chore(lint): update Ruff ignores for project conventions and maintainability (#1184)

- Added new ignores from flake8-bugbear (`B007`, `B008`)
- Ignored `C901` (high function complexity) for now, pending review
- Maintained PyTorch conventions (`N812`, `N817`)
- Allowed `E731` (lambda assignments) for flexibility
- Consolidated existing ignores (`E402`, `E501`, `F405`, `C408`, `N812`)
- Documented rationale for each ignored rule

This keeps our linting aligned with project needs while tracking
potential fixes.

Signed-off-by: Sébastien Han <seb@redhat.com>

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/cli/stack/_build.py

@@ -141,7 +141,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
                 completer=WordCompleter(available_providers),
                 complete_while_typing=True,
                 validator=Validator.from_callable(
-                    lambda x: x in available_providers,
+                    lambda x: x in available_providers,  # noqa: B023 - see https://github.com/astral-sh/ruff/issues/7847
                     error_message="Invalid provider, use <TAB> to see options",
                 ),
             )

llama_stack/cli/tests/test_stack_config.py

@@ -112,7 +112,7 @@ def test_parse_and_maybe_upgrade_config_old_format(old_config):
 
     inference_providers = result.providers["inference"]
     assert len(inference_providers) == 2
-    assert set(x.provider_id for x in inference_providers) == {
+    assert {x.provider_id for x in inference_providers} == {
         "remote::ollama-00",
         "meta-reference-01",
     }

llama_stack/distribution/distribution.py

@@ -13,7 +13,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec
 
 
 def stack_apis() -> List[Api]:
-    return [v for v in Api]
+    return list(Api)
 
 
 class AutoRoutedApiInfo(BaseModel):
@@ -55,7 +55,7 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
 
 
 def providable_apis() -> List[Api]:
-    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_apis())
+    routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
     return [api for api in Api if api not in routing_table_apis and api != Api.inspect]
 
 

llama_stack/distribution/resolver.py

@@ -115,8 +115,8 @@ async def resolve_impls(
     - flatmaps, sorts and resolves the providers in dependency order
     - for each API, produces either a (local, passthrough or router) implementation
     """
-    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_apis())
+    routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
-    router_apis = set(x.router_api for x in builtin_automatically_routed_apis())
+    router_apis = {x.router_api for x in builtin_automatically_routed_apis()}
 
     providers_with_specs = {}
 

llama_stack/distribution/ui/page/playground/rag.py

@@ -134,7 +134,7 @@ def rag_chat_page():
             dict(
                 name="builtin::rag/knowledge_search",
                 args={
-                    "vector_db_ids": [vector_db_id for vector_db_id in selected_vector_dbs],
+                    "vector_db_ids": list(selected_vector_dbs),
                 },
             )
         ],

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -797,10 +797,10 @@ class ChatAgent(ShieldRunnerMixin):
         self, toolgroups_for_turn: Optional[List[AgentToolGroup]] = None
     ) -> Tuple[List[ToolDefinition], Dict[str, str]]:
         # Determine which tools to include
-        agent_config_toolgroups = set(
+        agent_config_toolgroups = {
-            (toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup)
+            toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup
             for toolgroup in self.agent_config.toolgroups
-        )
+        }
         toolgroups_for_turn_set = (
             agent_config_toolgroups
             if toolgroups_for_turn is None

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -86,7 +86,6 @@ class MetaReferenceEvalImpl(
     ) -> Job:
         task_def = self.benchmarks[benchmark_id]
         dataset_id = task_def.dataset_id
-        candidate = task_config.eval_candidate
         scoring_functions = task_def.scoring_functions
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
         validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -208,7 +208,6 @@ class MetaReferenceInferenceImpl(
             logprobs = []
             stop_reason = None
 
