Update diagram

NVIDIA · Feb 13, 2025 · 68a3a52 · 68a3a52
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diff --git a/examples/ML+DL-Examples/Spark-DL/dl_inference/README.md b/examples/ML+DL-Examples/Spark-DL/dl_inference/README.md
@@ -133,9 +133,8 @@ The notebooks use [PyTriton](https://github.com/triton-inference-server/pytriton
 The diagram above shows how Spark distributes inference tasks to run on the Triton Inference Server, with PyTriton handling request/response communication with the server. 
 
 The process looks like this:
-- Distribute a PyTriton task across the Spark cluster, instructing each worker to launch a Triton server process.
-    - Use stage-level scheduling to ensure there is a 1:1 mapping between worker nodes and servers.
-- Define a Triton inference function, which contains a client that binds to the local server on a given worker and sends inference requests.
+- Prior to inference, launch a Triton server process on each node.
+- Define a Triton predict function, which creates a client that binds to the local server and sends/receives inference requests.
 - Wrap the Triton inference function in a predict_batch_udf to launch parallel inference requests using Spark.
 - Finally, distribute a shutdown signal to terminate the Triton server processes on each worker.
 

diff --git a/examples/ML+DL-Examples/Spark-DL/dl_inference/images/spark-pytriton.png b/examples/ML+DL-Examples/Spark-DL/dl_inference/images/spark-pytriton.png