feature: support local mode in SageMaker Studio (aws#1300) (aws#4347)

* feature: support local mode in SageMaker Studio

* chore: fix typo

* chore: fix formatting

* chore: revert changes for docker compose logs

* chore: black-format

* change: Use predtermined dns-allow-listed-hostname for Studio Local Support

* add support for CodeEditor and JupyterLabs

---------

Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com>
Co-authored-by: Mufaddal Rohawala <mufi@amazon.com>

src/sagemaker/local/image.py

@@ -42,6 +42,7 @@
 import sagemaker.utils
 
 CONTAINER_PREFIX = "algo"
+STUDIO_HOST_NAME = "sagemaker-local"
 DOCKER_COMPOSE_FILENAME = "docker-compose.yaml"
 DOCKER_COMPOSE_HTTP_TIMEOUT_ENV = "COMPOSE_HTTP_TIMEOUT"
 DOCKER_COMPOSE_HTTP_TIMEOUT = "120"
@@ -50,6 +51,7 @@
 REGION_ENV_NAME = "AWS_REGION"
 TRAINING_JOB_NAME_ENV_NAME = "TRAINING_JOB_NAME"
 S3_ENDPOINT_URL_ENV_NAME = "S3_ENDPOINT_URL"
+SM_STUDIO_LOCAL_MODE = "SM_STUDIO_LOCAL_MODE"
 
 # SELinux Enabled
 SELINUX_ENABLED = os.environ.get("SAGEMAKER_LOCAL_SELINUX_ENABLED", "False").lower() in [
@@ -107,10 +109,30 @@ def __init__(
         # Since we are using a single docker network, Generate a random suffix to attach to the
         # container names. This way multiple jobs can run in parallel.
         suffix = "".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(5))
-        self.hosts = [
-            "{}-{}-{}".format(CONTAINER_PREFIX, i, suffix)
-            for i in range(1, self.instance_count + 1)
-        ]
+        self.is_studio = sagemaker.local.utils.check_for_studio()
+        if self.is_studio:
+            if self.instance_count > 1:
+                raise NotImplementedError(
+                    "Multi instance Local Mode execution is "
+                    "currently not supported in SageMaker Studio."
+                )
+            # For studio use-case, directories need to be created in `~/tmp`, rather than /tmp
+            home = os.path.expanduser("~")
+            root_dir = os.path.join(home, "tmp")
+            if not os.path.isdir(root_dir):
+                os.mkdir(root_dir)
+            if self.sagemaker_session.config:
+                self.sagemaker_session.config["local"]["container_root"] = root_dir
+            else:
+                self.sagemaker_session.config = {"local": {"container_root": root_dir}}
+            # Studio only supports single instance run
+            self.hosts = [STUDIO_HOST_NAME]
+        else:
+            self.hosts = [
+                "{}-{}-{}".format(CONTAINER_PREFIX, i, suffix)
+                for i in range(1, self.instance_count + 1)
+            ]
+
         self.container_root = None
         self.container = None
 
@@ -201,22 +223,17 @@ def process(
         self._generate_compose_file(
             "process", additional_volumes=volumes, additional_env_vars=environment
         )
-        compose_command = self._compose()
 
         if _ecr_login_if_needed(self.sagemaker_session.boto_session, self.image):
             _pull_image(self.image)
 
+        compose_command = self._compose()
         process = subprocess.Popen(
             compose_command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
         )
 
         try:
             _stream_output(process)
-        except RuntimeError as e:
-            # _stream_output() doesn't have the command line. We will handle the exception
-            # which contains the exit code and append the command line to it.
-            msg = f"Failed to run: {compose_command}"
-            raise RuntimeError(msg) from e
         finally:
             # Uploading processing outputs back to Amazon S3.
             self._upload_processing_outputs(data_dir, processing_output_config)
@@ -283,22 +300,17 @@ def train(self, input_data_config, output_data_config, hyperparameters, environm
         compose_data = self._generate_compose_file(
             "train", additional_volumes=volumes, additional_env_vars=training_env_vars
         )
-        compose_command = self._compose()
 
         if _ecr_login_if_needed(self.sagemaker_session.boto_session, self.image):
             _pull_image(self.image)
 
+        compose_command = self._compose()
         process = subprocess.Popen(
             compose_command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
         )
 
         try:
             _stream_output(process)
-        except RuntimeError as e:
-            # _stream_output() doesn't have the command line. We will handle the exception
-            # which contains the exit code and append the command line to it.
-            msg = "Failed to run: %s, %s" % (compose_command, str(e))
-            raise RuntimeError(msg)
         finally:
             artifacts = self.retrieve_artifacts(compose_data, output_data_config, job_name)
 
