Add Ray Cluster Upgrade test for Ray Job Long Running scenarios

Author: Srihari1192

tests/e2e/mnist_sleep.py

@@ -0,0 +1,253 @@
+# Copyright 2022 IBM, Red Hat
+#
+# 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 time
+import torch
+import requests
+from pytorch_lightning import LightningModule, Trainer
+from pytorch_lightning.callbacks.progress import TQDMProgressBar
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader, random_split, RandomSampler
+from torchmetrics import Accuracy
+from torchvision import transforms
+from torchvision.datasets import MNIST
+import gzip
+import shutil
+from minio import Minio
+
+
+PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
+BATCH_SIZE = 256 if torch.cuda.is_available() else 64
+
+local_mnist_path = os.path.dirname(os.path.abspath(__file__))
+# %%
+
+print("prior to running the trainer")
+print("MASTER_ADDR: is ", os.getenv("MASTER_ADDR"))
+print("MASTER_PORT: is ", os.getenv("MASTER_PORT"))
+
+print("ACCELERATOR: is ", os.getenv("ACCELERATOR"))
+ACCELERATOR = os.getenv("ACCELERATOR")
+
+STORAGE_BUCKET_EXISTS = "AWS_DEFAULT_ENDPOINT" in os.environ
+print("STORAGE_BUCKET_EXISTS: ", STORAGE_BUCKET_EXISTS)
+
+print(
+    f'Storage_Bucket_Default_Endpoint : is {os.environ.get("AWS_DEFAULT_ENDPOINT")}'
+    if "AWS_DEFAULT_ENDPOINT" in os.environ
+    else ""
+)
+print(
+    f'Storage_Bucket_Name : is {os.environ.get("AWS_STORAGE_BUCKET")}'
+    if "AWS_STORAGE_BUCKET" in os.environ
+    else ""
+)
+print(
+    f'Storage_Bucket_Mnist_Directory : is {os.environ.get("AWS_STORAGE_BUCKET_MNIST_DIR")}'
+    if "AWS_STORAGE_BUCKET_MNIST_DIR" in os.environ
+    else ""
+)
+
+
+class LitMNIST(LightningModule):
+    def __init__(self, data_dir=PATH_DATASETS, hidden_size=64, learning_rate=2e-4):
+        super().__init__()
+
+        # Set our init args as class attributes
+        self.data_dir = data_dir
+        self.hidden_size = hidden_size
+        self.learning_rate = learning_rate
+
+        # Hardcode some dataset specific attributes
+        self.num_classes = 10
+        self.dims = (1, 28, 28)
+        channels, width, height = self.dims
+        self.transform = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize((0.1307,), (0.3081,)),
+            ]
+        )
+
+        # Define PyTorch model
+        self.model = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(channels * width * height, hidden_size),
+            nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.Linear(hidden_size, self.num_classes),
+        )
+
+        self.val_accuracy = Accuracy()
+        self.test_accuracy = Accuracy()
+
+    def forward(self, x):
+        x = self.model(x)
+        return F.log_softmax(x, dim=1)
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self(x)
+        loss = F.nll_loss(logits, y)
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self(x)
+        loss = F.nll_loss(logits, y)
+        preds = torch.argmax(logits, dim=1)
+        self.val_accuracy.update(preds, y)
+
+        # Calling self.log will surface up scalars for you in TensorBoard
+        self.log("val_loss", loss, prog_bar=True)
+        self.log("val_acc", self.val_accuracy, prog_bar=True)
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self(x)
+        loss = F.nll_loss(logits, y)
+        preds = torch.argmax(logits, dim=1)
+        self.test_accuracy.update(preds, y)
+
+        # Calling self.log will surface up scalars for you in TensorBoard
+        self.log("test_loss", loss, prog_bar=True)
+        self.log("test_acc", self.test_accuracy, prog_bar=True)
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
+        return optimizer
+
+    def on_train_start(self):
+        # Sleeping for 24 hours for upgrade test scenario
+        print("Sleeping for 24 hours before starting training...")
+        time.sleep(86400)
+        print("Waking up from sleep...")
+
+    ####################
+    # DATA RELATED HOOKS
+    ####################
+
+    def prepare_data(self):
+        # download
+        print("Downloading MNIST dataset...")
+
+        if (
+            STORAGE_BUCKET_EXISTS
