examples/lightning_classy_vision: support NAS

examples/apps/lightning_classy_vision/component.py

@@ -72,7 +72,7 @@ def trainer(
                 image=image,
                 resource=named_resources[resource]
                 if resource
-                else torchx.Resource(cpu=1, gpu=0, memMB=1024),
+                else torchx.Resource(cpu=1, gpu=0, memMB=1500),
             )
         ],
     )

examples/apps/lightning_classy_vision/model.py

@@ -14,7 +14,7 @@
 
 import os.path
 import subprocess
-from typing import Tuple
+from typing import Tuple, Optional, List
 
 import fsspec
 import pytorch_lightning as pl
@@ -29,15 +29,26 @@ class TinyImageNetModel(pl.LightningModule):
     An very simple linear model for the tiny image net dataset.
     """
 
-    def __init__(self) -> None:
+    def __init__(self, layer_sizes: Optional[List[int]] = None) -> None:
         super().__init__()
-        self.l1 = torch.nn.Linear(64 * 64, 4096)
+
+        # build a model with hidden layers specified by layer_sizes
+        if layer_sizes is None:
+            layer_sizes = []
+        dims = [64 * 64] + layer_sizes + [4096]
+        layers = []
+        for i, (a, b) in enumerate(zip(dims, dims[1:])):
+            if i > 0:
+                layers.append(torch.nn.ReLU(inplace=True))
+            layers.append(torch.nn.Linear(a, b))
+        self.seq = torch.nn.Sequential(*layers)
+
         self.train_acc = Accuracy()
         self.val_acc = Accuracy()
 
     # pyre-fixme[14]
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return torch.relu(self.l1(x.view(x.size(0), -1)))
+        return self.seq(x.view(x.size(0), -1))
 
     # pyre-fixme[14]
     def training_step(

examples/apps/lightning_classy_vision/profiler.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Simple Logging Profiler
+===========================
+
+This is a simple profiler that's used as part of the trainer app example. This
+logs the Lightning training stage durations a logger such as Tensorboard. This
+output is used for HPO optimization with Ax.
+"""
+
+import time
+from typing import Dict
+
+from pytorch_lightning.loggers.base import LightningLoggerBase
+from pytorch_lightning.profiler.base import BaseProfiler
+
+
+class SimpleLoggingProfiler(BaseProfiler):
+    """
+    This profiler records the duration of actions (in seconds) and reports the
+    mean duration of each action to the specified logger. Reported metrics are
+    in the format `duration_<event>`.
+    """
+
+    def __init__(self, logger: LightningLoggerBase) -> None:
+        super().__init__()
+
+        self.current_actions: Dict[str, float] = {}
+        self.logger = logger
+
+    def start(self, action_name: str) -> None:
+        if action_name in self.current_actions:
+            raise ValueError(
+                f"Attempted to start {action_name} which has already started."
+            )
+        self.current_actions[action_name] = time.monotonic()
+
+    def stop(self, action_name: str) -> None:
+        end_time = time.monotonic()
+        if action_name not in self.current_actions:
+            raise ValueError(
+                f"Attempting to stop recording an action ({action_name}) which was never started."
+            )
+        start_time = self.current_actions.pop(action_name)
+        duration = end_time - start_time
+        self.logger.log_metrics({"duration_" + action_name: duration})
+
+    def summary(self) -> str:
+        return ""

examples/apps/lightning_classy_vision/test/model_test.py

@@ -0,0 +1,31 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+from examples.apps.lightning_classy_vision.model import (
+    TinyImageNetModel,
+)
+
+
+class ModelTest(unittest.TestCase):
+    def test_basic(self) -> None:
+        model = TinyImageNetModel()
+        self.assertEqual(len(model.seq), 1)
+        out = model(torch.zeros((1, 64, 64)))
+        self.assertIsNotNone(out)
+
+    def test_layer_sizes(self) -> None:
+        model = TinyImageNetModel(
+            layer_sizes=[
+                10,
+                15,
+            ],
+        )
+        self.assertEqual(len(model.seq), 5)
+        out = model(torch.zeros((1, 64, 64)))
+        self.assertIsNotNone(out)

examples/apps/lightning_classy_vision/train.py

@@ -39,6 +39,9 @@
     TinyImageNetModel,
     export_inference_model,
 )
+from examples.apps.lightning_classy_vision.profiler import (
+    SimpleLoggingProfiler,
+)
 
 
 def parse_args(argv: List[str]) -> argparse.Namespace:
@@ -75,6 +78,12 @@ def parse_args(argv: List[str]) -> argparse.Namespace:
         help="path to place the tensorboard logs",
         default="/tmp",
     )
+    parser.add_argument(
+        "--layers",
+        nargs="+",
+        type=int,
+        help="the MLP hidden layers and sizes, used for neural architecture search",
+    )
     return parser.parse_args(argv)
 
 
@@ -83,7 +92,7 @@ def main(argv: List[str]) -> None:
         args = parse_args(argv)
 
         # Init our model
-        model = TinyImageNetModel()
+        model = TinyImageNetModel(args.layers)
 
         # Download and setup the data module
         if args.test:
@@ -124,6 +133,7 @@ def main(argv: List[str]) -> None:
             logger=logger,
             max_epochs=args.epochs,
             callbacks=[checkpoint_callback],
+            profiler=SimpleLoggingProfiler(logger),
         )
 
         # Train the model ⚡

scripts/kfpint.py

@@ -209,7 +209,7 @@ def run_pipeline(build: BuildInfo, pipeline_file: str) -> object:
         experiment_name="integration-tests",
         run_name=f"integration test {build.id} - {os.path.basename(pipeline_file)}",
     )
-    ui_url = f"{HOST}/_/pipeline/#/runs/details/{resp.run_id}"
+    ui_url = f"{HOST}/#/runs/details/{resp.run_id}"
     print(f"{resp.run_id} - launched! view run at {ui_url}")
     return resp