deploy: fc38b4d

releases/ant-2.41.0/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: a06a8034754b76e2f52e9b7d51eb4b56
+tags: 645f666f9bcd5a90fca523b33c5a78b7

releases/ant-2.41.0/_downloads/6adeb623647a7151ee6b7b6553dda299/tune-default.yaml

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+cluster_name: tune-default
+provider: {type: aws, region: us-west-2}
+auth: {ssh_user: ubuntu}
+min_workers: 3
+max_workers: 3
+# Deep Learning AMI (Ubuntu) Version 21.0
+available_node_types:
+  head_node:
+    node_config: {InstanceType: c5.xlarge, ImageId: ami-0b294f219d14e6a82}
+  worker_nodes:
+    node_config: {InstanceType: c5.xlarge, ImageId: ami-0b294f219d14e6a82}
+head_node_type: head_node
+setup_commands: # Set up each node.
+    - pip install ray torch torchvision tensorboard

releases/ant-2.41.0/_downloads/8515735d7ae45b272ee9ef78046d863d/mnist_pytorch_trainable.py

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+# Original Code here:
+# https://github.com/pytorch/examples/blob/master/mnist/main.py
+from __future__ import print_function
+
+import argparse
+import os
+
+import torch
+import torch.optim as optim
+
+import ray
+from ray import train, tune
+from ray.tune.examples.mnist_pytorch import (
+    ConvNet,
+    get_data_loaders,
+    test_func,
+    train_func,
+)
+from ray.tune.schedulers import ASHAScheduler
+
+# Change these values if you want the training to run quicker or slower.
+EPOCH_SIZE = 512
+TEST_SIZE = 256
+
+# Training settings
+parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
+parser.add_argument(
+    "--use-gpu", action="store_true", default=False, help="enables CUDA training"
+)
+parser.add_argument("--ray-address", type=str, help="The Redis address of the cluster.")
+parser.add_argument(
+    "--smoke-test", action="store_true", help="Finish quickly for testing"
+)
+
+
+# Below comments are for documentation purposes only.
+# fmt: off
+# __trainable_example_begin__
+class TrainMNIST(tune.Trainable):
+    def setup(self, config):
+        use_cuda = config.get("use_gpu") and torch.cuda.is_available()
+        self.device = torch.device("cuda" if use_cuda else "cpu")
+        self.train_loader, self.test_loader = get_data_loaders()
+        self.model = ConvNet().to(self.device)
+        self.optimizer = optim.SGD(
+            self.model.parameters(),
+            lr=config.get("lr", 0.01),
+            momentum=config.get("momentum", 0.9))
+
+    def step(self):
+        train_func(
+            self.model, self.optimizer, self.train_loader, device=self.device)
+        acc = test_func(self.model, self.test_loader, self.device)
+        return {"mean_accuracy": acc}
+
+    def save_checkpoint(self, checkpoint_dir):
+        checkpoint_path = os.path.join(checkpoint_dir, "model.pth")
+        torch.save(self.model.state_dict(), checkpoint_path)
+
+    def load_checkpoint(self, checkpoint_dir):
+        checkpoint_path = os.path.join(checkpoint_dir, "model.pth")
+        self.model.load_state_dict(torch.load(checkpoint_path))
+
+
+# __trainable_example_end__
+# fmt: on
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    ray.init(address=args.ray_address, num_cpus=6 if args.smoke_test else None)
+    sched = ASHAScheduler()
+
+    tuner = tune.Tuner(
+        tune.with_resources(TrainMNIST, resources={"cpu": 3, "gpu": int(args.use_gpu)}),
+        run_config=train.RunConfig(
+            stop={
+                "mean_accuracy": 0.95,
+                "training_iteration": 3 if args.smoke_test else 20,
+            },
+            checkpoint_config=train.CheckpointConfig(
+                checkpoint_at_end=True, checkpoint_frequency=3
+            ),
+        ),
+        tune_config=tune.TuneConfig(
+            metric="mean_accuracy",
+            mode="max",
+            scheduler=sched,
+            num_samples=1 if args.smoke_test else 20,
+        ),
+        param_space={
+            "args": args,
+            "lr": tune.uniform(0.001, 0.1),
+            "momentum": tune.uniform(0.1, 0.9),
+        },
+    )
+    results = tuner.fit()
+
+    print("Best config is:", results.get_best_result().config)