benchmark automation mention

Author: msaroufim

CONTRIBUTING.md

@@ -27,6 +27,8 @@ If you are interested in contributing to TorchServe, your contributions will fal
           ```
     - Run Regression test `python test/regression_tests.py`
     - For running individual test suites refer [code_coverage](docs/code_coverage.md) documentation
+    - If you are updating an existing model make sure that performance hasn't degraded by running [benchmarks](https://github.com/pytorch/serve/tree/master/benchmarks) on the master branch and your branch and verify there is no performance regression 
+    - For large changes make sure to run the [automated benchmark suite](https://github.com/pytorch/serve/tree/master/test/benchmark) which will run the apache bench tests on several configurations of CUDA and EC2 instances
     - If you need more context on a particular issue, please create raise a ticket on [`TorchServe` GH repo](https://github.com/pytorch/serve/issues/new/choose) or connect to [PyTorch's slack channel](https://pytorch.slack.com/)
 
 Once you finish implementing a feature or bug-fix, please send a Pull Request to https://github.com/pytorch/serve. Use this [template](pull_request_template.md) when creating a Pull Request.

README.md

@@ -48,17 +48,17 @@ TorchServe is a flexible and easy to use tool for serving PyTorch models.
 
     Refer to the documentation [here](docs/torchserve_on_win_native.md).
 
-2. Install torchserve and torch-model-archiver
+2. Install torchserve, torch-model-archiver and torch-workflow-archiver
 
     For [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install)  
     Note: Conda packages are not supported for Windows. Refer to the documentation [here](docs/torchserve_on_win_native.md).
     ```
-    conda install torchserve torch-model-archiver -c pytorch
+    conda install torchserve torch-model-archiver torch-workflow-archiver -c pytorch
     ```
 
     For Pip
     ```
-    pip install torchserve torch-model-archiver
+    pip install torchserve torch-model-archiver torch-workflow-archiver
     ```
 
 Now you are ready to [package and serve models with TorchServe](#serve-a-model).
@@ -71,7 +71,7 @@ Ensure that you have `python3` installed, and the user has access to the site-pa
 
 Run the following script from the top of the source directory.
 
-NOTE: This script uninstalls existing `torchserve` and `torch-model-archiver` installations
+NOTE: This script uninstalls existing `torchserve`, `torch-model-archiver` and `torch-workflow-archiver` installations
 
 #### For Debian Based Systems/ MacOS
 

benchmarks/README.md

@@ -6,6 +6,7 @@ We currently support benchmarking with JMeter & Apache Bench. One can also profi
 
 * [Benchmarking with JMeter](#benchmarking-with-jmeter)
 * [Benchmarking with Apache Bench](#benchmarking-with-apache-bench)
+* [AutoBenchmarking Apachage Bench on AWS](#benchmarking-apache-bench-aws)
 * [Profiling](#profiling)
 
 # Benchmarking with JMeter
@@ -315,6 +316,9 @@ The reports are generated at location "/tmp/benchmark/"
 ### Sample latency graph
 ![](predict_latency.png)
 
+# Benchmarking-apache-bench-aws
+If you're making a large change to TorchServe it's best to run an [automated benchmarking suite on AWS](https://github.com/pytorch/serve/tree/master/test/benchmark) so that you can test multiple CUDA versions and EC2 hardware configurations easily.
+
 # Profiling
 
 ## Frontend

binaries/README.md

@@ -9,7 +9,7 @@
    ```pwsh
    python .\ts_scripts\install_dependencies.py --environment=dev
    ```
-   > For GPU with Cuda 10.1, make sure add the `--cuda cu101` arg to the above command
+   > For GPU with Cuda 10.2, make sure add the `--cuda cu102` arg to the above command
    
 
 2. To build a `torchserve` and `torch-model-archiver` wheel execute: