Legacy: simple classif training

.github/workflows/legacy-checkpoints.yml

@@ -0,0 +1,40 @@
+name: Create Legacy Ckpts
+
+# https://help.github.com/en/actions/reference/events-that-trigger-workflows
+on:
+  workflow_dispatch:
+
+jobs:
+  create-legacy-ckpts:
+    runs-on: ubuntu-20.04
+    steps:
+    - uses: actions/checkout@v2
+
+    - uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+
+    - name: Install dependencies
+      run: |
+        pip install -r requirements.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html --quiet
+        pip install awscli
+
+    - name: Configure AWS credentials
+      uses: aws-actions/configure-aws-credentials@v1
+      with:
+        aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+        aws-secret-access-key: ${{ secrets.AWS_SECRET_KEY_ID }}
+        aws-region: us-east-1
+
+    - name: Generate checkpoint
+      run: |
+        while IFS= read -r line; do
+            bash legacy/generate_checkpoints.sh $line
+        done <<< $(cat legacy/back-compatible-versions.txt)
+
+    - name: Push files to S3
+      working-directory: ./legacy
+      run: |
+        aws s3 sync legacy/checkpoints/ s3://pl-public-data/legacy/checkpoints/
+        zip -r checkpoints.zip checkpoints
+        aws s3 cp checkpoints.zip s3://pl-public-data/legacy/ --acl public-read

legacy/README.md

@@ -14,6 +14,6 @@ unzip -o checkpoints.zip
 To back populate collection with past version you can use following bash:
 
 ```bash
-bash generate_checkpoints.sh 1.0.2 1.0.3 1.0.4
+bash generate_checkpoints.sh "1.3.7" "1.3.8"
 zip -r checkpoints.zip checkpoints/
 ```

legacy/back-compatible-versions.txt

@@ -0,0 +1,39 @@
+1.0.0
+1.0.1
+1.0.2
+1.0.3
+1.0.4
+1.0.5
+1.0.6
+1.0.7
+1.0.8
+1.1.0
+1.1.1
+1.1.2
+1.1.3
+1.1.4
+1.1.5
+1.1.6
+1.1.7
+1.1.8
+1.2.0
+1.2.1
+1.2.2
+1.2.3
+1.2.4
+1.2.5
+1.2.6
+1.2.7
+1.2.8
+1.2.10
+1.3.0
+1.3.1
+1.3.2
+1.3.3
+1.3.4
+1.3.5
+1.3.6
+1.3.7
+1.3.8
+1.4.0
+1.4.1

legacy/generate_checkpoints.sh

@@ -2,12 +2,14 @@
 # Sample call:
 #  bash generate_checkpoints.sh 1.0.2 1.0.3 1.0.4
 
+set -e
+
 LEGACY_PATH="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
-FROZEN_MIN_PT_VERSION="1.4"
+FROZEN_MIN_PT_VERSION="1.6"
 
 echo $LEGACY_PATH
 # install some PT version here so it does not need to reinstalled for each env
-pip install virtualenv "torch==1.5" --quiet --no-cache-dir
+pip install virtualenv "torch==1.6" --quiet
 
 ENV_PATH="$LEGACY_PATH/vEnv"
 
@@ -23,14 +25,14 @@ do
   # activate and install PL version
   source "$ENV_PATH/bin/activate"
   # there are problem to load ckpt in older versions since they are saved the newer versions
-  pip install "pytorch_lightning==$ver" "torch==$FROZEN_MIN_PT_VERSION" --quiet --no-cache-dir
+  pip install "pytorch_lightning==$ver" "torch==$FROZEN_MIN_PT_VERSION" "torchmetrics" "scikit-learn" --quiet
 
   python --version
   pip --version
   pip list | grep torch
 
-  python "$LEGACY_PATH/zero_training.py"
-  cp "$LEGACY_PATH/zero_training.py" ${LEGACY_PATH}/checkpoints/${ver}
+  python "$LEGACY_PATH/simple_classif_training.py"
+  cp "$LEGACY_PATH/simple_classif_training.py" ${LEGACY_PATH}/checkpoints/${ver}
 
   mv ${LEGACY_PATH}/checkpoints/${ver}/lightning_logs/version_0/checkpoints/*.ckpt ${LEGACY_PATH}/checkpoints/${ver}/
   rm -rf ${LEGACY_PATH}/checkpoints/${ver}/lightning_logs

