Spawn worker in custom environment

docs/user-guide/custom.md

@@ -224,12 +224,26 @@ To load a custom environment, [parallel inference](./parallel-inference)
 ```
 
 ```{warning}
-When loading custom environments, MLServer will always use the same Python
-interpreter that is used to run the main process.
-In other words, all custom environments will use the same version of Python
-than the main MLServer process.
+The main MLServer process communicates with workers in custom environments via
+[`multiprocessing.Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue)
+using pickled objects. Custom environments therefore **must** use the same
+version of MLServer and a compatible version of Python with the same [default
+pickle protocol](https://docs.python.org/3/library/pickle.html#pickle.DEFAULT_PROTOCOL)
+as the main process. Consult the tables below for environment compatibility.
 ```
 
+| Status | Description  |
+| ------ | ------------ |
+| 🔴     | Unsupported  |
+| 🟢     | Supported    |
+| 🔵     | Untested     |
+
+| Worker Python \ Server Python | 3.9 | 3.10 | 3.11 |
+| ----------------------------- | --- | ---- | ---- |
+| 3.9                           | 🟢  | 🟢   | 🔵   |
+| 3.10                          | 🟢  | 🟢   | 🔵   |
+| 3.11                          | 🔵  | 🔵   | 🔵   |
+
 If we take the [previous example](#loading-a-custom-mlserver-runtime) above as
 a reference, we could extend it to include our custom environment as:
 

mlserver/env.py

@@ -1,4 +1,5 @@
 import asyncio
+import multiprocessing
 import os
 import sys
 import tarfile
@@ -99,6 +100,13 @@ def _bin_path(self) -> str:
         """
         return os.path.join(self._env_path, "bin")
 
+    @cached_property
+    def _exec_path(self) -> str:
+        """
+        Path to python executable in our custom environment.
+        """
+        return os.path.join(self._bin_path, "python")
+
     @cached_property
     def _lib_path(self) -> str:
         """
@@ -118,11 +126,13 @@ def __enter__(self) -> "Environment":
         self._prev_sys_path = sys.path
         self._prev_bin_path = os.environ["PATH"]
 
+        multiprocessing.set_executable(self._exec_path)
         sys.path = [*self._sys_path, *self._prev_sys_path]
         os.environ["PATH"] = os.pathsep.join([self._bin_path, self._prev_bin_path])
 
         return self
 
     def __exit__(self, *exc_details) -> None:
+        multiprocessing.set_executable(sys.executable)
         sys.path = self._prev_sys_path
         os.environ["PATH"] = self._prev_bin_path

mlserver/parallel/pool.py

@@ -1,5 +1,6 @@
 import asyncio
 
+from contextlib import nullcontext
 from multiprocessing import Queue
 from typing import Awaitable, Callable, Dict, Optional, List, Iterable
 
@@ -24,6 +25,18 @@
 InferencePoolHook = Callable[[Worker], Awaitable[None]]
 
 
+def _spawn_worker(
+    settings: Settings,
+    responses: Queue,
+    env: Optional[Environment],
+) -> Worker:
+    with env or nullcontext():
+        worker = Worker(settings, responses, env)
+        worker.start()
+
+    return worker
+
+
 class WorkerRegistry:
     """
     Simple registry to keep track of which models have been loaded.
@@ -78,9 +91,8 @@ def __init__(
         self._worker_registry = WorkerRegistry()
         self._settings = settings
         self._responses: Queue[ModelResponseMessage] = Queue()
-        for idx in range(self._settings.parallel_workers):
-            worker = Worker(self._settings, self._responses, self._env)
-            worker.start()
+        for _ in range(self._settings.parallel_workers):
+            worker = _spawn_worker(self._settings, self._responses, self._env)
             self._workers[worker.pid] = worker  # type: ignore
 
         self._dispatcher = Dispatcher(self._workers, self._responses)
@@ -123,8 +135,7 @@ async def on_worker_stop(self, pid: int, exit_code: int):
         await self._start_worker()
 
     async def _start_worker(self) -> Worker:
-        worker = Worker(self._settings, self._responses, self._env)
-        worker.start()
+        worker = _spawn_worker(self._settings, self._responses, self._env)
         logger.info(f"Starting new worker with PID {worker.pid} on {self.name}...")
 
         # Add to dispatcher so that it can receive load requests and reload all

mlserver/parallel/registry.py

@@ -72,7 +72,7 @@ async def _handle_worker_stop(self, signum, frame):
 
         await self._default_pool.on_worker_stop(pid, exit_code)
         await asyncio.gather(
-            *[pool.on_worker_stop(pid) for pool in self._pools.values()]
+            *[pool.on_worker_stop(pid, exit_code) for pool in self._pools.values()]
         )
 
     async def _get_or_create(self, model: MLModel) -> InferencePool:

tests/cli/test_build_cases.py

@@ -5,6 +5,7 @@
 from typing import List
 
 from ..conftest import TESTS_PATH, TESTDATA_PATH
+from ..utils import _render_env_yml
 
 
 def _copy_test_files(
@@ -38,7 +39,11 @@ def case_environment_yml(tmp_path: str) -> str:
     """
     Custom model with environment provided through an environment.yml file.
