Hotfix/docs (#19)

Updating documentation to reflect current purpose of this repo.

README.md

@@ -1,14 +1,15 @@
 # Model Zoo for Intel® Architecture
 
-This repository contains **links to pre-trained models, benchmarking scripts, best practices, and step-by-step tutorials** for many popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors. 
+This repository contains **links to pre-trained models, sample scripts, best practices, and step-by-step tutorials** for many popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors. 
 
 ## Purpose of the Model Zoo
 
   - Demonstrate the AI workloads and deep learning models Intel has optimized and validated to run on Intel hardware
   - Show how to efficiently execute, train, and deploy Intel-optimized models
-  - Make it easy to benchmark model performance on Intel hardware
   - Make it easy to get started running Intel-optimized models on Intel hardware in the cloud or on bare metal
 
+***DISCLAIMER: These scripts are not intended for benchmarking Intel platforms. Please see [https://www.intel.ai/blog](https://www.intel.ai/blog) for any information on performance and/or benchmarking information on specific Intel platforms.***
+
 ## How to Use the Model Zoo
 
 ### Getting Started
@@ -17,10 +18,10 @@ This repository contains **links to pre-trained models, benchmarking scripts, be
 
 ### Directory Structure
 The Model Zoo is divided into four main directories:
-- **[benchmarks](/benchmarks)**: Look here for benchmarking scripts and complete instructions on downloading and benchmarking each Intel-optimized pre-trained model.
+- **[benchmarks](/benchmarks)**: Look here for sample scripts and complete instructions on downloading and running each Intel-optimized pre-trained model.
 - **[docs](/docs)**: General best practices and detailed tutorials for a selection of models and frameworks can be found in this part of the repo. 
 - **[models](/models)**: This directory contains optimized model code that has not yet been upstreamed to its respective official repository, such as dataset processing routines. 
-  There are no user-friendly READMEs in this directory, but many supporting modules used for benchmarking are here.
+  There are no user-friendly READMEs in this directory, but many supporting modules are here.
 - **[tests](/tests)**: Look here for unit tests and information on how to run them. 
 
 The benchmarks, models, and docs folders share a common structure. Each model (or document) is organized first by *use case* and then by *framework*. 

benchmarks/README.md

@@ -1,10 +1,10 @@
-# Benchmark scripts
+# Model Zoo Scripts
 
 Training and inference scripts with Intel-optimized MKL
 
 ## Prerequisites
 
-The benchmarking scripts can be run on Linux and require the following
+The model scripts can be run on Linux and require the following
 dependencies to be installed:
 * [Docker](https://docs.docker.com/install/)
 * [Python](https://www.python.org/downloads/) 2.7 or later
@@ -13,7 +13,7 @@ dependencies to be installed:
 
