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Update existing examples. Added multiple Pytorch examples
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Jethro Gaglione
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Nov 23, 2023
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mlruns/0/62ce60957a5a4e93b8182918697230cf/artifacts/estimator.html
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| <html lang="en"> | ||
| <head> | ||
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mlruns/0/62ce60957a5a4e93b8182918697230cf/artifacts/model/MLmodel
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mlruns/0/62ce60957a5a4e93b8182918697230cf/artifacts/model/conda.yaml
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mlruns/0/62ce60957a5a4e93b8182918697230cf/artifacts/model/python_env.yaml
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mlruns/0/62ce60957a5a4e93b8182918697230cf/artifacts/model/requirements.txt
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mlruns/0/62ce60957a5a4e93b8182918697230cf/inputs/2901e21780957eeb7b293a72714bc168/meta.yaml
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mlruns/0/datasets/21f6d9f0ac284309fa124c33fea309a0/meta.yaml
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| schema: '{"mlflow_tensorspec": {"features": "[{\"type\": \"tensor\", \"tensor-spec\": | ||
| {\"dtype\": \"float64\", \"shape\": [-1, 10]}}]", "targets": "[{\"type\": \"tensor\", | ||
| \"tensor-spec\": {\"dtype\": \"float64\", \"shape\": [-1]}}]"}}' | ||
| source: '{"tags": {"mlflow.user": "gaglijt1", "mlflow.source.name": "train_skl_rf.py", | ||
| "mlflow.source.type": "LOCAL", "mlflow.source.git.commit": "2cf5575fae20e2b2e89a661ea42a68d57d318647"}}' | ||
| source_type: code |
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| @@ -0,0 +1,9 @@ | ||
| digest: fdd80d1b | ||
| name: dataset | ||
| profile: '{"features_shape": [111, 10], "features_size": 1110, "features_nbytes": | ||
| 8880}' | ||
| schema: '{"mlflow_tensorspec": {"features": "[{\"type\": \"tensor\", \"tensor-spec\": | ||
| {\"dtype\": \"float64\", \"shape\": [-1, 10]}}]", "targets": null}}' | ||
| source: '{"tags": {"mlflow.user": "gaglijt1", "mlflow.source.name": "train_skl_rf.py", | ||
| "mlflow.source.type": "LOCAL", "mlflow.source.git.commit": "2cf5575fae20e2b2e89a661ea42a68d57d318647"}}' | ||
| source_type: code |
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| artifact_location: file:///panfs/accrepfs.vampire/home/gaglijt1/mltf/mlruns/0 | ||
| creation_time: 1700613890473 | ||
| experiment_id: '0' | ||
| last_update_time: 1700613890473 | ||
| lifecycle_stage: active | ||
| name: Default |
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| # Sequential NN example using the PyTorch Lightning API. | ||
| # Lightning modules must be used to activate MLflow autolog capabilities. | ||
| # Lightning will autodetect and run on a GPU, if availble, without explicit code. | ||
| import torch | ||
| import mlflow | ||
| from torch.utils.data import Dataset | ||
| from torchvision import datasets | ||
| from torchvision.transforms import ToTensor | ||
| from torch.utils.data import DataLoader | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| import torch.optim as optim | ||
| import pytorch_lightning as L | ||
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| mlflow.pytorch.autolog() | ||
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| #In order to take advantage of MLflow autolog capabilities, we need a LightningModule | ||
| class SeqNet(L.LightningModule): | ||
| def __init__(self, input_size, hidden_size1, hidden_size2, output_size, lr): | ||
| super().__init__() | ||
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| self.lin1 = nn.Linear(input_size, hidden_size1) | ||
| self.lin2 = nn.Linear(hidden_size1, hidden_size2) | ||
| self.lin3 = nn.Linear(hidden_size2, output_size) | ||
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| def forward(self, x): | ||
| x = torch.flatten(x,1) | ||
| x = self.lin1(x) | ||
| x = F.sigmoid(x) | ||
| x = self.lin2(x) | ||
| x = F.log_softmax(x, dim=1) | ||
| out = self.lin3(x) | ||
| return out | ||
| # training_step must be defined to use Lightning | ||
| def training_step(self, batch, batchidx): | ||
| images, labels = batch | ||
| output = self(images) | ||
| loss_func = nn.CrossEntropyLoss() | ||
| loss = loss_func(output, labels) | ||
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| #Using PyTorch, logging your loss in MLflow requires the following line: | ||
| self.log("train_loss", loss, prog_bar=True, on_step=False, on_epoch=True) | ||
| return loss | ||
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| def configure_optimizers(self): | ||
| optimizer = torch.optim.SGD(self.parameters(), lr=lr) | ||
| return optimizer | ||
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| input_size = 784 | ||
| hidden_size1 = 200 | ||
| hidden_size2 = 200 | ||
| output_size = 10 | ||
| num_epochs = 20 | ||
| batch_size = 100 | ||
| lr = 0.01 | ||
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| model = SeqNet(input_size, hidden_size1, hidden_size2, output_size, lr) | ||
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| fmnist_train = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor()) | ||
| fmnist_test = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor()) | ||
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| fmnist_train_loader = DataLoader(fmnist_train) | ||
| fmnist_test_loader = DataLoader(fmnist_test) | ||
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| #MLflow autologs runs from calls to Lighning "Trainers": | ||
| trainer = L.Trainer(limit_train_batches=batch_size, max_epochs=num_epochs) | ||
| trainer.fit(model=model, train_dataloaders=fmnist_train_loader) | ||
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