     """
-    to_copy = ["env_models.py", "environment.yml"]
+    to_copy = ["env_models.py"]
+    _render_env_yml(
+        os.path.join(TESTDATA_PATH, "environment.yml"),
+        os.path.join(tmp_path, "environment.yml"),
+    )
     model_settings = {
         "name": "environment-yml",
         "implementation": "env_models.DummySKLearnModel",

tests/conftest.py

@@ -6,7 +6,7 @@
 import json
 
 from filelock import FileLock
-from typing import Dict, Any
+from typing import Dict, Any, Tuple
 from starlette_exporter import PrometheusMiddleware
 from prometheus_client.registry import REGISTRY, CollectorRegistry
 from unittest.mock import Mock
@@ -29,6 +29,13 @@
 from .fixtures import SumModel, ErrorModel, SimpleModel
 from .utils import RESTClient, get_available_ports, _pack, _get_tarball_name
 
+MIN_PYTHON_VERSION = (3, 9)
+MAX_PYTHON_VERSION = (3, 10)
+PYTHON_VERSIONS = [
+    (major, minor)
+    for major in range(MIN_PYTHON_VERSION[0], MAX_PYTHON_VERSION[0] + 1)
+    for minor in range(MIN_PYTHON_VERSION[1], MAX_PYTHON_VERSION[1] + 1)
+]
 TESTS_PATH = os.path.dirname(__file__)
 TESTDATA_PATH = os.path.join(TESTS_PATH, "testdata")
 TESTDATA_CACHE_PATH = os.path.join(TESTDATA_PATH, ".cache")
@@ -59,17 +66,28 @@ def testdata_cache_path() -> str:
     return TESTDATA_CACHE_PATH
 
 
+@pytest.fixture(
+    params=PYTHON_VERSIONS,
+    ids=[f"py{major}{minor}" for (major, minor) in PYTHON_VERSIONS],
+)
+def env_python_version(request: pytest.FixtureRequest) -> Tuple[int, int]:
+    return request.param
+
+
 @pytest.fixture
-async def env_tarball(testdata_cache_path: str) -> str:
-    tarball_name = _get_tarball_name()
+async def env_tarball(
+    env_python_version: Tuple[int, int],
+    testdata_cache_path: str,
+) -> str:
+    tarball_name = _get_tarball_name(env_python_version)
     tarball_path = os.path.join(testdata_cache_path, tarball_name)
 
     with FileLock(f"{tarball_path}.lock"):
         if os.path.isfile(tarball_path):
             return tarball_path
 
         env_yml = os.path.join(TESTDATA_PATH, "environment.yml")
-        await _pack(env_yml, tarball_path)
+        await _pack(env_python_version, env_yml, tarball_path)
 
     return tarball_path
 

tests/parallel/conftest.py

@@ -10,7 +10,7 @@
 from mlserver.env import Environment
 from mlserver.parallel.dispatcher import Dispatcher
 from mlserver.parallel.model import ModelMethods
-from mlserver.parallel.pool import InferencePool
+from mlserver.parallel.pool import InferencePool, _spawn_worker
 from mlserver.parallel.worker import Worker
 from mlserver.parallel.utils import configure_inference_pool, cancel_task
 from mlserver.parallel.messages import (
@@ -170,9 +170,7 @@ async def worker_with_env(
 ):
