Add linting steps to doc-automation workflow (#1855)

* Add linting steps to doc-automation workflow * Move changes to lint.yml * Some improvements * Fix bug * Some enhancements * Fix bug * Fix some links, a few typos and also linting steps * Fix intel-extension links and some others * Fix some typos * Fix remaining typos * Fix some other links * Fix bug * Add some words to wordllist.txt * Add some other words to wordlist.txt * Fix some links and improve markdown-lin-check config * Fix bug * Temporary fix a link * Fix 4 remaining links * Fix typos * Fix typos * Fix bug * Improve spellcheck and fix misspellings * Clean wordlist.txt abit * Fix GitHub 403 errors From tcort/markdown-link-check#201 (comment) * Fix md-link-check version * Fix a link Co-authored-by: Mark Saroufim <[email protected]>
pytorch · Oct 13, 2022 · 3a7187c · 3a7187c
1 parent b2f80d2
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diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
@@ -11,7 +11,7 @@ on:
 jobs:
   build:
     runs-on: ubuntu-latest
-    name: Test changed-files
+    name: Lint changed files
     steps:
       - uses: actions/checkout@v3
         with:
@@ -20,18 +20,24 @@ jobs:
       - name: Install lint utilities
         run: |
           pip install pre-commit
-          pre-commit install      
+          pre-commit install
 
-      - name: Get specific changed files
-        id: changed-files-specific
-        uses: tj-actions/[email protected]
+      - name: Check links in all markdown files
+        uses: gaurav-nelson/[email protected]
+        with:
+          use-verbose-mode: 'yes'
+          config-file: "ts_scripts/markdown_link_check_config.json"
+
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/[email protected]
         with:
           files: | 
             **/*.py
   
       - name: Lint all changed files
         run: |
-          for file in ${{ steps.changed-files-specific.outputs.all_changed_files }}; do
+          for file in ${{ steps.changed-files.outputs.all_changed_files }}; do
             pre-commit run --files $file
           done
           
@@ -43,3 +49,33 @@ jobs:
           echo "cd serve/"
           echo "pre-commit install"
           echo "pre-commit will lint your code for you, so git add and commit those new changes and this check should become green"
+  spellcheck:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Install dependencies
+        run: |
+          sudo apt-get install aspell aspell-en
+          pip install pyspelling
+
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/[email protected]
+        with:
+          files: | 
+            **/*.md
+
+      - name: Check spellings
+        run: |
+          sources=""
+          for file in ${{ steps.changed-files.outputs.all_changed_files }}; do
+            sources+=" -S $file"
+          done
+          pyspelling -c $GITHUB_WORKSPACE/ts_scripts/spellcheck_conf/spellcheck.yaml --name Markdown $sources
+
+      - name: In the case of misspellings
+        if: ${{ failure() }}
+        run: |
+          echo "Please fix the misspellings. If you are sure about some of them, "
+          echo "so append those to ts_scripts/spellcheck_conf/wordlist.txt"
diff --git a/.gitignore b/.gitignore
@@ -16,6 +16,7 @@ plugins/.gradle
 *.pem
 *.backup
 docs/sphinx/src/
+ts_scripts/spellcheck_conf/wordlist.dic
 
 # Postman files
 test/artifacts/

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -35,7 +35,7 @@ Your contributions will fall into two categories:
     - 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 typing running [benchmarks](https://github.com/pytorch/serve/tree/master/benchmarks) on the master branch and your branch and verify there is no performance regression
     - Run `ts_scripts/spellcheck.sh` to fix any typos in your documentation
-    - 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
+    - For large changes make sure to run the [automated benchmark suite](https://github.com/pytorch/serve/tree/master/benchmarks) 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.

diff --git a/README.md b/README.md
@@ -73,7 +73,7 @@ Refer to [torchserve docker](docker/README.md) for details.
 
