chore(misc): make tests and starter faster (#3042)

A bunch of miscellaneous cleanup focusing on tests, but ended up
speeding up starter distro substantially.

- Pulled llama stack client init for tests into `pytest_sessionstart` so
it does not clobber output
- Profiling of that told me where we were doing lots of heavy imports
for starter, so lazied them
- starter now starts 20seconds+ faster on my Mac
- A few other smallish refactors for `compat_client`

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

@@ -23,12 +23,8 @@ from llama_stack.apis.post_training import (
 from llama_stack.providers.inline.post_training.torchtune.config import (
     TorchtunePostTrainingConfig,
 )
-from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
-    LoraFinetuningSingleDevice,
-)
 from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
 from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
-from llama_stack.schema_utils import webmethod
 
 
 class TrainingArtifactType(Enum):
@@ -84,6 +80,10 @@ class TorchtunePostTrainingImpl:
         if isinstance(algorithm_config, LoraFinetuningConfig):
 
             async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
+                from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
+                    LoraFinetuningSingleDevice,
+                )
+
                 on_log_message_cb("Starting Lora finetuning")
 
                 recipe = LoraFinetuningSingleDevice(
@@ -144,7 +144,6 @@ class TorchtunePostTrainingImpl:
         data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value)
         return data[0] if data else None
 
-    @webmethod(route="/post-training/job/status")
     async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
         job = self._scheduler.get_job(job_uuid)
 
@@ -171,11 +170,9 @@ class TorchtunePostTrainingImpl:
             resources_allocated=self._get_resources_allocated(job),
         )
 
-    @webmethod(route="/post-training/job/cancel")
     async def cancel_training_job(self, job_uuid: str) -> None:
         self._scheduler.cancel(job_uuid)
 
-    @webmethod(route="/post-training/job/artifacts")
     async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
         job = self._scheduler.get_job(job_uuid)
         return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job))