[2/n][torchtune integration] implement job management and return training artifacts (#593)

### Context 
In this PR, we 
- Implement the post training job management and get training artifacts
apis
  - get_training_jobs
  - get_training_job_status
  - get_training_job_artifacts
- get_training_job_logstream is deleted since the trace can be directly
accessed by UI with Jaeger
https://llama-stack.readthedocs.io/en/latest/building_applications/telemetry.html#jaeger-to-visualize-traces
- Refactor the post training and training types definition to make them
more intuitive.
- Rewrite the checkpointer to make it compatible with llama-stack file
system and can be recognized during inference


### Test
Unit test
`pytest llama_stack/providers/tests/post_training/test_post_training.py
-m "torchtune_post_training_huggingface_datasetio" -v -s --tb=short
--disable-warnings`

<img width="1506" alt="Screenshot 2024-12-10 at 4 06 17 PM"
src="https://github.com/user-attachments/assets/16225029-bdb7-48c4-9d13-e580cc769c0a">


e2e test with client side call

<img width="888" alt="Screenshot 2024-12-10 at 4 09 44 PM"
src="https://github.com/user-attachments/assets/de375e4c-ef67-4dcc-a045-4037d9489191">

llama_stack/apis/common/job_types.py

@@ -18,3 +18,5 @@ class Job(BaseModel):
 class JobStatus(Enum):
     completed = "completed"
     in_progress = "in_progress"
+    failed = "failed"
+    scheduled = "scheduled"

llama_stack/apis/common/training_types.py

@@ -4,13 +4,26 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_models.llama3.api.datatypes import URL
+from datetime import datetime
+from typing import Optional
+
 from llama_models.schema_utils import json_schema_type
 from pydantic import BaseModel
 
 
+@json_schema_type
+class PostTrainingMetric(BaseModel):
+    epoch: int
+    train_loss: float
+    validation_loss: float
+    perplexity: float
+
+
 @json_schema_type(schema={"description": "Checkpoint created during training runs"})
 class Checkpoint(BaseModel):
-    iters: int
-    path: URL
+    identifier: str
+    created_at: datetime
     epoch: int
+    post_training_job_id: str
+    path: str
+    training_metrics: Optional[PostTrainingMetric] = None