 ## 🏆 Highlighted Examples
 * [🤗 HuggingFace Transformers](examples/Huggingface_Transformers)
-* [Model parallel inference](examples/Huggingface_Transformers#model-paralellism)
+* [Model parallel inference](examples/Huggingface_Transformers#model-parallelism)
 * [MultiModal models with MMF](https://github.com/pytorch/serve/tree/master/examples/MMF-activity-recognition) combining text, audio and video
 * [Dual Neural Machine Translation](examples/Workflows/nmt_transformers_pipeline) for a complex workflow DAG
 
@@ -96,7 +96,7 @@ To learn more about how to contribute, see the contributor guide [here](https://
 * [Optimize your inference jobs using dynamic batch inference with TorchServe on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/optimize-your-inference-jobs-using-dynamic-batch-inference-with-torchserve-on-amazon-sagemaker/)
 * [Using AI to bring children's drawings to life](https://ai.facebook.com/blog/using-ai-to-bring-childrens-drawings-to-life/)
 * [🎥 Model Serving in PyTorch](https://www.youtube.com/watch?v=2A17ZtycsPw)
-* [Evolution of Cresta’s machine learning architecture: Migration to AWS and PyTorch](https://aws.amazon.com/blogs/machine-learning/evolution-of-crestas-machine-learning-architecture-migration-to-aws-and-pytorch/)
+* [Evolution of Cresta's machine learning architecture: Migration to AWS and PyTorch](https://aws.amazon.com/blogs/machine-learning/evolution-of-crestas-machine-learning-architecture-migration-to-aws-and-pytorch/)
 * [🎥 Explain Like I’m 5: TorchServe](https://www.youtube.com/watch?v=NEdZbkfHQCk)
 * [🎥 How to Serve PyTorch Models with TorchServe](https://www.youtube.com/watch?v=XlO7iQMV3Ik)
 * [How to deploy PyTorch models on Vertex AI](https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-deploy-pytorch-models-vertex-ai)

diff --git a/benchmarks/README.md b/benchmarks/README.md
@@ -5,7 +5,7 @@ The benchmarks measure the performance of TorchServe on various models and bench
 We currently support benchmarking with JMeter & Apache Bench. One can also profile backend code with snakeviz.
 
 * [Benchmarking with Apache Bench](#benchmarking-with-apache-bench)
-* [Auto Benchmarking with Apache Bench](#Auto-Benchmarking-with-Apache-Bench)
+* [Auto Benchmarking with Apache Bench](#auto-benchmarking-with-apache-bench)
 * [Benchmarking and Profiling with JMeter](jmeter.md)
 
 # Benchmarking with Apache Bench
@@ -32,7 +32,7 @@ Apache Bench is available on Mac by default. You can test by running ```ab -h```
 
 * Windows
     - Download apache binaries from [Apache Lounge](https://www.apachelounge.com/download/)
-    - Extract and place the contents at some location eg: `C:\Program Files\`
+    - Extract and place the contents at some location e.g.: `C:\Program Files\`
     - Add this path `C:\Program Files\Apache24\bin`to the environment variable PATH.
     NOTE - You may need to  install Visual C++ Redistributable for Visual Studio 2015-2019.
 
@@ -156,7 +156,7 @@ The reports are generated at location "/tmp/benchmark/"
 ![](predict_latency.png)
 
 # Auto Benchmarking with Apache Bench
-`auto_benchmark.py` runs Apache Bench on a set of models and generates an easy to read `report.md` once [Apach bench installation](https://github.com/pytorch/serve/tree/master/benchmarks#installation-1) is done.
+`auto_benchmark.py` runs Apache Bench on a set of models and generates an easy to read `report.md` once [Apache bench installation](https://github.com/pytorch/serve/tree/master/benchmarks#installation-1) is done.
 
 ## How does the auto benchmark script work?
 Auto Benchmarking is tool to allow users to run multiple test cases together and generates final report. Internally, the workflow is:
@@ -214,6 +214,6 @@ If you need to run your benchmarks on a specific cloud or hardware infrastructur
 The high level approach
 1. Create a cloud instance in your favorite cloud provider
 2. Configure it so it can talk to github actions by running some shell commands listed here https://docs.github.com/en/actions/hosting-your-own-runners/adding-self-hosted-runners
-3. Tag your instances in https://github.com/pytorch/serve/settings/actions/runners
+3. Tag your instances in the runners tab on Github
 3. In the `.yml` make sure to use `runs-on [self-hosted, your_tag]`
 4. Inspect the results in https://github.com/pytorch/serve/actions and download the artifacts for further analysis
diff --git a/benchmarks/add_jmeter_test.md b/benchmarks/add_jmeter_test.md
@@ -4,7 +4,7 @@ A new Jmeter test plan for torchserve benchmark can be added as follows:
 
 * Assuming you know how to create a jmeter test plan. If not then please use this jmeter [guide](https://jmeter.apache.org/usermanual/build-test-plan.html)
 * Here, we will show you how 'MMS Benchmarking Image Input Model Test Plan' plan can be added.
-This test plan doesn following: 
+This test plan does following: 
 
