feat: add config-based named_resources support (#1085)

docs/source/advanced.rst

@@ -153,6 +153,33 @@ resource can then be used in the following manner:
 
   test_app("gpu_x2")
 
+Alternatively, you can define custom named resources in a Python module and point
+to it using the ``TORCHX_CUSTOM_NAMED_RESOURCES`` environment variable:
+
+.. code-block:: python
+
+   # my_resources.py
+   from torchx.specs import Resource
+
+   def gpu_x8_efa() -> Resource:
+       return Resource(cpu=100, gpu=8, memMB=819200, devices={"vpc.amazonaws.com/efa": 1})
+
+   def cpu_x32() -> Resource:
+       return Resource(cpu=32, gpu=0, memMB=131072)
+
+   NAMED_RESOURCES = {
+       "gpu_x8_efa": gpu_x8_efa,
+       "cpu_x32": cpu_x32,
+   }
+
+Then set the environment variable:
+
+.. code-block:: bash
+
+   export TORCHX_CUSTOM_NAMED_RESOURCES=my_resources
+
+This allows you to use your custom resources without creating a package with entry points.
+
 
 Registering Custom Components
 -------------------------------

torchx/specs/__init__.py

@@ -12,6 +12,8 @@
 scheduler or pipeline adapter.
 """
 import difflib
+
+import os
 from typing import Callable, Dict, Mapping, Optional
 
 from torchx.specs.api import (
@@ -63,8 +65,10 @@
 GENERIC_NAMED_RESOURCES: Mapping[str, ResourceFactory] = import_attr(
     "torchx.specs.named_resources_generic", "NAMED_RESOURCES", default={}
 )
-FB_NAMED_RESOURCES: Mapping[str, ResourceFactory] = import_attr(
-    "torchx.specs.fb.named_resources", "NAMED_RESOURCES", default={}
+CUSTOM_NAMED_RESOURCES: Mapping[str, ResourceFactory] = import_attr(
+    os.environ.get("TORCHX_CUSTOM_NAMED_RESOURCES", "torchx.specs.fb.named_resources"),
+    "NAMED_RESOURCES",
+    default={},
 )
 
 
@@ -75,7 +79,7 @@ def _load_named_resources() -> Dict[str, Callable[[], Resource]]:
     for name, resource in {
         **GENERIC_NAMED_RESOURCES,
         **AWS_NAMED_RESOURCES,
-        **FB_NAMED_RESOURCES,
+        **CUSTOM_NAMED_RESOURCES,
         **resource_methods,
     }.items():
         materialized_resources[name] = resource

torchx/specs/test/named_resources_test.py

@@ -8,6 +8,7 @@
 # pyre-strict
 
 
+import os
 import unittest
 from unittest.mock import MagicMock, patch
 
@@ -47,3 +48,19 @@ def test_named_resources_library(self, mock_named_resources: MagicMock) -> None:
     def test_null_and_missing_named_resources(self) -> None:
         self.assertEqual(named_resources["NULL"], NULL_RESOURCE)
         self.assertEqual(named_resources["MISSING"], NULL_RESOURCE)
+
+    def test_custom_named_resources_env_var(self) -> None:
+        import sys
+
+        mock_module = type(sys)("test_module")
+        mock_module.NAMED_RESOURCES = {"test_resource": mock_resource}
+
+        with patch.dict(sys.modules, {"test_module": mock_module}):
+            with patch(
+                "torchx.specs.CUSTOM_NAMED_RESOURCES", mock_module.NAMED_RESOURCES
+            ):
+                import torchx.specs
+
+                factories = torchx.specs._load_named_resources()
+
+                self.assertIn("test_resource", factories)