-            tokenizer = self.generator.formatter.tokenizer
             for token_result in self.generator.completion(request):
                 tokens.append(token_result.token)
                 if token_result.text == "<|eot_id|>":

llama_stack/providers/inline/inference/meta_reference/parallel_utils.py

@@ -207,7 +207,7 @@ def maybe_parse_message(maybe_json: Optional[str]) -> Optional[ProcessingMessage
         return parse_message(maybe_json)
     except json.JSONDecodeError:
         return None
-    except ValueError as e:
+    except ValueError:
         return None
 
 
@@ -352,7 +352,7 @@ class ModelParallelProcessGroup:
                 if isinstance(obj, TaskResponse):
                     yield obj.result
 
-        except GeneratorExit as e:
+        except GeneratorExit:
             self.request_socket.send(encode_msg(CancelSentinel()))
             while True:
                 obj_json = self.request_socket.send()

llama_stack/providers/inline/inference/meta_reference/quantization/fp8_txest_disabled.py

@@ -7,6 +7,9 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
+# The file gets a special treatment for now?
+# ruff: noqa: N803
+
 import unittest
 
 import torch

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -264,7 +264,7 @@ class LoraFinetuningSingleDevice:
             )
 
         self.adapter_params = get_adapter_params(model)
-        self._is_dora = any(["magnitude" in k for k in self.adapter_params.keys()])
+        self._is_dora = any("magnitude" in k for k in self.adapter_params.keys())
 
         set_trainable_params(model, self.adapter_params)
 

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -133,7 +133,7 @@ class BraintrustScoringImpl(
     async def shutdown(self) -> None: ...
 
     async def list_scoring_functions(self) -> List[ScoringFn]:
-        scoring_fn_defs_list = [x for x in self.supported_fn_defs_registry.values()]
+        scoring_fn_defs_list = list(self.supported_fn_defs_registry.values())
         for f in scoring_fn_defs_list:
             assert f.identifier.startswith("braintrust"), (
                 "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -198,7 +198,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         tool_config: Optional[ToolConfig] = None,
     ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
         if tool_prompt_format:
-            warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring")
+            warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring", stacklevel=2)
 
         await check_health(self._config)  # this raises errors
 

llama_stack/providers/remote/inference/nvidia/openai_utils.py

@@ -106,7 +106,7 @@ async def convert_chat_completion_request(
             payload.update(temperature=strategy.temperature)
         elif isinstance(strategy, TopKSamplingStrategy):
             if strategy.top_k != -1 and strategy.top_k < 1:
-                warnings.warn("top_k must be -1 or >= 1")
+                warnings.warn("top_k must be -1 or >= 1", stacklevel=2)
             nvext.update(top_k=strategy.top_k)
         elif isinstance(strategy, GreedySamplingStrategy):
             nvext.update(top_k=-1)
@@ -168,7 +168,7 @@ def convert_completion_request(
             payload.update(top_p=request.sampling_params.top_p)
         elif request.sampling_params.strategy == "top_k":
             if request.sampling_params.top_k != -1 and request.sampling_params.top_k < 1:
-                warnings.warn("top_k must be -1 or >= 1")
+                warnings.warn("top_k must be -1 or >= 1", stacklevel=2)
             nvext.update(top_k=request.sampling_params.top_k)
         elif request.sampling_params.strategy == "greedy":
             nvext.update(top_k=-1)

llama_stack/providers/tests/eval/test_eval.py

@@ -39,12 +39,11 @@ class Testeval:
 
     @pytest.mark.asyncio
     async def test_eval_evaluate_rows(self, eval_stack, inference_model, judge_model):
-        eval_impl, benchmarks_impl, datasetio_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasetio_impl, datasets_impl = (
             eval_stack[Api.eval],
             eval_stack[Api.benchmarks],
             eval_stack[Api.datasetio],
             eval_stack[Api.datasets],
-            eval_stack[Api.models],
         )
 
         await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@@ -92,11 +91,10 @@ class Testeval:
 