 ## Use Cases
 
-| Use Case               | Framework     | Model               | Mode      | Benchmarking Instructions    |
+| Use Case               | Framework     | Model               | Mode      | Instructions    |
 | -----------------------| --------------| ------------------- | --------- |------------------------------|
 | Adversarial Networks   | TensorFlow    | [DCGAN](https://arxiv.org/pdf/1511.06434.pdf)               | Inference | [FP32](adversarial_networks/tensorflow/dcgan/README.md#fp32-inference-instructions) |
 | Content Creation       | TensorFlow    | [DRAW](https://arxiv.org/pdf/1502.04623.pdf)               | Inference | [FP32](content_creation/tensorflow/draw/README.md#fp32-inference-instructions) |
@@ -31,9 +31,9 @@ dependencies to be installed:
 | Language Translation   | TensorFlow    | [GNMT](https://arxiv.org/pdf/1609.08144.pdf)                | Inference | [FP32](language_translation/tensorflow/gnmt/README.md#fp32-inference-instructions) |
 | Language Translation   | TensorFlow    | [Transformer Language](https://arxiv.org/pdf/1706.03762.pdf)| Inference | [FP32](language_translation/tensorflow/transformer_language/README.md#fp32-inference-instructions) |
 | Language Translation   | TensorFlow    | [Transformer_LT_Official ](https://arxiv.org/pdf/1706.03762.pdf)| Inference | [FP32](language_translation/tensorflow/transformer_lt_official/README.md#fp32-inference-instructions) |
-| Object Detection       | TensorFlow    | [R-FCN](https://arxiv.org/pdf/1605.06409.pdf)               | Inference | [Int8](object_detection/tensorflow/rfcn/README.md#int8-inference-instructions) [FP32](object_detection/tensorflow/rfcn/README.md#fp32-inference-instructions) |
+| Object Detection       | TensorFlow    | [R-FCN](https://arxiv.org/pdf/1605.06409.pdf)               | Inference | [FP32](object_detection/tensorflow/rfcn/README.md#fp32-inference-instructions) |
 | Object Detection       | TensorFlow    | [Faster R-CNN](https://arxiv.org/pdf/1506.01497.pdf)        | Inference | [Int8](object_detection/tensorflow/faster_rcnn/README.md#int8-inference-instructions) [FP32](object_detection/tensorflow/faster_rcnn/README.md#fp32-inference-instructions) |
-| Object Detection       | TensorFlow    | [SSD-MobileNet](https://arxiv.org/pdf/1704.04861.pdf)       | Inference | [Int8](object_detection/tensorflow/ssd-mobilenet/README.md#int8-inference-instructions) [FP32](object_detection/tensorflow/ssd-mobilenet/README.md#fp32-inference-instructions) |
+| Object Detection       | TensorFlow    | [SSD-MobileNet](https://arxiv.org/pdf/1704.04861.pdf)       | Inference | [FP32](object_detection/tensorflow/ssd-mobilenet/README.md#fp32-inference-instructions) |
 | Object Detection       | TensorFlow    | [SSD-ResNet34](https://arxiv.org/pdf/1512.02325.pdf)        | Inference | [FP32](object_detection/tensorflow/ssd-resnet34/README.md#fp32-inference-instructions) |
 | Recommendation         | TensorFlow    | [NCF](https://arxiv.org/pdf/1708.05031.pdf)                 | Inference | [FP32](recommendation/tensorflow/ncf/README.md#fp32-inference-instructions) |
 | Recommendation         | TensorFlow    | [Wide & Deep Large Dataset](https://arxiv.org/pdf/1606.07792.pdf)	| Inference | [Int8](recommendation/tensorflow/wide_deep_large_ds/README.md#int8-inference-instructions) [FP32](recommendation/tensorflow/wide_deep_large_ds/README.md#fp32-inference-instructions) |

benchmarks/adversarial_networks/tensorflow/dcgan/README.md

@@ -4,7 +4,7 @@ This document has instructions for how to run DCGAN for the
 following modes/precisions:
 * [FP32 inference](#fp32-inference-instructions)
 
-Benchmarking instructions and scripts for model training and inference.
+Script instructions for model training and inference.
 
