feedback

Signed-off-by: Lawrence Lane <llane@nvidia.com>

Author: lbliii

docs/about/release-notes/index.md

@@ -116,7 +116,7 @@ New API for tracking and analyzing pipeline execution:
 
 ## Bug Fixes
 
-- Fixed fasttext predict call compatibility with numpy>2 
+- Fixed fasttext predict call compatibility with numpy>2
 - Fixed broken NeMo Framework documentation links
 - Fixed MegatronTokenizerWriter to download only necessary tokenizer files
 - Fixed ID generator blocking issues for large-scale processing
@@ -147,7 +147,6 @@ New API for tracking and analyzing pipeline execution:
 - **Memory Management**: New guidance for handling CPU/GPU memory constraints
 - **AWS Integration**: Updated tutorials with correct AWS credentials setup
 
-
 ---
 
 ## What's Next

docs/curate-video/process-data/dedup.md

@@ -56,7 +56,7 @@ workflow = SemanticDeduplicationWorkflow(
     n_clusters=1000,
     id_field="id",
     embedding_field="embedding",
-    embedding_dim=512,  # 512 for InternVideo2, varies for Cosmos-Embed1
+    embedding_dim=768,  # Embedding dimension (768 for Cosmos-Embed1, varies by model)
     input_filetype="parquet",
     eps=0.1,  # Similarity threshold: cosine_sim >= 1.0 - eps identifies duplicates
     ranking_strategy=RankingStrategy.metadata_based(

docs/get-started/image.md

@@ -120,8 +120,16 @@ Here's a simple example to get started with NeMo Curator's image curation pipeli
 Image loading and decoding happens in CPU memory before GPU processing. If you encounter out-of-memory errors during the `ImageReaderStage`, reduce:
 - `batch_size`: Number of images per batch (reduce to 32-50 for systems with limited RAM)
 - `num_threads`: Parallel decoding threads (reduce to 4 for systems with limited RAM)
+- `num_cpus`: Ray Client CPU allocation (reduce to 8-16 for systems with limited RAM)
 
 The example below uses conservative defaults suitable for most systems. For high-memory systems, you can increase these values for better performance.
+
+To configure Ray with limited CPU resources:
+```python
+from nemo_curator.core.client import RayClient
+ray_client = RayClient(num_cpus=8)  # Adjust based on available CPU cores
+ray_client.start()
+```
 :::
 
 ```python

docs/reference/infrastructure/container-environments.md

@@ -37,7 +37,7 @@ NeMo Curator provides official Docker containers with all dependencies pre-insta
 
 The primary container includes comprehensive support for all curation modalities:
 
-**Container registry:** `nvcr.io/nvidia/nemo-curator:26.02`
+**Container registry:** `nvcr.io/nvidia/nemo-curator:{{ container_version }}`
 
 **Supported modalities:**
 - ✅ Text curation (CPU/GPU)