     * Register a model - `default is resnet-18`
     * Scale up to add workers for inference
@@ -40,7 +40,7 @@ e.g. on macOS, type `jmeter` on commandline
 ![](img/inference.png)
 * Right Click on test plan to add `tearDown Thread Group` and configured required details indicated in the following screenshot
 
-![](img/teardn-tg.png)
+![](img/teardown-tg.png)
 * Right Click on `tearDown Thread Group` to add `HTTP Request` and configure `unregister` request per given screenshot
 
 ![](img/unregister.png)

diff --git a/benchmarks/img/teardn-tg.png → benchmarks/img/teardown-tg.png b/benchmarks/img/teardn-tg.png → benchmarks/img/teardown-tg.png
diff --git a/benchmarks/sample_report.md b/benchmarks/sample_report.md
@@ -10,29 +10,29 @@ TorchServe Benchmark on gpu
 
 |version|Benchmark|Batch size|Batch delay|Workers|Model|Concurrency|Requests|TS failed requests|TS throughput|TS latency P50|TS latency P90|TS latency P99|TS latency mean|TS error rate|Model_p50|Model_p90|Model_p99|predict_mean|handler_time_mean|waiting_time_mean|worker_thread_mean|cpu_percentage_mean|memory_percentage_mean|gpu_percentage_mean|gpu_memory_percentage_mean|gpu_memory_used_mean|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-|master|AB|1|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|2345.99|4|5|6|4.263|0.0|1.04|1.15|1.53|1.06|1.02|1.93|0.28|0.0|0.0|0.0|0.0|0.0|
-|master|AB|2|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|3261.31|3|4|5|3.066|0.0|1.36|1.91|2.18|1.45|1.41|0.17|0.44|0.0|0.0|0.0|0.0|0.0|
-|master|AB|4|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|2457.64|4|6|7|4.069|0.0|1.89|2.2|2.96|1.97|1.94|0.53|0.59|0.0|0.0|0.0|0.0|0.0|
-|master|AB|8|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|1640.2|5|9|11|6.097|0.0|2.95|3.15|3.43|3.0|2.96|1.06|0.8|0.0|0.0|0.0|0.0|0.0|
-|master|AB|1|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|3444.57|3|3|4|2.903|0.0|1.32|1.68|1.87|1.37|1.34|0.08|0.46|0.0|0.0|0.0|0.0|0.0|
-|master|AB|2|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|3275.88|3|4|5|3.053|0.0|1.61|2.23|2.51|1.72|1.68|0.01|0.55|0.0|0.0|0.0|0.0|0.0|
-|master|AB|4|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|2346.15|4|6|8|4.262|0.0|2.01|2.42|3.19|2.1|2.06|0.57|0.57|0.0|0.0|0.0|0.0|0.0|
-|master|AB|8|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|[input](10000)|0|1572.82|5|9|12|6.358|0.0|3.09|3.39|4.7|3.15|3.11|1.1|0.82|0.0|0.0|0.0|0.0|0.0|
+|master|AB|1|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|2345.99|4|5|6|4.263|0.0|1.04|1.15|1.53|1.06|1.02|1.93|0.28|0.0|0.0|0.0|0.0|0.0|
+|master|AB|2|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|3261.31|3|4|5|3.066|0.0|1.36|1.91|2.18|1.45|1.41|0.17|0.44|0.0|0.0|0.0|0.0|0.0|
+|master|AB|4|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|2457.64|4|6|7|4.069|0.0|1.89|2.2|2.96|1.97|1.94|0.53|0.59|0.0|0.0|0.0|0.0|0.0|
+|master|AB|8|100|4|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|1640.2|5|9|11|6.097|0.0|2.95|3.15|3.43|3.0|2.96|1.06|0.8|0.0|0.0|0.0|0.0|0.0|
+|master|AB|1|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|3444.57|3|3|4|2.903|0.0|1.32|1.68|1.87|1.37|1.34|0.08|0.46|0.0|0.0|0.0|0.0|0.0|
+|master|AB|2|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|3275.88|3|4|5|3.053|0.0|1.61|2.23|2.51|1.72|1.68|0.01|0.55|0.0|0.0|0.0|0.0|0.0|
+|master|AB|4|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|2346.15|4|6|8|4.262|0.0|2.01|2.42|3.19|2.1|2.06|0.57|0.57|0.0|0.0|0.0|0.0|0.0|
+|master|AB|8|100|8|[.mar](https://torchserve.pytorch.org/mar_files/mnist_v2.mar)|10|10000|0|1572.82|5|9|12|6.358|0.0|3.09|3.39|4.7|3.15|3.11|1.1|0.82|0.0|0.0|0.0|0.0|0.0|
 