 ## FP32 Inference Instructions
 
@@ -35,19 +35,18 @@ repository:
 $ git clone https://github.com/IntelAI/models.git
 ```
 
-This repository includes launch scripts for running benchmarks and the
-an optimized version of the DCGAN model code.
+This repository includes launch scripts for running an optimized version of the DCGAN model code.
 
 5. Navigate to the `benchmarks` directory in your local clone of
 the [intelai/models](https://github.com/IntelAI/models) repo from step 4.
 The `launch_benchmark.py` script in the `benchmarks` directory is
-used for starting a benchmarking run in a optimized TensorFlow docker
+used for starting a model script run in a optimized TensorFlow docker
 container. It has arguments to specify which model, framework, mode,
 precision, and docker image to use, along with your path to the external model directory
 for `--model-source-dir` (from step 1) `--data-location` (from step 2), and `--checkpoint` (from step 3).
 
 
-Run benchmarking 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
 
@@ -66,7 +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 benchmarking 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

@@ -18,7 +18,7 @@ modes/precisions:
    ```
 
    The mnist directory will be passed as the dataset location when we
-   run the benchmarking script in step 4.
+   run the model script in step 4.
 
 2. Download and extract the pretrained model:
    ```
@@ -27,21 +27,21 @@ modes/precisions:
    ```
 
 3. Clone this [intelai/models](https://github.com/IntelAI/models) repo,
-   which contains the scripts that we will be using to run benchmarking
-   for DRAW.  After the clone has completed, navigate to the `benchmarks`
+   which contains the DRAW model scripts. 
+   After the clone has completed, navigate to the `benchmarks`
    directory in the repository.
 
    ```
    $ git clone https://github.com/IntelAI/models.git
    $ cd models/benchmarks
    ```
 
-4. Run benchmarking 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 benchmarking 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 benchmarking for throughput (with `--batch-size 100`):
+    * Run DRAW for batch inference (with `--batch-size 100`):
       ```
         python launch_benchmark.py \
 	        --precision fp32 \
@@ -70,9 +70,9 @@ modes/precisions:
       Note that the `--verbose` or `--output-dir` flag can be added to any of the above
       commands to get additional debug output or change the default output location.
 
-5. The log files for each benchmarking run are saved at the value of `--output-dir`.
+5. The log files for each run are saved at the value of `--output-dir`.
 
-   * Below is a sample log file tail when benchmarking 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 benchmarking 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

@@ -4,8 +4,7 @@ This document has instructions for how to run FaceNet for the
 following modes/precisions:
 * [FP32 inference](#fp32-inference-instructions)
 
-Benchmarking instructions and scripts for model training and inference
-other precisions are coming later.
+Script instructions for model training and inference for other precisions are coming later.
 
 ## FP32 Inference Instructions
 
@@ -37,18 +36,17 @@ Instructions for downloading the dataset and converting it can be found in the d
 5. Navigate to the `benchmarks` directory in your local clone of
 the [intelai/models](https://github.com/IntelAI/models) repo from step 2.
 The `launch_benchmark.py` script in the `benchmarks` directory is
-used for starting a benchmarking run in a optimized TensorFlow docker
+used for starting a model run in a optimized TensorFlow docker
 container. It has arguments to specify which model, framework, mode,
 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 latency benchmarking, throughput
-benchmarking, or accuracy. Use one of the following examples below,
-depending on your use case.
+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 \
@@ -63,7 +61,7 @@ python launch_benchmark.py \
     --model-source-dir /home/<user>/facenet/ \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl
 ```
-Example log tail when benchmarking for latency:
+Example log tail for online inference:
 ```
 Batch 979 elapsed Time 0.0297989845276
 Batch 989 elapsed Time 0.029657125473
@@ -85,7 +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 \
@@ -100,7 +98,7 @@ python launch_benchmark.py \
     --model-source-dir /home/<user>/facenet/ \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl
 ```
-Example log tail when benchmarking for throughput:
+Example log tail for batch inference:
 ```
 Batch 219 elapsed Time 0.446497917175
 Batch 229 elapsed Time 0.422048091888
@@ -134,7 +132,7 @@ python launch_benchmark.py \
     --model-source-dir /home/<user>/facenet/ \
     --docker-image intelaipg/intel-optimized-tensorflow:latest-devel-mkl
 ```
-Example log tail when benchmarking for accuracy:
+Example log tail for accuracy:
 ```
 Batch 219 elapsed Time 0.398629188538
 Batch 229 elapsed Time 0.354953050613

benchmarks/face_detection_and_alignment/tensorflow/mtcc/README.md

@@ -4,8 +4,7 @@ This document has instructions for how to run MTCC for the
 following modes/precisions:
 * [FP32 inference](#fp32-inference-instructions)
 
-Benchmarking instructions and scripts for the MTCC model training and inference
-other precisions are coming later.
+Instructions for MTCC model training and inference for other precisions are coming later.
 
 ## FP32 Inference Instructions
 
@@ -33,7 +32,7 @@ other precisions are coming later.
 ```
 
 4. Clone the [intelai/models](https://github.com/intelai/models) repo.
-This repo has the launch script for running benchmarking.
+This repo has the launch script for running models.
 
 ```
     $ git clone https://github.com/IntelAI/models.git
@@ -43,7 +42,7 @@ This repo has the launch script for running benchmarking.
 5. Run the `launch_benchmark.py` script from the intelai/models repo with the appropriate parameters including: the `--model-source-dir` from step 1, `--data-location` from step 2,
 and the `--checkpoint` from step 3.
 
-Run benchmarking:
+Run:
 ```
     $ cd /home/<user>/models/benchmarks
     
@@ -61,7 +60,7 @@ Run benchmarking:
 
 6. The log file is saved to the value of `--output-dir`.
 
-Below is a sample log file tail when running benchmarking for 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