     @pytest.mark.asyncio
     async def test_eval_run_eval(self, eval_stack, inference_model, judge_model):
-        eval_impl, benchmarks_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasets_impl = (
             eval_stack[Api.eval],
             eval_stack[Api.benchmarks],
             eval_stack[Api.datasets],
-            eval_stack[Api.models],
         )
 
         await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@@ -131,11 +129,10 @@ class Testeval:
 
     @pytest.mark.asyncio
     async def test_eval_run_benchmark_eval(self, eval_stack, inference_model):
-        eval_impl, benchmarks_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasets_impl = (
             eval_stack[Api.eval],
             eval_stack[Api.benchmarks],
             eval_stack[Api.datasets],
-            eval_stack[Api.models],
         )
 
         response = await datasets_impl.list_datasets()

llama_stack/providers/tests/report.py

@@ -18,54 +18,48 @@ from llama_stack.models.llama.sku_list import all_registered_models
 INFERENCE_APIS = ["chat_completion"]
 FUNCTIONALITIES = ["streaming", "structured_output", "tool_calling"]
 SUPPORTED_MODELS = {
-    "ollama": set(
+    "ollama": {
-        [
+        CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama3_3_70b_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama_guard_3_8b.value,
+        CoreModelId.llama_guard_3_1b.value,
-            CoreModelId.llama_guard_3_1b.value,
+    },
-        ]
+    "fireworks": {
-    ),
+        CoreModelId.llama3_1_8b_instruct.value,
-    "fireworks": set(
+        CoreModelId.llama3_1_70b_instruct.value,
-        [
+        CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama_guard_3_11b_vision.value,
-            CoreModelId.llama3_3_70b_instruct.value,
+    },
-            CoreModelId.llama_guard_3_8b.value,
+    "together": {
-            CoreModelId.llama_guard_3_11b_vision.value,
+        CoreModelId.llama3_1_8b_instruct.value,
-        ]
+        CoreModelId.llama3_1_70b_instruct.value,
-    ),
+        CoreModelId.llama3_1_405b_instruct.value,
-    "together": set(
+        CoreModelId.llama3_2_3b_instruct.value,
-        [
+        CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama_guard_3_11b_vision.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
+    },
-            CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama_guard_3_11b_vision.value,
-        ]
-    ),
 }
 
 

llama_stack/providers/tests/scoring/test_scoring.py

@@ -45,13 +45,11 @@ class TestScoring:
             scoring_functions_impl,
             datasetio_impl,
             datasets_impl,
-            models_impl,
         ) = (
             scoring_stack[Api.scoring],
             scoring_stack[Api.scoring_functions],
             scoring_stack[Api.datasetio],
             scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
         )
         scoring_fns_list = await scoring_functions_impl.list_scoring_functions()
         provider_id = scoring_fns_list[0].provider_id
@@ -102,13 +100,11 @@ class TestScoring:
             scoring_functions_impl,
             datasetio_impl,
             datasets_impl,
-            models_impl,
         ) = (
             scoring_stack[Api.scoring],
             scoring_stack[Api.scoring_functions],
             scoring_stack[Api.datasetio],
             scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
         )
         await register_dataset(datasets_impl, for_rag=True)
         response = await datasets_impl.list_datasets()
@@ -163,13 +159,11 @@ class TestScoring:
             scoring_functions_impl,
             datasetio_impl,
             datasets_impl,
-            models_impl,
         ) = (
             scoring_stack[Api.scoring],
             scoring_stack[Api.scoring_functions],
             scoring_stack[Api.datasetio],
             scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
         )
         await register_dataset(datasets_impl, for_rag=True)
         rows = await datasetio_impl.get_rows_paginated(