 ## eager_mode_vgg16
 
 |version|Benchmark|Batch size|Batch delay|Workers|Model|Concurrency|Requests|TS failed requests|TS throughput|TS latency P50|TS latency P90|TS latency P99|TS latency mean|TS error rate|Model_p50|Model_p90|Model_p99|predict_mean|handler_time_mean|waiting_time_mean|worker_thread_mean|cpu_percentage_mean|memory_percentage_mean|gpu_percentage_mean|gpu_memory_percentage_mean|gpu_memory_used_mean|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-|master|AB|1|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|[input](10000)|0|277.64|353|384|478|360.178|0.0|13.27|14.49|18.55|13.61|13.57|343.11|0.35|69.2|11.3|22.25|12.4|2004.0|
-|master|AB|2|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|[input](10000)|0|284.7|344|377|462|351.248|0.0|25.69|29.79|49.7|26.86|26.82|320.57|0.84|33.3|11.29|16.25|12.39|2002.0|
-|master|AB|4|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|[input](10000)|0|298.66|331|355|386|334.831|0.0|50.61|54.65|72.63|51.69|51.64|278.95|1.33|66.7|11.63|16.0|12.81|2070.0|
-|master|AB|8|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|[input](10000)|0|302.97|321|367|401|330.066|0.0|100.17|108.43|134.97|102.03|101.97|222.5|2.62|0.0|12.1|15.25|13.4|2166.0|
+|master|AB|1|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|10000|0|277.64|353|384|478|360.178|0.0|13.27|14.49|18.55|13.61|13.57|343.11|0.35|69.2|11.3|22.25|12.4|2004.0|
+|master|AB|2|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|10000|0|284.7|344|377|462|351.248|0.0|25.69|29.79|49.7|26.86|26.82|320.57|0.84|33.3|11.29|16.25|12.39|2002.0|
+|master|AB|4|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|10000|0|298.66|331|355|386|334.831|0.0|50.61|54.65|72.63|51.69|51.64|278.95|1.33|66.7|11.63|16.0|12.81|2070.0|
+|master|AB|8|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16.mar)|100|10000|0|302.97|321|367|401|330.066|0.0|100.17|108.43|134.97|102.03|101.97|222.5|2.62|0.0|12.1|15.25|13.4|2166.0|
 
 ## scripted_mode_vgg16
 
 |version|Benchmark|Batch size|Batch delay|Workers|Model|Concurrency|Requests|TS failed requests|TS throughput|TS latency P50|TS latency P90|TS latency P99|TS latency mean|TS error rate|Model_p50|Model_p90|Model_p99|predict_mean|handler_time_mean|waiting_time_mean|worker_thread_mean|cpu_percentage_mean|memory_percentage_mean|gpu_percentage_mean|gpu_memory_percentage_mean|gpu_memory_used_mean|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-|master|AB|1|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|[input](10000)|0|282.06|351|368|430|354.53|0.0|13.18|13.91|18.68|13.41|13.37|337.73|0.33|80.0|11.32|23.25|12.4|2004.0|
-|master|AB|2|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|[input](10000)|0|288.03|345|363|406|347.18|0.0|25.68|29.08|40.61|26.53|26.49|316.93|0.83|37.5|11.31|16.5|12.39|2002.0|
-|master|AB|4|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|[input](10000)|0|296.25|332|356|447|337.552|0.0|50.72|55.09|84.0|52.09|52.04|281.21|1.34|0.0|11.63|16.0|12.81|2070.0|
-|master|AB|8|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|[input](10000)|0|301.07|324|367|407|332.147|0.0|100.49|109.71|136.18|102.69|102.63|223.7|2.59|0.0|0.0|0.0|0.0|0.0|
+|master|AB|1|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|10000|0|282.06|351|368|430|354.53|0.0|13.18|13.91|18.68|13.41|13.37|337.73|0.33|80.0|11.32|23.25|12.4|2004.0|
+|master|AB|2|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|10000|0|288.03|345|363|406|347.18|0.0|25.68|29.08|40.61|26.53|26.49|316.93|0.83|37.5|11.31|16.5|12.39|2002.0|
+|master|AB|4|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|10000|0|296.25|332|356|447|337.552|0.0|50.72|55.09|84.0|52.09|52.04|281.21|1.34|0.0|11.63|16.0|12.81|2070.0|
+|master|AB|8|100|4|[.mar](https://torchserve.pytorch.org/mar_files/vgg16_scripted.mar)|100|10000|0|301.07|324|367|407|332.147|0.0|100.49|109.71|136.18|102.69|102.63|223.7|2.59|0.0|0.0|0.0|0.0|0.0|
diff --git a/binaries/README.md b/binaries/README.md
@@ -1,6 +1,6 @@
 # Building TorchServe and Torch-Model-Archiver release binaries 
 1. Make sure all the dependencies are installed
-   ##### Linux and MacOs:
+   ##### Linux and macOS:
    ```bash
    python ts_scripts/install_dependencies.py --environment=dev
    ```
@@ -13,7 +13,7 @@
    
