Corrected performance terms

benchmarks/adversarial_networks/tensorflow/dcgan/README.md

@@ -46,7 +46,7 @@ precision, and docker image to use, along with your path to the external model d
 for `--model-source-dir` (from step 1) `--data-location` (from step 2), and `--checkpoint` (from step 3).
 
 
-Run the model script for throughput and latency with `--batch-size=100` :
+Run the model script for batch and online inference with `--batch-size=100` :
 ```
 $ cd /home/<user>/models/benchmarks
 
@@ -65,7 +65,7 @@ $ python launch_benchmark.py \
 
 5. Log files are located at the value of `--output-dir`.
 
-Below is a sample log file tail when running for throughput:
+Below is a sample log file tail when running for batch inference:
 ```
 Batch size: 100 
 Batches number: 500

benchmarks/content_creation/tensorflow/draw/README.md

@@ -36,12 +36,12 @@ modes/precisions:
    $ cd models/benchmarks
    ```
 
-4. Run the model for either throughput or latency using the commands
+4. Run the model for either batch or online inference using the commands
    below. Replace in the path to the `--data-location` with your `mnist`
    dataset directory from step 1 and the `--checkpoint` files that you
    downloaded and extracted in step 2.
 
-   * Run DRAW for latency (with `--batch-size 1`):
+   * Run DRAW for online inference (with `--batch-size 1`):
      ```
         python launch_benchmark.py \
 	        --precision fp32 \
@@ -54,7 +54,7 @@ modes/precisions:
             --batch-size 1 \
             --socket-id 0
      ```
-    * Run DRAW for throughput (with `--batch-size 100`):
+    * Run DRAW for batch inference (with `--batch-size 100`):
       ```
         python launch_benchmark.py \
 	        --precision fp32 \
@@ -72,7 +72,7 @@ modes/precisions:
 
 5. The log files for each run are saved at the value of `--output-dir`.
 
-   * Below is a sample log file tail when testing latency:
+   * Below is a sample log file tail when testing online inference:
      ```
      ...
      Elapsed Time 0.006622
@@ -88,7 +88,7 @@ modes/precisions:
      Log location outside container: {--output-dir value}/benchmark_draw_inference_fp32_20190123_012947.log
      ```
 
-   * Below is a sample log file tail when testing throughput:
+   * Below is a sample log file tail when testing batch inference:
      ```
      Elapsed Time 0.028355
      Elapsed Time 0.028221

benchmarks/face_detection_and_alignment/tensorflow/facenet/README.md

@@ -43,10 +43,10 @@ precision, and docker image.
 Substitute in your own `--checkpoint` pretrained model file path (from step 3),
 and `--data-location` (from step 4).
 
-FaceNet can be run for testing latency, throughput, or accuracy. 
+FaceNet can be run for testing online inference, batch inference, or accuracy. 
 Use one of the following examples below, depending on your use case.
 
-* For latency (using `--batch-size 1`):
+* For online inference (using `--batch-size 1`):
 
 ```
 python launch_benchmark.py \
@@ -61,7 +61,7 @@ python launch_benchmark.py \
     --model-source-dir /home/<user>/facenet/ \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl
 ```
-Example log tail for latency:
+Example log tail for online inference:
 ```
 Batch 979 elapsed Time 0.0297989845276
 Batch 989 elapsed Time 0.029657125473
@@ -83,7 +83,7 @@ Ran inference with batch size 1
 Log location outside container: {--output-dir value}/benchmark_facenet_inference_fp32_20190328_205911.log
 ```
 
-* For throughput (using `--batch-size 100`):
+* For batch inference (using `--batch-size 100`):
 
 ```
 python launch_benchmark.py \
@@ -98,7 +98,7 @@ python launch_benchmark.py \
     --model-source-dir /home/<user>/facenet/ \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl
 ```
-Example log tail for throughput:
+Example log tail for batch inference:
 ```
 Batch 219 elapsed Time 0.446497917175
 Batch 229 elapsed Time 0.422048091888

benchmarks/face_detection_and_alignment/tensorflow/mtcc/README.md

@@ -60,7 +60,7 @@ Run:
 
 6. The log file is saved to the value of `--output-dir`.
 
-Below is a sample log file tail when running for model throughput, latency, and accuracy:
+Below is a sample log file tail when running for batch inference, online inference, and accuracy:
 