llama_stack/providers/utils/inference/openai_compat.py

@@ -605,7 +605,7 @@ def convert_tool_call(
             tool_name=tool_call.function.name,
             arguments=json.loads(tool_call.function.arguments),
         )
-    except Exception as e:
+    except Exception:
         return UnparseableToolCall(
             call_id=tool_call.id or "",
             tool_name=tool_call.function.name or "",
@@ -876,7 +876,9 @@ async def convert_openai_chat_completion_stream(
             # it is possible to have parallel tool calls in stream, but
             # ChatCompletionResponseEvent only supports one per stream
             if len(choice.delta.tool_calls) > 1:
-                warnings.warn("multiple tool calls found in a single delta, using the first, ignoring the rest")
+                warnings.warn(
+                    "multiple tool calls found in a single delta, using the first, ignoring the rest", stacklevel=2
+                )
 
             if not enable_incremental_tool_calls:
                 yield ChatCompletionResponseStreamChunk(

llama_stack/providers/utils/kvstore/redis/redis.py

@@ -36,7 +36,7 @@ class RedisKVStoreImpl(KVStore):
         value = await self.redis.get(key)
         if value is None:
             return None
-        ttl = await self.redis.ttl(key)
+        await self.redis.ttl(key)
         return value
 
     async def delete(self, key: str) -> None:

llama_stack/providers/utils/scoring/aggregation_utils.py

@@ -32,7 +32,7 @@ def aggregate_categorical_count(
     scoring_results: List[ScoringResultRow],
 ) -> Dict[str, Any]:
     scores = [str(r["score"]) for r in scoring_results]
-    unique_scores = sorted(list(set(scores)))
+    unique_scores = sorted(set(scores))
     return {"categorical_count": {s: scores.count(s) for s in unique_scores}}
 
 

llama_stack/providers/utils/scoring/base_scoring_fn.py

@@ -66,7 +66,7 @@ class RegisteredBaseScoringFn(BaseScoringFn):
         return self.__class__.__name__
 
     def get_supported_scoring_fn_defs(self) -> List[ScoringFn]:
-        return [x for x in self.supported_fn_defs_registry.values()]
+        return list(self.supported_fn_defs_registry.values())
 
     def register_scoring_fn_def(self, scoring_fn: ScoringFn) -> None:
         if scoring_fn.identifier in self.supported_fn_defs_registry:

llama_stack/scripts/distro_codegen.py

@@ -99,7 +99,7 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[
             template = template_func()
             normal_deps, special_deps = get_provider_dependencies(template.providers)
             # Combine all dependencies in order: normal deps, special deps, server deps
-            all_deps = sorted(list(set(normal_deps + SERVER_DEPENDENCIES))) + sorted(list(set(special_deps)))
+            all_deps = sorted(set(normal_deps + SERVER_DEPENDENCIES)) + sorted(set(special_deps))
 
             return template.name, all_deps
     except Exception:

pyproject.toml

@@ -123,39 +123,16 @@ select = [
     "I",  # isort
 ]
 ignore = [
-    "E203",
+    # The following ignores are desired by the project maintainers.
-    "E305",
+    "E402", # Module level import not at top of file
-    "E402",
+    "E501", # Line too long
-    "E501", # line too long
+    "F405", # Maybe undefined or defined from star import
-    "E721",
+    "C408", # Ignored because we like the dict keyword argument syntax
-    "E741",
+    "N812", # Ignored because import torch.nn.functional as F is PyTorch convention
-    "F405",
+
-    "F841",
-    "C408", # ignored because we like the dict keyword argument syntax
-    "E302",
-    "W291",
-    "E303",
-    "N812", # ignored because import torch.nn.functional as F is PyTorch convention
-    "N817", # ignored because importing using acronyms is convention (DistributedDataParallel as DDP)
-    "E731", # allow usage of assigning lambda expressions
     # These are the additional ones we started ignoring after moving to ruff. We should look into each one of them later.
-    "C901",
+    "C901", # Complexity of the function is too high
-    "C405",
-    "C414",
-    "N803",
-    "N999",
-    "C403",
-    "C416",
-    "B028",
-    "C419",
-    "C401",
-    "B023",
-    # shebang has extra meaning in fbcode lints, so I think it's not worth trying
-    # to line this up with executable bit
-    "EXE001",
-    "N802",   # random naming hints don't need
     # these ignores are from flake8-bugbear; please fix!
-    "B007",
     "B008",
 ]
 

tests/client-sdk/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/agents/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/conftest.py