 
 2. To build a `torchserve` and `torch-model-archiver` wheel execute:
-   ##### Linux and MacOs:
+   ##### Linux and macOS:
    ```bash
    python binaries/build.py
    ```
@@ -26,7 +26,7 @@
    > For additional info on conda builds refer to [this readme](conda/README.md)
 
 3. Build outputs are located at
-    ##### Linux and MacOs:
+    ##### Linux and macOS:
    - Wheel files
      `dist/torchserve-*.whl`  
      `model-archiver/dist/torch_model_archiver-*.whl`
@@ -44,7 +44,7 @@
 
 # Install torchserve and torch-model-archiver binaries
 1. To install torchserve using the newly created binaries execute:
-    ##### Linux and MacOs:
+    ##### Linux and macOS:
    ```bash
    python binaries/install.py
    ```
@@ -56,7 +56,7 @@
    ```
 2. Alternatively, you can manually install binaries
    - Using wheel files
-      ##### Linux and MacOs:
+      ##### Linux and macOS:
       ```bash
       pip install dist/torchserve-*.whl
       pip install model-archiver/dist/torch_model_archiver-*.whl
@@ -70,7 +70,7 @@
       pip install .\workflow-archiver\dist\<torch_workflow_archiver_wheel>
       ```
    - Using conda packages
-      ##### Linux and MacOs:
+      ##### Linux and macOS:
      ```bash
       conda install --channel ./binaries/conda/output -y torchserve torch-model-archiver torch-workflow-archiver
      ```
@@ -80,7 +80,7 @@
 
 # Building TorchServe, Torch-Model-Archiver & Torch-WorkFlow-Archiver nightly binaries
 1. Make sure all the dependencies are installed
-   ##### Linux and MacOs:
+   ##### Linux and macOS:
    ```bash
    python ts_scripts/install_dependencies.py --environment=dev
    ```
@@ -93,7 +93,7 @@
 
 
 2. To build a `torchserve`, `torch-model-archiver` & `torch-workflow-archiver` nightly wheel execute:
-   ##### Linux and MacOs:
+   ##### Linux and macOS:
    ```bash
    python binaries/build.py --nightly
    ```
@@ -106,7 +106,7 @@
    > For additional info on conda builds refer to [this readme](conda/README.md)
 
 3. Build outputs are located at
-    ##### Linux and MacOs:
+    ##### Linux and macOS:
    - Wheel files
      `dist/torchserve-*.whl`
      `model-archiver/dist/torch_model_archiver-*.whl`

diff --git a/docker/README.md b/docker/README.md
@@ -29,7 +29,7 @@ cd serve/docker
 # Create TorchServe docker image
 
 Use `build_image.sh` script to build the docker images. The script builds the `production`, `dev` and `codebuild` docker images.
-| Parameter | Desciption |
+| Parameter | Description |
 |------|------|
 |-h, --help|Show script help|
 |-b, --branch_name|Specify a branch name to use. Default: master |
@@ -271,7 +271,7 @@ torch-model-archiver --model-name densenet161 --version 1.0 --model-file /home/m
 
 Refer [torch-model-archiver](../model-archiver/README.md) for details.
 
-6. desnet161.mar file should be present at /home/model-server/model-store
+6. densenet161.mar file should be present at /home/model-server/model-store
 
 # Running TorchServe in a Production Docker Environment.