[release] update torch_tune_serve_test to use anyscale connect

release/golden_notebook_tests/golden_notebook_tests.yaml

@@ -26,6 +26,7 @@
  compute_template: gpu_tpl.yaml
 
  run:
+ use_connect: True
  timeout: 1800
  script: python workloads/torch_tune_serve_test.py
 

release/golden_notebook_tests/workloads/torch_tune_serve_test.py

@@ -17,6 +17,13 @@
 from torchvision.datasets import MNIST
 
 
+def _is_anyscale_connect():
+ address = os.environ.get("RAY_ADDRESS")
+ is_anyscale_connect = address is not None and address.startswith(
+ "anyscale:https://")
+ return is_anyscale_connect
+
+
 def load_mnist_data(train: bool, download: bool):
  transform = transforms.Compose(
  [transforms.ToTensor(),
@@ -85,8 +92,33 @@ def train_mnist(test_mode=False, num_workers=1, use_gpu=False):
  checkpoint_at_end=True)
 
 
-def get_best_model(best_model_checkpoint_path):
- model_state = torch.load(best_model_checkpoint_path)
+def get_remote_model(remote_model_checkpoint_path):
+ if _is_anyscale_connect():
+ # Download training results to local client.
+ local_dir = "~/ray_results"
+ # TODO(matt): remove the following line when Anyscale Connect
+ # supports tilde expansion.
+ local_dir = os.path.expanduser(local_dir)
+ remote_dir = "/home/ray/ray_results/"
+ ray.client().download_results(
+ local_dir=local_dir, remote_dir=remote_dir)
+
+ # Compute local path.
+ rel_model_checkpoint_path = os.path.relpath(
+ remote_model_checkpoint_path, remote_dir)
+ local_model_checkpoint_path = os.path.join(local_dir,
+ rel_model_checkpoint_path)
+
+ # Load model reference.
+ return get_model(local_model_checkpoint_path)
+ else:
+ get_best_model_remote = ray.remote(get_model)
+ return ray.get(
+ get_best_model_remote.remote(remote_model_checkpoint_path))
+
+
+def get_model(model_checkpoint_path):
+ model_state = torch.load(model_checkpoint_path)
 
  model = ResNet18(None)
  model.conv1 = nn.Conv2d(
@@ -184,7 +216,12 @@ def test_predictions(test_mode=False):
 
  start = time.time()
 
- ray.client("anyscale:https://").connect()
+ client_builder = ray.client()
+ if (_is_anyscale_connect()):
+ job_name = os.environ.get("RAY_JOB_NAME", "torch_tune_serve_test")
+ client_builder.job_name(job_name)
+ client_builder.connect()
+
  num_workers = 2
  use_gpu = True
 
@@ -193,7 +230,7 @@ def test_predictions(test_mode=False):
 
  print("Retrieving best model.")
  best_checkpoint = analysis.best_checkpoint
- model_id = get_best_model(best_checkpoint)
+ model_id = get_remote_model(best_checkpoint)
 
  print("Setting up Serve.")
  setup_serve(model_id)