documentation updates

Author: udaij12

docs/M1_support.md

@@ -0,0 +1,62 @@
+# M1 support
+
+## M1 Support 
+TorchServe supports Mac OS with M1 hardware. 
+
+1. TorchServe CI jobs now include M1 hardware in order to ensure support, [documentation](https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners/about-github-hosted-runners#standard-github-hosted-runners-for-public-repositories) on github M1 hardware.
+    - [Regression Tests](https://github.com/pytorch/serve/blob/master/.github/workflows/regression_tests_cpu.yml)  
+    - [Regression binaries Test](https://github.com/pytorch/serve/blob/master/.github/workflows/regression_tests_cpu_binaries.yml) 
+2. For [Docker](https://docs.docker.com/desktop/install/mac-install/) ensure Docker for Apple silicon is installed then follow [setup steps](https://github.com/pytorch/serve/tree/master/docker)
+## Running Torchserve on M1
+ 
+Follow [getting started documentation](https://github.com/pytorch/serve?tab=readme-ov-file#-quick-start-with-torchserve-conda) 
+
+### Example
+ 
+```
+(myenv) serve % pip list | grep torch                                                                   
+torch                     2.2.1
+torchaudio                2.2.1
+torchdata                 0.7.1
+torchtext                 0.17.1
+torchvision               0.17.1
+(myenv3) serve % conda install -c pytorch-nightly torchserve torch-model-archiver torch-workflow-archiver
+(myenv3) serve % pip list | grep torch                                                                   
+torch                     2.2.1
+torch-model-archiver      0.10.0b20240312
+torch-workflow-archiver   0.2.12b20240312
+torchaudio                2.2.1
+torchdata                 0.7.1
+torchserve                0.10.0b20240312
+torchtext                 0.17.1
+torchvision               0.17.1
+
+(myenv3) serve % torchserve --start --ncs  --models densenet161.mar --model-store ./model_store_gen/
+Torchserve version: 0.10.0
+Number of GPUs: 0
+Number of CPUs: 10
+Max heap size: 8192 M
+Config file: N/A
+Inference address: http://127.0.0.1:8080
+Management address: http://127.0.0.1:8081
+Metrics address: http://127.0.0.1:8082
+Initial Models: densenet161.mar
+Netty threads: 0
+Netty client threads: 0
+Default workers per model: 10
+Blacklist Regex: N/A
+Maximum Response Size: 6553500
+Maximum Request Size: 6553500
+Limit Maximum Image Pixels: true
+Prefer direct buffer: false
+Allowed Urls: [file://.*|http(s)?://.*]
+Custom python dependency for model allowed: false
+Enable metrics API: true
+Metrics mode: LOG
+Disable system metrics: false
+CPP log config: N/A
+Model config: N/A
+System metrics command: default
+...
+Model server started.
+```

docs/Security.md

@@ -5,6 +5,7 @@
 | Version | Supported          |
 |---------| ------------------ |
 | 0.9.0   | :white_check_mark: |
+| 0.10.0  | :white_check_mark: |
 
 
 ## How we do security
@@ -36,6 +37,7 @@ TorchServe as much as possible relies on automated tools to do security scanning
     2. Using private-key/certificate files
 
     You can find more details in the [configuration guide](https://pytorch.org/serve/configuration.html#enable-ssl)
+6. TorchServe supports token authorization: check [documentaion](https://github.com/pytorch/serve/blob/master/docs/token_authorization_api.md) for more information.