[data][llm][doc] Add in resiliency section and refine doc code

.vale/styles/config/vocabularies/Data/accept.txt

@@ -22,6 +22,7 @@ Modin
 [Mm]ultiget(s)?
 ndarray(s)?
 NLP
+[Oo]mni
 [Oo]utqueue(s)?
 PDFs
 PIL

doc/source/data/doc_code/working-with-llms/basic_llm_example.py

@@ -4,6 +4,8 @@
 """
 
 # __basic_llm_example_start__
+import os
+import shutil
 import ray
 from ray.data.llm import vLLMEngineProcessorConfig, build_processor
 
@@ -125,6 +127,66 @@
 )
 # __s3_config_example_end__
 
+base_dir = "/tmp/llm_checkpoint_demo"
+input_path = os.path.join(base_dir, "input")
+output_path = os.path.join(base_dir, "output")
+checkpoint_path = os.path.join(base_dir, "checkpoint")
+
+# Reset directories
+for path in (input_path, output_path, checkpoint_path):
+    shutil.rmtree(path, ignore_errors=True)
+    os.makedirs(path)
+
+# __row_level_fault_tolerance_config_example_start__
+# Row-level fault tolerance configuration
+config = vLLMEngineProcessorConfig(
+    model_source="unsloth/Llama-3.1-8B-Instruct",
+    concurrency=1,
+    batch_size=64,
+    should_continue_on_error=True,
+)
+# __row_level_fault_tolerance_config_example_end__
+
+# __checkpoint_config_setup_example_start__
+from ray.data.checkpoint import CheckpointConfig
+
+ctx = ray.data.DataContext.get_current()
+ctx.checkpoint_config = CheckpointConfig(
+    id_column="id",
+    checkpoint_path=checkpoint_path,
+    delete_checkpoint_on_success=False,
+)
+# __checkpoint_config_setup_example_end__
+
+# __checkpoint_usage_example_start__
+processor_config = vLLMEngineProcessorConfig(
+    model_source="unsloth/Llama-3.1-8B-Instruct",
+    concurrency=1,
+    batch_size=16,
+)
+
+processor = build_processor(
+    processor_config,
+    preprocess=lambda row: dict(
+        id=row["id"], # Preserve the ID column for checkpointing
+        prompt=row["prompt"],
+        sampling_params=dict(
+            temperature=0.3,
+            max_tokens=10,
+        ),
+    ),
+    postprocess=lambda row: {
+        "id": row["id"], # Preserve the ID column for checkpointing
+        "answer": row.get("generated_text"),
+    },
+)
+
+ds = ray.data.read_parquet(input_path)
+ds = processor(ds)
+ds.write_parquet(output_path)
+# __checkpoint_usage_example_end__
+
+
 # __gpu_memory_config_example_start__
 # GPU memory management configuration
 # If you encounter CUDA out of memory errors, try these optimizations:
@@ -199,8 +261,8 @@ def create_embedding_processor():
     ),
     batch_size=8,
     concurrency=1,
-    apply_chat_template=False,
-    detokenize=False,
+    chat_template_stage=False,
+    detokenize_stage=False,
 )
 
 

doc/source/data/doc_code/working-with-llms/classification_example.py

@@ -29,8 +29,8 @@
     ),
     batch_size=8,
     concurrency=1,
-    apply_chat_template=False,
-    detokenize=False,
+    chat_template_stage=False,
+    detokenize_stage=False,
 )
 
 classification_processor = build_processor(

doc/source/data/doc_code/working-with-llms/omni_audio_example.py

@@ -53,9 +53,9 @@
         "enabled": True,
         "chat_template_content_format": "openai",
     },
-    chat_template_stage={"enabled": True},
-    tokenize_stage={"enabled": True},
-    detokenize_stage={"enabled": True},
+    chat_template_stage=True,
+    tokenize_stage=True,
+    detokenize_stage=True,
 )
 # __omni_audio_config_example_end__
 
@@ -112,6 +112,7 @@ def audio_postprocess(row: dict) -> dict:
 
 
 def load_audio_dataset():
+# __omni_audio_load_dataset_example_start__
     """
     Load audio dataset from MRSAudio Hugging Face dataset.
     """
@@ -151,6 +152,7 @@ def load_audio_dataset():
     except Exception as e:
         print(f"Error loading dataset: {e}")
         return None
+# __omni_audio_load_dataset_example_end__
 
 
 def create_omni_audio_config():
@@ -169,13 +171,13 @@ def create_omni_audio_config():
             "enabled": True,
             "chat_template_content_format": "openai",
         },
-        chat_template_stage={"enabled": True},
-        tokenize_stage={"enabled": True},
-        detokenize_stage={"enabled": True},
+        chat_template_stage=True,
+        tokenize_stage=True,
+        detokenize_stage=True,
     )
 
