Update Memory Management Guide

docs/curate-audio/tutorials/beginner.md

@@ -40,19 +40,17 @@ The complete working code for this tutorial is located at:
 
 ```
 <nemo_curator_repository>/tutorials/audio/fleurs/
-├── run.py              # Main tutorial script
 ├── README.md           # Tutorial documentation
-└── requirements.txt    # Python dependencies
+├── pipeline.py         # Main tutorial script
+├── pipeline.yaml       # Configuration file for run.py
+└── run.py              # Same as pipeline.py, but defines pipeline using YAML file instead
 ```
 
 **Accessing the code:**
 ```bash
 # Clone NeMo Curator repository
 git clone https://github.com/NVIDIA/NeMo-Curator.git
 cd NeMo-Curator/tutorials/audio/fleurs/
-
-# Install dependencies
-pip install -r requirements.txt
 ```
 
 ## Prerequisites
@@ -222,16 +220,15 @@ To run the working tutorial:
 ```bash
 cd tutorials/audio/fleurs/
 
-# Basic run with default settings
-python run.py --raw_data_dir /data/fleurs_output
-
-# Customize parameters
-python run.py \
-    --raw_data_dir /data/fleurs_output \
-    --lang ko_kr \
-    --split train \
-    --model_name nvidia/stt_ko_fastconformer_hybrid_large_pc \
-    --wer_threshold 50.0
+python tutorials/audio/fleurs/pipeline.py \
+  --raw_data_dir ./example_audio/fleurs \
+  --model_name nvidia/stt_hy_fastconformer_hybrid_large_pc \
+  --lang hy_am \
+  --split dev \
+  --wer_threshold 75 \
+  --gpus 1 \
+  --clean \
+  --verbose
 ```
 
 **Command-line options:**

docs/get-started/video.md

@@ -266,10 +266,10 @@ Organize input videos and output locations before running the pipeline.
 
 ## Run the Splitting Pipeline Example
 
-Use the following example script to read videos, split into clips, and write outputs. This runs a Ray pipeline with `XennaExecutor` under the hood.
+Use the example script from https://github.com/NVIDIA-NeMo/Curator/tree/main/tutorials/video/getting-started to read videos, split into clips, and write outputs. This runs a Ray pipeline with `XennaExecutor` under the hood.
 
 ```bash
-python -m nemo_curator.examples.video.video_split_clip_example \
+python tutorials/video/getting-started/video_split_clip_example.py \
   --video-dir "$DATA_DIR" \
   --model-dir "$MODEL_DIR" \
   --output-clip-path "$OUT_DIR" \

docs/reference/infrastructure/execution-backends.md

@@ -108,9 +108,7 @@ results = pipeline.run(executor)
 
 For more details, refer to the official [NVIDIA Cosmos-Xenna project](https://github.com/nvidia-cosmos/cosmos-xenna/tree/main).
 
-### `RayActorPoolExecutor`
-
-Executor using Ray Actor pools for custom distributed processing patterns such as deduplication.
+### `RayDataExecutor`
 
 `RayDataExecutor` uses Ray Data, a scalable data processing library built on Ray Core. Ray Data provides a familiar DataFrame-like API for distributed data transformations. This executor is experimental and best suited for large-scale batch processing tasks that benefit from Ray Data's optimized data loading and transformation pipelines.
 
@@ -120,21 +118,23 @@ Executor using Ray Actor pools for custom distributed processing patterns such a
 - **Experimental status**: API and performance characteristics may change
 
 ```python
-from nemo_curator.backends.experimental.ray_actor_pool import RayActorPoolExecutor
+from nemo_curator.backends.experimental.ray_data import RayDataExecutor
 
-executor = RayActorPoolExecutor()
+executor = RayDataExecutor()
 results = pipeline.run(executor)
 ```
 
 :::{note}`RayDataExecutor` currently has limited configuration options. For more control over execution, consider using `XennaExecutor` or `RayActorPoolExecutor`.
 :::
 
-### `RayActorPoolExecutor` (experimental)
+### `RayActorPoolExecutor`
+
+Executor using Ray Actor pools for custom distributed processing patterns such as deduplication.
 
 ```python
-from nemo_curator.backends.experimental.ray_data import RayDataExecutor
+from nemo_curator.backends.experimental.ray_actor_pool import RayActorPoolExecutor
 
-executor = RayDataExecutor()
+executor = RayActorPoolExecutor()
 results = pipeline.run(executor)
 ```