+            and os.environ.get("AWS_DEFAULT_ENDPOINT") != ""
+            and os.environ.get("AWS_DEFAULT_ENDPOINT") != None
+        ):
+            print("Using storage bucket to download datasets...")
+
+            dataset_dir = os.path.join(self.data_dir, "MNIST/raw")
+            endpoint = os.environ.get("AWS_DEFAULT_ENDPOINT")
+            access_key = os.environ.get("AWS_ACCESS_KEY_ID")
+            secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY")
+            bucket_name = os.environ.get("AWS_STORAGE_BUCKET")
+
+            client = Minio(
+                endpoint,
+                access_key=access_key,
+                secret_key=secret_key,
+                cert_check=False,
+            )
+
+            if not os.path.exists(dataset_dir):
+                os.makedirs(dataset_dir)
+            else:
+                print(f"Directory '{dataset_dir}' already exists")
+
+            # To download datasets from storage bucket's specific directory, use prefix to provide directory name
+            prefix = os.environ.get("AWS_STORAGE_BUCKET_MNIST_DIR")
+            # download all files from prefix folder of storage bucket recursively
+            for item in client.list_objects(bucket_name, prefix=prefix, recursive=True):
+                file_name = item.object_name[len(prefix) + 1 :]
+                dataset_file_path = os.path.join(dataset_dir, file_name)
+                if not os.path.exists(dataset_file_path):
+                    client.fget_object(bucket_name, item.object_name, dataset_file_path)
+                else:
+                    print(f"File-path '{dataset_file_path}' already exists")
+                # Unzip files
+                with gzip.open(dataset_file_path, "rb") as f_in:
+                    with open(dataset_file_path.split(".")[:-1][0], "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+                # delete zip file
+                os.remove(dataset_file_path)
+                unzipped_filepath = dataset_file_path.split(".")[0]
+                if os.path.exists(unzipped_filepath):
+                    print(
+                        f"Unzipped and saved dataset file to path - {unzipped_filepath}"
+                    )
+            download_datasets = False
+
+        else:
+            print("Using default MNIST mirror reference to download datasets...")
+            download_datasets = True
+
+        MNIST(self.data_dir, train=True, download=download_datasets)
+        MNIST(self.data_dir, train=False, download=download_datasets)
+
+    def setup(self, stage=None):
+        # Assign train/val datasets for use in dataloaders
+        if stage == "fit" or stage is None:
+            mnist_full = MNIST(
+                self.data_dir, train=True, transform=self.transform, download=False
+            )
+            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
+
+        # Assign test dataset for use in dataloader(s)
+        if stage == "test" or stage is None:
+            self.mnist_test = MNIST(
+                self.data_dir, train=False, transform=self.transform, download=False
+            )
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.mnist_train,
+            batch_size=BATCH_SIZE,
+            sampler=RandomSampler(self.mnist_train, num_samples=1000),
+        )
+
+    def val_dataloader(self):
+        return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)
+
+    def test_dataloader(self):
+        return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)
+
+
+# Init DataLoader from MNIST Dataset
+
+model = LitMNIST(data_dir=local_mnist_path)
+
+print("GROUP: ", int(os.environ.get("GROUP_WORLD_SIZE", 1)))
+print("LOCAL: ", int(os.environ.get("LOCAL_WORLD_SIZE", 1)))
+
+# Initialize a trainer
+trainer = Trainer(
+    accelerator=ACCELERATOR,
+    # devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs
+    max_epochs=3,
+    callbacks=[TQDMProgressBar(refresh_rate=20)],
+    num_nodes=int(os.environ.get("GROUP_WORLD_SIZE", 1)),
+    devices=int(os.environ.get("LOCAL_WORLD_SIZE", 1)),
+    replace_sampler_ddp=False,
+    strategy="ddp",
+)
+
+# Train the model ⚡
+trainer.fit(model)