-
 def run_omni_audio_example():
+# __omni_audio_run_example_start__
     """Run the complete Omni audio example workflow."""
     config = create_omni_audio_config()
     audio_dataset = load_audio_dataset()
@@ -191,7 +193,7 @@ def run_omni_audio_example():
         print(f"Has multimodal support: {config.prepare_multimodal_stage.get('enabled', False)}")
         result = processor(audio_dataset).take_all()
         return config, processor, result
-    # __omni_audio_run_example_end__
+# __omni_audio_run_example_end__
     return None, None, None
 
 

doc/source/data/doc_code/working-with-llms/vlm_image_example.py

@@ -48,27 +48,17 @@
 fs = HfFileSystem()
 vision_dataset = ray.data.read_parquet(path, filesystem=fs)
 
-HF_TOKEN = "your-hf-token-here"  # Replace with actual token if needed
-
 # __vlm_config_example_start__
 vision_processor_config = vLLMEngineProcessorConfig(
     model_source="Qwen/Qwen2.5-VL-3B-Instruct",
     engine_kwargs=dict(
         tensor_parallel_size=1,
         pipeline_parallel_size=1,
         max_model_len=4096,
-        enable_chunked_prefill=True,
-        max_num_batched_tokens=2048,
         trust_remote_code=True,
         limit_mm_per_prompt={"image": 1},
     ),
-    runtime_env=dict(
-        env_vars=dict(
-            VLLM_USE_V1="1",
-        ),
-    ),
     batch_size=16,
-    accelerator_type="L4",
     concurrency=1,
     prepare_multimodal_stage={"enabled": True},
 )
@@ -146,6 +136,7 @@ def vision_postprocess(row: dict) -> dict:
 
 
 def load_vision_dataset():
+# __vlm_image_load_dataset_example_start__
     """
     Load vision dataset from Hugging Face.
 
@@ -171,6 +162,7 @@ def load_vision_dataset():
     except Exception as e:
         print(f"Error loading dataset: {e}")
         return None
+# __vlm_image_load_dataset_example_end__
 
 
 def create_vlm_config():
@@ -185,13 +177,13 @@ def create_vlm_config():
             limit_mm_per_prompt={"image": 1},
         ),
         batch_size=1,
-        accelerator_type="L4",
         concurrency=1,
         prepare_multimodal_stage={"enabled": True},
     )
 
 
 def run_vlm_example():
+# __vlm_run_example_start__
     """Run the complete VLM example workflow."""
     config = create_vlm_config()
     vision_dataset = load_vision_dataset()
@@ -207,7 +199,7 @@ def run_vlm_example():
         print(f"Has multimodal support: {config.prepare_multimodal_stage.get('enabled', False)}")
         result = processor(vision_dataset).take_all()
         return config, processor, result
-    # __vlm_run_example_end__
+# __vlm_run_example_end__
     return None, None, None
 
 

doc/source/data/doc_code/working-with-llms/vlm_video_example.py

@@ -58,9 +58,9 @@
             allowed_local_media_path="/tmp",
         ),
     },
-    chat_template_stage={"enabled": True},
-    tokenize_stage={"enabled": True},
-    detokenize_stage={"enabled": True},
+    chat_template_stage=True,
+    tokenize_stage=True,
+    detokenize_stage=True,
 )
 # __vlm_video_config_example_end__
 
@@ -123,6 +123,7 @@ def video_postprocess(row: dict) -> dict:
 
 
 def load_video_dataset():
+# __vlm_video_load_dataset_example_start__
     """
     Load video dataset from ShareGPTVideo Hugging Face dataset.
     """
@@ -166,7 +167,7 @@ def load_video_dataset():
     except Exception as e:
         print(f"Error loading dataset: {e}")
         return None
-
+# __vlm_video_load_dataset_example_end__
 
 def create_vlm_video_config():
     """Create VLM video configuration."""
@@ -188,13 +189,14 @@ def create_vlm_video_config():
                 allowed_local_media_path="/tmp",
             ),
         },
-        chat_template_stage={"enabled": True},
-        tokenize_stage={"enabled": True},
-        detokenize_stage={"enabled": True},
+        chat_template_stage=True,
+        tokenize_stage=True,
+        detokenize_stage=True,
     )
 
 
 def run_vlm_video_example():
+# __vlm_video_run_example_start__
     """Run the complete VLM video example workflow."""
     config = create_vlm_video_config()
     video_dataset = load_video_dataset()
@@ -210,7 +212,7 @@ def run_vlm_video_example():
         print(f"Has multimodal support: {config.prepare_multimodal_stage.get('enabled', False)}")
         result = processor(video_dataset).take_all()
         return config, processor, result
-    # __vlm_video_run_example_end__
+# __vlm_video_run_example_end__
     return None, None, None