524 doc remove usage of buffer stage from examples.rst (nv-morpheus#528)

- Removed `buffer` stage usage
- Updated graph images

Authors:
  - Bhargav Suryadevara (https://github.com/bsuryadevara)
  - https://github.com/bsuryadev

Approvers:
  - David Gardner (https://github.com/dagardner-nv)

URL: nv-morpheus#528

docs/source/basics/examples.rst

@@ -27,7 +27,7 @@ This example will copy the values from Kafka into ``out.jsonlines``.
 .. code-block:: bash
 
    morpheus run pipeline-nlp --viz_file=basic_usage_img/simple_identity.png  \
-      from-kafka --input_topic test_pcap \
+      from-kafka --bootstrap_servers localhost:9092 --input_topic test_pcap \
       deserialize \
       serialize \
       to-file --filename out.jsonlines
@@ -63,16 +63,15 @@ This example will report the throughput on the command line.
       monitor --description "Lines Throughput" --smoothing 0.1 --unit "lines" \
       serialize \
       to-file --filename out.jsonlines
-   Configuring Pipeline via CLI
-   Starting pipeline via CLI... Ctrl+C to Quit
-   Pipeline visualization saved to basic_usage_img/monitor_throughput.png
-   Lines Throughput: 88064lines [00:11, 7529.37lines/s]
+  Configuring Pipeline via CLI
+  Starting pipeline via CLI... Ctrl+C to Quit
+  Lines Throughput[Complete]: 93085 lines [00:04, 19261.06 lines/s]
+  Pipeline visualization saved to basic_usage_img/monitor_throughput.png
 
 Multi-Monitor Throughput
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-This example will report the throughput for each stage independently. Keep in mind, ``buffer`` stages are necessary to
-decouple one stage from the next. Without the buffers, all monitoring would show the same throughput.
+This example will report the throughput for each stage independently.
 
 .. image:: img/multi_monitor_throughput.png
 
@@ -81,20 +80,18 @@ decouple one stage from the next. Without the buffers, all monitoring would show
    $ morpheus run pipeline-nlp --viz_file=basic_usage_img/multi_monitor_throughput.png  \
       from-file --filename examples/data/pcap_dump.jsonlines \
       monitor --description "From File Throughput" \
-      buffer \
       deserialize \
       monitor --description "Deserialize Throughput" \
-      buffer \
       serialize \
       monitor --description "Serialize Throughput" \
-      buffer \
       to-file --filename out.jsonlines --overwrite
    Configuring Pipeline via CLI
    Starting pipeline via CLI... Ctrl+C to Quit
+   From File Throughput[Complete]: 93085 messages [00:00, 93852.05 messages/s]
+   Deserialize Throughput[Complete]: 93085 messages [00:05, 16898.32 messages/s]
+   Serialize Throughput[Complete]: 93085 messages [00:08, 11110.10 messages/s]
    Pipeline visualization saved to basic_usage_img/multi_monitor_throughput.png
-   From File Throughput: 93085messages [00:09, 83515.94messages/s]
-   Deserialize Throughput: 93085messages [00:20, 9783.56messages/s]
-   Serialize Throughput: 93085messages [00:20, 9782.07messages/s]
+
 
 NLP Kitchen Sink
 ^^^^^^^^^^^^^^^^
@@ -108,17 +105,18 @@ This example shows an NLP Pipeline which uses most stages available in Morpheus.
    $ morpheus run --num_threads=8 --pipeline_batch_size=1024 --model_max_batch_size=32 \
       pipeline-nlp --viz_file=basic_usage_img/nlp_kitchen_sink.png  \
       from-file --filename examples/data/pcap_dump.jsonlines \
-      buffer --count=500 \
       deserialize \
       preprocess \
-      buffer \
       inf-triton --model_name=sid-minibert-onnx --server_url=localhost:8001 \
       monitor --description "Inference Rate" --smoothing=0.001 --unit "inf" \
       add-class \
       filter --threshold=0.8 \
       serialize --include 'timestamp' --exclude '^_ts_' \
-      to-kafka --output_topic "inference_output"
+      to-kafka --bootstrap_servers localhost:9092 --output_topic "inference_output" \
+      monitor --description "ToKafka Rate" --smoothing=0.001 --unit "msg"
    Configuring Pipeline via CLI
    Starting pipeline via CLI... Ctrl+C to Quit
+   Inference Rate[Complete]: 93085 inf [00:07, 12334.49 inf/s]
+   ToKafka Rate[Complete]: 93085 msg [00:07, 13297.85 msg/s]
    Pipeline visualization saved to basic_usage_img/nlp_kitchen_sink.png
-   Inference Rate: 16384inf [19:50, 13.83inf/s]
+