add nvidia distribution

llama_stack/providers/registry/post_training.py

@@ -6,7 +6,7 @@
 
 from typing import List
 
-from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec
+from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
 
 
 def available_providers() -> List[ProviderSpec]:
@@ -22,15 +22,13 @@ def available_providers() -> List[ProviderSpec]:
                 Api.datasets,
             ],
         ),
-        InlineProviderSpec(
+        remote_provider_spec(
             api=Api.post_training,
-            provider_type="remote::nvidia",
-            pip_packages=["torch", "numpy"],
-            module="llama_stack.providers.remote.post_training.nvidia",
-            config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig",
-            api_dependencies=[
-                Api.datasetio,
-                Api.datasets,
-            ],
+            adapter=AdapterSpec(
+                adapter_type="nvidia",
+                pip_packages=["requests"],
+                module="llama_stack.providers.remote.post_training.nvidia",
+                config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig",
+            ),
         ),
     ]

llama_stack/providers/remote/post_training/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/remote/post_training/nvidia/__init__.py

@@ -13,7 +13,7 @@ from .config import NvidiaPostTrainingConfig
 # post_training api and the torchtune provider is still experimental and under heavy development
 
 
-async def get_provider_impl(
+async def get_adapter_impl(
     config: NvidiaPostTrainingConfig,
     deps: Dict[Api, ProviderSpec],
 ):

llama_stack/providers/remote/post_training/nvidia/config.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 import os
-from typing import Optional
+from typing import Any, Dict, Optional
 
 from pydantic import BaseModel, Field
 
@@ -58,3 +58,15 @@ class NvidiaPostTrainingConfig(BaseModel):
         default_factory=lambda: os.getenv("NVIDIA_OUTPUT_MODEL_DIR", "test-example-model@v1"),
         description="Directory to save the output model",
     )
+
+    @classmethod
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+        return {
+            "api_key": "${env.NVIDIA_API_KEY:}",
+            "user_id": "${env.NVIDIA_USER_ID:llama-stack-user}",
+            "dataset_namespace": "${env.NVIDIA_DATASET_NAMESPACE:default}",
+            "access_policies": "${env.NVIDIA_ACCESS_POLICIES:}",
+            "project_id": "${env.NVIDIA_PROJECT_ID:test-project}",
+            "customizer_url": "${env.NVIDIA_CUSTOMIZER_URL:}",
+            "output_model_dir": "${env.NVIDIA_OUTPUT_MODEL_DIR:test-example-model@v1}",
+        }