@@ -347,6 +359,7 @@ def serve(self, model_dir, environment):
         self._generate_compose_file(
             "serve", additional_env_vars=environment, additional_volumes=volumes
         )
+
         compose_command = self._compose()
 
         self.container = _HostingContainer(compose_command)
@@ -710,6 +723,9 @@ def _generate_compose_file(self, command, additional_volumes=None, additional_en
         additional_env_var_list = ["{}={}".format(k, v) for k, v in additional_env_vars.items()]
         environment.extend(additional_env_var_list)
 
+        if self.is_studio:
+            environment.extend([f"{SM_STUDIO_LOCAL_MODE}=True"])
+
         if os.environ.get(DOCKER_COMPOSE_HTTP_TIMEOUT_ENV) is None:
             os.environ[DOCKER_COMPOSE_HTTP_TIMEOUT_ENV] = DOCKER_COMPOSE_HTTP_TIMEOUT
 
@@ -723,12 +739,19 @@ def _generate_compose_file(self, command, additional_volumes=None, additional_en
             for h in self.hosts
         }
 
-        content = {
-            # Use version 2.3 as a minimum so that we can specify the runtime
-            "version": "2.3",
-            "services": services,
-            "networks": {"sagemaker-local": {"name": "sagemaker-local"}},
-        }
+        if self.is_studio:
+            content = {
+                # Use version 2.3 as a minimum so that we can specify the runtime
+                "version": "2.3",
+                "services": services,
+            }
+        else:
+            content = {
+                # Use version 2.3 as a minimum so that we can specify the runtime
+                "version": "2.3",
+                "services": services,
+                "networks": {"sagemaker-local": {"name": "sagemaker-local"}},
+            }
 
         docker_compose_path = os.path.join(self.container_root, DOCKER_COMPOSE_FILENAME)
 
@@ -810,7 +833,6 @@ def _create_docker_host(
             "tty": True,
             "volumes": [v.map for v in optml_volumes],
             "environment": environment,
-            "networks": {"sagemaker-local": {"aliases": [host]}},
         }
 
         is_train_with_entrypoint = False
@@ -827,14 +849,19 @@ def _create_docker_host(
             if self.container_arguments:
                 host_config["entrypoint"] = host_config["entrypoint"] + self.container_arguments
 
+        if self.is_studio:
+            host_config["network_mode"] = "sagemaker"
+        else:
+            host_config["networks"] = {"sagemaker-local": {"aliases": [host]}}
+
         # for GPU support pass in nvidia as the runtime, this is equivalent
         # to setting --runtime=nvidia in the docker commandline.
         if self.instance_type == "local_gpu":
             host_config["deploy"] = {
                 "resources": {"reservations": {"devices": [{"capabilities": ["gpu"]}]}}
             }
 
-        if command == "serve":
+        if not self.is_studio and command == "serve":
             serving_port = (
                 sagemaker.utils.get_config_value(
                     "local.serving_port", self.sagemaker_session.config
@@ -910,7 +937,7 @@ def __init__(self, command):
         """Creates a new threaded hosting container.
 
         Args:
-            command:
+            command (dict): docker compose command
         """
         Thread.__init__(self)
         self.command = command
@@ -987,8 +1014,8 @@ def _stream_output(process):
         sys.stdout.write(stdout)
         exit_code = process.poll()
 
-    if exit_code != 0:
-        raise RuntimeError("Process exited with code: %s" % exit_code)
+    if exit_code not in [0, 130]:
+        raise RuntimeError(f"Failed to run: {process.args}. Process exited with code: {exit_code}")
 
     return exit_code
 

src/sagemaker/local/utils.py

@@ -28,6 +28,8 @@
 
 logger = logging.getLogger(__name__)
 
+STUDIO_APP_TYPES = ["KernelGateway", "CodeEditor", "JupyterLab"]
+
 
 def copy_directory_structure(destination_directory, relative_path):
     """Creates intermediate directory structure for relative_path.
@@ -216,3 +218,29 @@ def get_using_dot_notation(dictionary, keys):
         return get_using_dot_notation(inner_dict, rest)
     except (KeyError, IndexError, TypeError):
         raise ValueError(f"{keys} does not exist in input dictionary.")
+
+
+def check_for_studio():
+    """Helper function to determine if the run environment is studio.
+
+    Returns (bool): Returns True if valid Studio request.
+
+    Raises:
+        NotImplementedError:
+            if run environment = Studio and AppType not in STUDIO_APP_TYPES
+    """
+    is_studio = False
+    if os.path.exists("/opt/ml/metadata/resource-metadata.json"):
+        with open("/opt/ml/metadata/resource-metadata.json", "r") as handle:
+            metadata = json.load(handle)
+        app_type = metadata.get("AppType")
+        if app_type:
+            # check if the execution is triggered from Studio KernelGateway App
+            if app_type in STUDIO_APP_TYPES:
+                is_studio = True
+            else:
+                raise NotImplementedError(
+                    f"AppType {app_type} in Studio does not support Local Mode."
+                )
+        # if no apptype, case of classic notebooks
+    return is_studio