legacy/simple_classif_training.py

@@ -0,0 +1,170 @@
+# 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.
+import os
+
+import torch
+import torch.nn.functional as F
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from torch import nn
+from torch.utils.data import DataLoader, Dataset
+from torchmetrics import Accuracy
+
+import pytorch_lightning as pl
+from pytorch_lightning import LightningDataModule, LightningModule, seed_everything
+from pytorch_lightning.callbacks import EarlyStopping
+
+PATH_LEGACY = os.path.dirname(__file__)
+
+
+class SklearnDataset(Dataset):
+    def __init__(self, x, y, x_type, y_type):
+        self.x = x
+        self.y = y
+        self._x_type = x_type
+        self._y_type = y_type
+
+    def __getitem__(self, idx):
+        return torch.tensor(self.x[idx], dtype=self._x_type), torch.tensor(self.y[idx], dtype=self._y_type)
+
+    def __len__(self):
+        return len(self.y)
+
+
+class SklearnDataModule(LightningDataModule):
+    def __init__(self, sklearn_dataset, x_type, y_type, batch_size: int = 128):
+        super().__init__()
+        self.batch_size = batch_size
+        self._x, self._y = sklearn_dataset
+        self._split_data()
+        self._x_type = x_type
+        self._y_type = y_type
+
+    def _split_data(self):
+        self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(
+            self._x, self._y, test_size=0.20, random_state=42
+        )
+        self.x_train, self.x_valid, self.y_train, self.y_valid = train_test_split(
+            self.x_train, self.y_train, test_size=0.40, random_state=42
+        )
+
+    def train_dataloader(self):
+        return DataLoader(
+            SklearnDataset(self.x_train, self.y_train, self._x_type, self._y_type),
+            shuffle=True,
+            batch_size=self.batch_size,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            SklearnDataset(self.x_valid, self.y_valid, self._x_type, self._y_type), batch_size=self.batch_size
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            SklearnDataset(self.x_test, self.y_test, self._x_type, self._y_type), batch_size=self.batch_size
+        )
+
+
+class ClassifDataModule(SklearnDataModule):
+    def __init__(self, num_features=24, length=6000, num_classes=3, batch_size=128):
+        data = make_classification(
+            n_samples=length,
+            n_features=num_features,
+            n_classes=num_classes,
+            n_clusters_per_class=2,
+            n_informative=int(num_features / num_classes),
+            random_state=42,
+        )
+        super().__init__(data, x_type=torch.float32, y_type=torch.long, batch_size=batch_size)
+
+
+class ClassificationModel(LightningModule):
+    def __init__(self, num_features=24, num_classes=3, lr=0.01):
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.lr = lr
+        for i in range(3):
+            setattr(self, f"layer_{i}", nn.Linear(num_features, num_features))
+            setattr(self, f"layer_{i}a", torch.nn.ReLU())
+        setattr(self, "layer_end", nn.Linear(num_features, num_classes))
+
+        self.train_acc = Accuracy()
+        self.valid_acc = Accuracy()
+        self.test_acc = Accuracy()
+
+    def forward(self, x):
+        x = self.layer_0(x)
+        x = self.layer_0a(x)
+        x = self.layer_1(x)
+        x = self.layer_1a(x)
+        x = self.layer_2(x)
+        x = self.layer_2a(x)
+        x = self.layer_end(x)
+        logits = F.softmax(x, dim=1)
+        return logits
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
+        return [optimizer], []
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self.forward(x)
+        loss = F.cross_entropy(logits, y)
+        self.log("train_loss", loss, prog_bar=True)
+        self.log("train_acc", self.train_acc(logits, y), prog_bar=True)
+        return {"loss": loss}
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self.forward(x)
+        self.log("val_loss", F.cross_entropy(logits, y), prog_bar=False)
+        self.log("val_acc", self.valid_acc(logits, y), prog_bar=True)
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self.forward(x)
+        self.log("test_loss", F.cross_entropy(logits, y), prog_bar=False)
+        self.log("test_acc", self.test_acc(logits, y), prog_bar=True)
+
+
+def main_train(dir_path, max_epochs: int = 20):
+    seed_everything(42)
+    stopping = EarlyStopping(monitor="val_acc", mode="max", min_delta=0.005)
+    trainer = pl.Trainer(
+        default_root_dir=dir_path,
+        gpus=int(torch.cuda.is_available()),
+        precision=(16 if torch.cuda.is_available() else 32),
+        checkpoint_callback=True,
+        callbacks=[stopping],
+        min_epochs=3,
+        max_epochs=max_epochs,
+        accumulate_grad_batches=2,
+        deterministic=True,
+    )
+
+    dm = ClassifDataModule()
+    model = ClassificationModel()
+    trainer.fit(model, datamodule=dm)
+    res = trainer.test(model, datamodule=dm)
+    assert res[0]["test_loss"] <= 0.7
+    assert res[0]["test_acc"] >= 0.85
+    assert trainer.current_epoch < (max_epochs - 1)
+
+
+if __name__ == "__main__":
+    path_dir = os.path.join(PATH_LEGACY, "checkpoints", str(pl.__version__))
+    main_train(path_dir)