 ```
 time cost 0.459  pnet 0.166  rnet 0.144  onet 0.149

benchmarks/image_recognition/tensorflow/inception_resnet_v2/README.md

@@ -22,8 +22,7 @@ $ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/inceptio
 ```
 
 3. If you would like to run Inception ResNet V2 inference and test for
-accuracy, you will need the full ImageNet dataset. Running for latency
-and throughput do not require the ImageNet dataset.
+accuracy, you will need the full ImageNet dataset. Running for online and batch inference performance do not require the ImageNet dataset.
 
 Register and download the
 [ImageNet dataset](http://image-net.org/download-images).
@@ -66,7 +65,7 @@ only) and `--in-graph` pre-trained model file path (from step 2). Note
 that the docker image in the commands below is built using MKL PRs that
 are required to run Inception ResNet V2 Int8.
 
-Inception ResNet V2 can be run for accuracy, latency, or throughput. 
+Inception ResNet V2 can be run for accuracy, online inference, or batch inference. 
 Use one of the following examples below, depending on your use case. 
 
 For accuracy (using your `--data-location`, `--accuracy-only` and
@@ -85,7 +84,7 @@ python launch_benchmark.py \
     --data-location /home/<user>/datasets/ImageNet_TFRecords
 ```
 
-For latency (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`):
+For online inference (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`):
 
 ```
 python launch_benchmark.py \
@@ -100,7 +99,7 @@ python launch_benchmark.py \
     --in-graph /home/<user>/inception_resnet_v2_int8_pretrained_model.pb
 ```
 
-For throughput (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`):
+For batch inference (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`):
 
 ```
 python launch_benchmark.py \
@@ -134,7 +133,7 @@ Ran inference with batch size 100
 Log location outside container: <output directory>/benchmark_inception_resnet_v2_inference_int8_20190330_012925.log
 ```
 
-Example log tail when running for latency:
+Example log tail when running for online inference:
 ```
 ...
 Iteration 37: 0.046 sec
@@ -149,7 +148,7 @@ Ran inference with batch size 1
 Log location outside container: <output directory>/benchmark_inception_resnet_v2_inference_int8_20190330_012557.log
 ```
 
-Example log tail when running for throughput:
+Example log tail when running for batch inference:
 ```
 ...
 Iteration 37: 0.975 sec
@@ -183,16 +182,16 @@ For accuracy:
 $ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/inception_resnet_v2_fp32_pretrained_model.pb
 ```
 
-For throughput and latency:
+For batch and online inference:
 
 ```
 $ wget http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
 $ mkdir -p checkpoints && tar -C ./checkpoints/ -zxf inception_resnet_v2_2016_08_30.tar.gz
 ```
 
 3. If you would like to run Inception ResNet V2 inference and test for
-accuracy, you will need the full ImageNet dataset. Running for latency
-and throughput do not require the ImageNet dataset.
+accuracy, you will need the full ImageNet dataset. Running for online
+and batch inference do not require the ImageNet dataset.
 
 Register and download the
 [ImageNet dataset](http://image-net.org/download-images).
@@ -233,7 +232,7 @@ TF Records that you generated in step 3.
 Substitute in your own `--data-location` (from step 3, for accuracy
 only), `--checkpoint` pre-trained model checkpoint file path (from step 2).
 
-Inception ResNet V2 can be run for accuracy, latency, or throughput. 
+Inception ResNet V2 can be run for accuracy, online inference, or batch inference. 
 Use one of the following examples below, depending on your use case.
 
 For accuracy (using your `--data-location`, `--accuracy-only` and
@@ -252,7 +251,7 @@ python launch_benchmark.py \
     --data-location /home/<user>/datasets/ImageNet_TFRecords
 ```
 
-For latency (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`):
+For online inference (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`):
 
 ```
 python launch_benchmark.py \
@@ -268,7 +267,7 @@ python launch_benchmark.py \
     --data-location /home/<user>/datasets/ImageNet_TFRecords
 ```
 
-For throughput (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`):
+For batch inference (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`):
 
 ```
 python launch_benchmark.py \
@@ -304,7 +303,7 @@ Ran inference with batch size 100
 Log location outside container: {--output-dir value}/benchmark_inception_resnet_v2_inference_fp32_20190109_081637.log
 ```
 
-Example log tail when running for latency:
+Example log tail when running for online inference:
 ```
 eval/Accuracy[0]
 eval/Recall_5[0.01]
@@ -319,7 +318,7 @@ Ran inference with batch size 1
 Log location outside container: {--output-dir value}/benchmark_inception_resnet_v2_inference_fp32_20190108_015057.log
 ```
 
-Example log tail when running for throughput:
+Example log tail when running for batch inference:
 ```
 eval/Accuracy[0.00078125]
 eval/Recall_5[0.00375]

benchmarks/image_recognition/tensorflow/inceptionv3/README.md

@@ -78,7 +78,7 @@ only), `--in-graph` pretrained model file path (from step 3) and
 [tensorflow/models](https://github.com/tensorflow/models) repo
 (from step 2).
 