@@ -117,7 +117,7 @@ def client_with_models(llama_stack_client, text_model_id, vision_model_id, embed
     assert len(providers) > 0, "No inference providers found"
     inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]
 
-    model_ids = set(m.identifier for m in client.models.list())
+    model_ids = {m.identifier for m in client.models.list()}
     model_ids.update(m.provider_resource_id for m in client.models.list())
 
     if text_model_id and text_model_id not in model_ids:

tests/client-sdk/inference/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/inference/test_embedding.py

@@ -176,7 +176,7 @@ def test_embedding_truncation_error(
 ):
     if inference_provider_type not in SUPPORTED_PROVIDERS:
         pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
-    with pytest.raises(BadRequestError) as excinfo:
+    with pytest.raises(BadRequestError):
         llama_stack_client.inference.embeddings(
             model_id=embedding_model_id, contents=[DUMMY_LONG_TEXT], text_truncation=text_truncation
         )
@@ -243,7 +243,7 @@ def test_embedding_text_truncation_error(
 ):
     if inference_provider_type not in SUPPORTED_PROVIDERS:
         pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
-    with pytest.raises(BadRequestError) as excinfo:
+    with pytest.raises(BadRequestError):
         llama_stack_client.inference.embeddings(
             model_id=embedding_model_id, contents=[DUMMY_STRING], text_truncation=text_truncation
         )

tests/client-sdk/inference/test_text_inference.py

@@ -139,7 +139,7 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id,
             "top_k": 1,
         },
     )
-    streamed_content = [chunk for chunk in response]
+    streamed_content = list(response)
     for chunk in streamed_content:
         if chunk.delta:  # if there's a token, we expect logprobs
             assert chunk.logprobs, "Logprobs should not be empty"
@@ -405,7 +405,7 @@ def test_text_chat_completion_tool_calling_tools_not_in_request(
                 assert delta.tool_call.tool_name == "get_object_namespace_list"
             if delta.type == "tool_call" and delta.parse_status == "failed":
                 # expect raw message that failed to parse in tool_call
-                assert type(delta.tool_call) == str
+                assert isinstance(delta.tool_call, str)
                 assert len(delta.tool_call) > 0
     else:
         for tc in response.completion_message.tool_calls:

tests/client-sdk/report.py

@@ -42,29 +42,27 @@ def featured_models():
 
 
 SUPPORTED_MODELS = {
-    "ollama": set(
+    "ollama": {
-        [
+        CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama3_3_70b_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama_guard_3_8b.value,
+        CoreModelId.llama_guard_3_1b.value,
-            CoreModelId.llama_guard_3_1b.value,
+    },
-        ]
+    "tgi": {model.core_model_id.value for model in all_registered_models() if model.huggingface_repo},
-    ),
+    "vllm": {model.core_model_id.value for model in all_registered_models() if model.huggingface_repo},
-    "tgi": set([model.core_model_id.value for model in all_registered_models() if model.huggingface_repo]),
-    "vllm": set([model.core_model_id.value for model in all_registered_models() if model.huggingface_repo]),
 }
 
 

tests/client-sdk/safety/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/safety/test_safety.py

@@ -42,7 +42,7 @@ def code_scanner_shield_id(available_shields):
 
 @pytest.fixture(scope="session")
 def model_providers(llama_stack_client):
-    return set([x.provider_id for x in llama_stack_client.providers.list() if x.api == "inference"])
+    return {x.provider_id for x in llama_stack_client.providers.list() if x.api == "inference"}
 
 
 def test_unsafe_examples(llama_stack_client, llama_guard_text_shield_id):

tests/client-sdk/vector_io/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999