train-with-mlflow-logging.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Script from: https://github.com/PyTorchLightning/pytorch-lightning/blob/1.0.0rc2/pl_examples/basic_examples/autoencoder.py

from argparse import ArgumentParser
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from torch.utils.data import random_split
from pytorch_lightning.loggers import MLFlowLogger
import mlflow

try:
    from torchvision.datasets.mnist import MNIST
    from torchvision import transforms
except ModuleNotFoundError:
    from tests.base.datasets import MNIST


class LitAutoEncoder(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, 64), nn.ReLU(), nn.Linear(64, 3)
        )
        self.decoder = nn.Sequential(
            nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28 * 28)
        )

    def forward(self, x):
        # in lightning, forward defines the prediction/inference actions
        embedding = self.encoder(x)
        return embedding

    def training_step(self, batch, batch_idx):
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        self.log("loss", loss, on_epoch=True, on_step=False)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer


def cli_main():
    pl.seed_everything(1234)

    # ------------
    # args
    # ------------
    parser = ArgumentParser()
    parser.add_argument("--batch_size", default=32, type=int)
    parser.add_argument("--hidden_dim", type=int, default=128)
    parser = pl.Trainer.add_argparse_args(parser)
    args = parser.parse_args()

    # ------------
    # data
    # ------------
    dataset = MNIST("", train=True, download=True, transform=transforms.ToTensor())
    mnist_test = MNIST("", train=False, download=True, transform=transforms.ToTensor())
    mnist_train, mnist_val = random_split(dataset, [55000, 5000])

    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)

    # ------------
    # model
    # ------------
    model = LitAutoEncoder()

    # ------------
    # logging
    # ------------
    # get run object using mlflow
    with mlflow.start_run() as run:
        experiment_id = run.info.experiment_id
        # get the experiment name
        exp_name = mlflow.get_experiment(experiment_id).name
        # get the mlflow tracking uri
        mlflow_uri = mlflow.get_tracking_uri()

        mlf_logger = MLFlowLogger(experiment_name=exp_name, tracking_uri=mlflow_uri)
        # link the mlflowlogger run ID to the azureml run ID
        mlf_logger._run_id = run.info.run_id

        # ------------
        # training
        # ------------
        trainer = pl.Trainer.from_argparse_args(args, logger=mlf_logger)
        trainer.fit(model, train_loader, val_loader)

        # ------------
        # testing
        # ------------
        result = trainer.test(test_dataloaders=test_loader)
        print(result)


if __name__ == "__main__":
    cli_main()