-Inception V3 can be run for accuracy, latency, or throughput. 
+Inception V3 can be run for accuracy, online inference, or batch inference. 
 Use one of the following examples below, depending on your use case.
 
 For accuracy (using your `--data-location`, `--accuracy-only` and
@@ -102,7 +102,7 @@ number of `warmup_steps` and `steps` as extra args, as shown in the
 commands below. If these values are not specified, the script will
 default to use `warmup_steps=10` and `steps=50`.
 
-For latency with ImageNet data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`):
+For online inference with ImageNet data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`):
 
 ```
 python launch_benchmark.py \
@@ -119,7 +119,7 @@ python launch_benchmark.py \
     -- warmup_steps=50 steps=500
 ```
 
-For latency with dummy data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`), remove `--data-location` argument:
+For online inference with dummy data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 1`), remove `--data-location` argument:
 
 ```
 python launch_benchmark.py \
@@ -135,7 +135,7 @@ python launch_benchmark.py \
     -- warmup_steps=50 steps=500
 ```
 
-For throughput with ImageNet data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`):
+For batch inference with ImageNet data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`):
 
 ```
 python launch_benchmark.py \
@@ -152,7 +152,7 @@ python launch_benchmark.py \
     -- warmup_steps=50 steps=500
 ```
 
-For throughput with dummy data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`), remove `--data-location` argument::
+For batch inference with dummy data (using `--benchmark-only`, `--socket-id 0` and `--batch-size 128`), remove `--data-location` argument::
 
 ```
 python launch_benchmark.py \
@@ -193,7 +193,7 @@ Ran inference with batch size 100
 Log location outside container: {--output-dir value}/benchmark_inceptionv3_inference_int8_20190104_013246.log
 ```
 
-Example log tail when running for latency:
+Example log tail when running for online inference:
 ```
 ...
 steps = 470, 53.7256017113 images/sec
@@ -206,7 +206,7 @@ Ran inference with batch size 1
 Log location outside container: {--output-dir value}/benchmark_inceptionv3_inference_int8_20190223_194002.log
 ```
 
-Example log tail when running for throughput:
+Example log tail when running for batch inference:
 ```
 ...
 steps = 470, 370.435654276 images/sec
@@ -234,8 +234,8 @@ $ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/inceptio
 ```
 
 3. If you would like to run Inception V3 FP32 inference and test for
-accuracy, you will need the ImageNet dataset. Running for latency
-and throughput do not require the ImageNet dataset. Instructions for
+accuracy, you will need the ImageNet dataset. Running for online
+and batch inference do not require the ImageNet dataset. Instructions for
 downloading the dataset and converting it to the TF Records format can
 be found in the TensorFlow documentation
 [here](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data).
@@ -249,10 +249,10 @@ precision, and docker image.
 
 Substitute in your own `--in-graph` pretrained model file path (from step 2).
 
-Inception V3 can be run for latency, throughput, or accuracy. Use one of the following examples below,
+Inception V3 can be run for online inference, batch inference, or accuracy. Use one of the following examples below,
 depending on your use case.
 
-* For latency with dummy data (using `--batch-size 1`):
+* For online inference with dummy data (using `--batch-size 1`):
 
 ```
 python launch_benchmark.py \
@@ -265,7 +265,7 @@ python launch_benchmark.py \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl \
     --in-graph /home/<user>/inceptionv3_fp32_pretrained_model.pb
 ```
-Example log tail when running for latency:
+Example log tail when running for online inference:
 ```
 Inference with dummy data.
 Iteration 1: 1.075 sec
@@ -285,7 +285,7 @@ Ran inference with batch size 1
 Log location outside container: {--output-dir value}/benchmark_inceptionv3_inference_fp32_20190104_025220.log
 ```
 
-* For throughput with dummy data (using `--batch-size 128`):
+* For batch inference with dummy data (using `--batch-size 128`):
 
 ```
 python launch_benchmark.py \
@@ -298,7 +298,7 @@ python launch_benchmark.py \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl \
     --in-graph /home/<user>/inceptionv3_fp32_pretrained_model.pb
 ```
-Example log tail when running for throughput:
+Example log tail when running for batch inference:
 ```
 Inference with dummy data.
 Iteration 1: 2.024 sec