     # NOTE: This fixture will start an actual worker running on a separate
     # process.
-    worker = Worker(settings, responses, env)
-
-    worker.start()
+    worker = _spawn_worker(settings, responses, env)
 
     load_message = ModelUpdateMessage(
         update_type=ModelUpdateType.Load, model_settings=env_model_settings

tests/test_env.py

@@ -3,13 +3,15 @@
 import os
 import shutil
 
+from typing import Tuple
+
 from mlserver.env import Environment, compute_hash
 
 
 @pytest.fixture
-def expected_python_folder() -> str:
-    v = sys.version_info
-    return f"python{v.major}.{v.minor}"
+def expected_python_folder(env_python_version: Tuple[int, int]) -> str:
+    major, minor = env_python_version
+    return f"python{major}.{minor}"
 
 
 async def test_compute_hash(env_tarball: str):
@@ -68,6 +70,10 @@ def test_bin_path(env: Environment):
     assert env._bin_path == f"{env._env_path}/bin"
 
 
+def test_exec_path(env: Environment):
+    assert env._exec_path == f"{env._env_path}/bin/python"
+
+
 def test_activate_env(env: Environment):
     assert env._env_path not in ",".join(sys.path)
     assert env._env_path not in os.environ["PATH"]
@@ -82,5 +88,8 @@ def test_activate_env(env: Environment):
         bin_paths = os.environ["PATH"].split(os.pathsep)
         assert bin_paths[0] == env._bin_path
 
+        exec_path = os.path.join(bin_paths[0], "python")
+        assert exec_path == env._exec_path
+
     assert env._env_path not in ",".join(sys.path)
     assert env._env_path not in os.environ["PATH"]

tests/testdata/environment.yml

@@ -2,7 +2,7 @@ name: custom-runtime-environment
 channels:
   - conda-forge
 dependencies:
-  - python == 3.8
+  - python == 3.9
   - scikit-learn == 1.0.2
   - pip:
-      - mlserver == 1.3.0.dev2
+      - mlserver @ git+${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}.git@${GITHUB_REF}

tests/utils.py

@@ -3,13 +3,12 @@
 import socket
 import yaml
 
-import sys
 import os
 
 from itertools import filterfalse
-
+from string import Template
 from asyncio import subprocess
-from typing import List
+from typing import List, Tuple
 
 from aiohttp.client_exceptions import (
     ClientConnectorError,
@@ -51,7 +50,14 @@ async def _run(cmd):
         raise Exception(f"Command '{cmd}' failed with code '{return_code}'")
 
 
-def _read_env(env_yml) -> dict:
+def _render_env_yml(src_env_yml: str, dst_env_yml: str):
+    with open(src_env_yml, "r") as src_env_file:
+        env = Template(src_env_file.read()).substitute(os.environ)
+    with open(dst_env_yml, "w") as dst_env_file:
+        dst_env_file.write(env)
+
+
+def _read_env(env_yml: str) -> dict:
     with open(env_yml, "r") as env_file:
         return yaml.safe_load(env_file.read())
 
@@ -65,29 +71,30 @@ def _is_python(dep: str) -> bool:
     return "python" in dep
 
 
-def _inject_python_version(env_yml: str, tarball_path: str) -> str:
+def _inject_python_version(
+    version: Tuple[int, int],
+    env_yml: str,
+    tarball_path: str,
+) -> str:
     """
-    To ensure the same environment.yml fixture we've got works across
-    environments, we inject dynamically the current Python version.
-    That way, we ensure tests using the tarball to load a dynamic custom
-    environment are using the same Python version used to run the tests.
+    To test the same environment.yml fixture we've got with different Python
+    versions across environments, we inject dynamically the requested version.
     """
     env = _read_env(env_yml)
-
-    v = sys.version_info
+    major, minor = version
     without_python = list(filterfalse(_is_python, env["dependencies"]))
-    with_env_python = [f"python == {v.major}.{v.minor}", *without_python]
+    with_env_python = [f"python == {major}.{minor}", *without_python]
     env["dependencies"] = with_env_python
 
     dst_folder = os.path.dirname(tarball_path)
-    new_env_yml = os.path.join(dst_folder, f"environment-py{v.major}{v.minor}.yml")
+    new_env_yml = os.path.join(dst_folder, f"environment-py{major}{minor}.yml")
     _write_env(env, new_env_yml)
     return new_env_yml
 
 
-async def _pack(env_yml: str, tarball_path: str):
+async def _pack(version: Tuple[int, int], env_yml: str, tarball_path: str):
     uuid = generate_uuid()
-    fixed_env_yml = _inject_python_version(env_yml, tarball_path)
+    fixed_env_yml = _inject_python_version(version, env_yml, tarball_path)
     env_name = f"mlserver-{uuid}"
     try:
         await _run(f"conda env create -n {env_name} -f {fixed_env_yml}")
@@ -104,9 +111,9 @@ async def _pack(env_yml: str, tarball_path: str):
         await _run(f"conda env remove -n {env_name}")
 
 
-def _get_tarball_name() -> str:
-    v = sys.version_info
-    return f"environment-py{v.major}{v.minor}.tar.gz"
+def _get_tarball_name(version: Tuple[int, int]) -> str:
+    major, minor = version
+    return f"environment-py{major}{minor}.tar.gz"
 
 
 class RESTClient:

tox.ini

@@ -21,6 +21,10 @@ commands_pre =
 commands =
     python -m pytest {posargs} -n auto \
         {toxinidir}/tests
+set_env =
+    GITHUB_SERVER_URL = {env:GITHUB_SERVER_URL:https\://github.com}
+    GITHUB_REPOSITORY = {env:GITHUB_REPOSITORY:SeldonIO/MLServer}
+    GITHUB_REF        = {env:GITHUB_REF:refs/heads/master}
 
 [testenv:all-runtimes]
 commands_pre =