remove nvdecs in docs

Signed-off-by: Ao Tang <aot@nvidia.com>

Author: suiyoubi

.github/copilot-instructions.md

@@ -361,8 +361,6 @@ class Resources:
     cpus: float = 1.0 # Number of CPU cores
     gpu_memory_gb: float = 0.0 # Number of GPU memory in GB (Only for single GPU)
     gpus: float = 0.0 # Number of GPUs (Only for multi-GPU)
-    nvdecs: int = 0 # Number of NVDEC decoders
-    nvencs: int = 0 # Number of NVENC encoders
     entire_gpu: bool = False # Whether to use the entire GPU
 ```
 

api-design.md

@@ -132,8 +132,6 @@ class Resources:
     cpus: float = 1.0 # Number of CPU cores
     gpu_memory_gb: float = 0.0 # Number of GPU memory in GB (Only for single GPU)
     gpus: float = 0.0 # Number of GPUs (Only for multi-GPU)
-    nvdecs: int = 0 # Number of NVDEC decoders
-    nvencs: int = 0 # Number of NVENC encoders
     entire_gpu: bool = False # Whether to use the entire GPU
 ```
 

docs/about/concepts/video/abstractions.md

@@ -48,7 +48,7 @@ A stage represents a single step in your data curation workflow. Video stages ar
 Each processing stage:
 
 1. Inherits from `ProcessingStage`
-2. Declares a stable `name` and `resources: Resources` (CPU cores, GPU memory, optional NVDEC/NVENC, or more than one GPU)
+2. Declares a stable `name` and `resources: Resources` (CPU cores, GPU memory, entire GPU flag, or multiple GPUs)
 3. Defines `inputs()`/`outputs()` to document required attributes and produced attributes on tasks
 4. Implements `setup(worker_metadata)` for model initialization and `process(task)` to transform tasks
 
@@ -75,9 +75,8 @@ Refer to the stage base and resources definitions in Curator for full details.
 `Resources` support both fractional and whole‑GPU semantics:
 
 - `gpu_memory_gb`: Request a fraction of a single GPU by memory; Curator rounds to a fractional GPU share and enforces that `gpu_memory_gb` stays within one device.
-- `entire_gpu`: Request an entire GPU regardless of memory (also implies access to NVDEC/NVENC on that device).
+- `entire_gpu`: Request an entire GPU regardless of memory (also implies access to hardware decoders and encoders on that device).
 - `gpus`: Request more than one GPU for a stage that is multi‑GPU aware.
-- `nvdecs` / `nvencs`: Request hardware decode/encode units when needed.
 
 Choose one of `gpu_memory_gb` (single‑GPU fractional) or `gpus` (multi‑GPU). Combining both is not allowed.
 

docs/curate-video/index.md

@@ -33,7 +33,7 @@ Understand how components work together so you can plan, scale, and troubleshoot
 ```
 
 ```{note}
-Video pipelines use the `XennaExecutor` backend by default, which provides optimized support for GPU-accelerated video processing including hardware decoders (`nvdecs`) and encoders (`nvencs`). You do not need to import or configure the executor unless you want to use an alternative backend. For more information about customizing backends, refer to [Pipeline Execution Backends](reference-execution-backends).
+Video pipelines use the `XennaExecutor` backend by default, which provides optimized support for GPU-accelerated video processing including hardware decoders and encoders. You do not need to import or configure the executor unless you want to use an alternative backend. For more information about customizing backends, refer to [Pipeline Execution Backends](reference-execution-backends).
 ```
 
 ---

docs/curate-video/tutorials/pipeline-customization/add-cust-env.md

@@ -70,7 +70,3 @@ docker build -t my-ray-curator:latest .
 ## Next Steps
 
 Now that you have created a custom environment, you can [create custom code](video-tutorials-pipeline-cust-add-code) for that environment.
-
-```{note}
-Ray Data backends do not support `nvdecs`/`nvencs` resource keys. Xenna does. If you plan to use `nvdecs`/`nvencs`, prefer the default Xenna executor.
-```

docs/curate-video/tutorials/pipeline-customization/add-cust-model.md

@@ -131,7 +131,7 @@ The setup method initializes the underlying `MyCore` class that performs the mod
 
 The `model_id_names` property returns a list of weight IDs. These typically correspond to model repository names but do not have to.
 
-If your stage requires a specific environment, manage that in the stage’s `resources` (for example, `gpu_memory_gb`, `nvdecs`, `nvencs`) and container image, rather than on the model. GPU allocation is managed at the stage level using `Resources`, not on the model.
+If your stage requires a specific environment, manage that in the stage's `resources` (for example, `gpu_memory_gb`, `entire_gpu`, or `gpus`) and container image, rather than on the model. GPU allocation is managed at the stage level using `Resources`, not on the model.
 
 ### Manage model weights
 

docs/curate-video/tutorials/pipeline-customization/add-cust-stage.md

@@ -61,7 +61,7 @@ class MyCustomStage(ProcessingStage[VideoTask, VideoTask]):
 from nemo_curator.stages.resources import Resources
 
 stage = MyCustomStage().with_(
-    resources=Resources(cpus=4.0, gpu_memory_gb=16.0, nvdecs=1, nvencs=1)
+    resources=Resources(cpus=4.0, gpu_memory_gb=16.0)
 )
 ```
 
@@ -160,6 +160,3 @@ For end-to-end usage, review and adapt the example:
 
 If you need a container image, extend your base image using a Dockerfile and include your code and dependencies. Then build and run with your preferred container tooling.
 
-```{note}
-Ray Data backends do not support `nvdecs`/`nvencs` resource keys. Xenna does. If you plan to use `nvdecs`/`nvencs`, prefer the default Xenna executor.
-```