_lstm_regressor import LitLSTMRegressor, ConfigModel_LSTM + +model_registry = {"cifar": LitCNN, "lstm": LitLSTMRegressor} + +ModelConfig = Annotated[ + Union[ConfigModel_Cifar10, ConfigModel_LSTM], + Field(discriminator="name"), +] + +datamodule_registry = { + "cifar": CifarLitDataModule, + "turbofan": LitTurbofanDataModule, +} + +DataConfig = Annotated[ + Union[ConfigData_Cifar10, ConfigData_TurbofanData], + Field(discriminator="name"), +]