Metropolis Microservices

https://docs.nvidia.com/mms/text/
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Metropolis Microservices is an NVIDIA reference application for multi-camera video analytics that...
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### Install NVIDIA MMS Foundational Systems with Helm

Source: https://docs.nvidia.com/mms/text/MDX_Kubernetes_Deployment_prereq_foundational

Deploys the foundational systems for storage and monitoring using the mdx-foundation-sys-svcs Helm chart. This command installs the specified chart from a given URL, waits for the installation to complete, and uses override values for monitoring configurations. Ensure you have a Kubernetes cluster configured and your API key is set.

```bash
helm install mdx-foundation-sys-svcs --wait https://helm.ngc.nvidia.com/nfgnkvuikvjm/mdx-v2-0/charts/mdx-foundation-sys-svcs-v1.3.tgz --username='$oauthtoken' --password=YOUR_API_KEY -f application-helm-configs/foundational-sys/foundational-sys-monitoring-override-values.yaml
```

--------------------------------

### Download and Un-tar Deployment Artifact (Bash)

Source: https://docs.nvidia.com/mms/text/Multi_Camera_Sim2Deploy_Azure

This snippet demonstrates how to download the Metropolis Azure deployment artifact from NGC and then un-tar the contents. It verifies the necessary files for Azure infrastructure installation.

```bash
# download the artifact
$ ngc registry resource download-version nfgnkvuikvjm/mdx-v2-0/metropolis-azure-nv-one-click-script:0.0.1


## Untar deploy script artifact
$ cd metropolis-azure-nv-one-click-script_v0.0.1/
$ tar -xvf deploy-mdx-azure-cns.tar.gz


# verify necessary files required for Installing Infra on Azure CSP
$ ls
deploy-mdx-azure-cns.tar.gz  dist
$ cd dist


$ ls
ansible-requirements.yml  config-files         deploy-template.yml  envbuild.sh  modules    README.md
cns                       config.yml  envaccess.sh         iac-ref      playbooks   setup-system.sh
$
```

--------------------------------

### Install NVIDIA Container Toolkit - Bash

Source: https://docs.nvidia.com/mms/text/MDX_Download_install

Installs the NVIDIA Container Toolkit by setting up the repository, GPG key, installing the nvidia-docker2 package, configuring the Docker daemon, and restarting the Docker service. It requires root privileges for some commands.

```bash
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) 
&& curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg 
&& curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | 
 sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | 
 sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list 
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu20.04 nvidia-smi
```

--------------------------------

### Install Edge VST with Docker (Auto-discovery)

Source: https://docs.nvidia.com/mms/text/Edge_to_Cloud_Video_Streaming_Manual_Steps

Command to run the Edge VST Docker container with camera auto-discovery (ONVIF) enabled. This requires mounting a local directory containing configuration files to the container and utilizes host networking and GPU resources.

```bash
sudo docker run -it -v <absolute_host_path_of_configs_dir>/:/home/vst/vst_release/configs/ --net=host --gpus all nvcr.io/rxczgrvsg8nx/vst-1-0/vst:v1.2.33_x86_64 --vstConfigFile /home/vst/vst_release/configs/vst_config.json
```

--------------------------------

### Install Docker Engine on Ubuntu

Source: https://docs.nvidia.com/mms/text/MDX_Download_install

This sequence of commands installs the Docker Engine, CLI, containerd, and related plugins on an Ubuntu system. It includes steps to remove old versions, set up the Docker repository by adding its GPG key and the repository source, and finally install the necessary packages. Ensure your system is updated before running these commands.

```bash
# Uninstall old versions
for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done 

# Set up the repository
sudo apt-get update
sudo apt-get install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

# Add the repository to Apt sources:
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
$(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

# Install Docker Engine
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
```

--------------------------------

### Fetch Unique Object Count via Web API

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App_overview

Example cURL request to the Web API to fetch the count of unique objects within a specified time range and from selected sensors. This demonstrates how to interact with the Behavior Analytics data.

```bash
curl "http://localhost:8081/tracker/unique-object-count?timestamp=T&sensorIds=Building_K_Cam1&sensorIds=Building_K_Cam2&sensorIds=Building_K_Cam7"
```

--------------------------------

### SSH into Application Instance

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_GCP_CSP_Setup_Guide

Command to log into the application instance using SSH. Requires specifying the path to the PEM file, the bastion VM's public IP, and the application VM's private IP. Ensure the PEM file is associated with the public key used during setup.

```bash
ssh -i <path-to-pem-file> -o StrictHostKeyChecking=no -o ProxyCommand="ssh -i <path-to-pem-file> -W %h:%p -o StrictHostKeyChecking=no ubuntu@<bastion-vm-public-ip>" ubuntu@<app-vm-private-ip>

```

--------------------------------

### Install NVIDIA MMS Text Helm Chart

Source: https://docs.nvidia.com/mms/text/media-service/VST_Quickstart_guide

Installs the NVIDIA MMS Text Helm chart using the `helm install` command. Replace `<name>` with your desired release name and `<latest-version>` with the chart's version.

```bash
# with default config parameters
    helm install <name> <latest-version>
```

--------------------------------

### Install coturn Server for WebRTC Streaming

Source: https://docs.nvidia.com/mms/text/media-service/VST_Quickstart_guide

This command installs the coturn server, which is mandatory for WebRTC streaming and essential for establishing peer-to-peer connections when clients and servers are on different networks. Ensure your system meets the NAT type requirements for STUN/TURN.

```bash
sudo apt-get install coturn

```

--------------------------------

### Base Infrastructure Setup (YAML)

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_AWS_CSP_Setup_Guide

Defines the base infrastructure for an environment, including VPC CIDR block, SSH public key, and IP address ranges for development and user access. This section can be repeated for multiple base setups.

```yaml
base_infra:
  <your-base-env-name>:
    spec:
      vpc_cidr_block: '<replace-with-an-available-cidr-range>'
      ssh_public_key: '${_ssh_public_key}'
      dev_access_ipv4_cidr_blocks:
        - '<replace-with-list-of-dev-ip-cidrs>'
      user_access_ipv4_cidr_blocks:
        - '<replace-with-list-of-user-ip-cidrs>'
      base_domain: '<replace-with-the-dns-hosted-zone-under-which-apps-will-be-registered>'
```

--------------------------------

### Deploy Metropolis Application Using mtmc-app-deploy

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_GCP_CSP_Setup_Guide

Commands for managing the deployment of the Metropolis application and its infrastructure. Includes options for viewing available commands, previewing changes, installing, showing results, and uninstalling the application. Use 'preview' before 'install' and 'uninstall' for safety.

```bash
# To view available options
bash mtmc-app-deploy

# To preview changes based on deploy-template.yml without actually applying the changes
bash mtmc-app-deploy preview

# To install changes showed in preview option based on deploy-template.yml
bash mtmc-app-deploy install

# To show results/information about the project installed
bash mtmc-app-deploy show-results

# To uninstall the deployed infra and application
bash mtmc-app-deploy uninstall

```

--------------------------------

### Application Infrastructure Setup (YAML)

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_AWS_CSP_Setup_Guide

Sets up the application infrastructure, referencing a base environment and optionally a COTURN environment. It includes NGC API key and optional configurations for instance type, disk size, and specific application charts (foundational, app, nvstreamer, vst, ds) with their versions and override values files.

```yaml
app_infra:
  <your-application-env-name>:
    base_ref: '<replace-with-your-base-env-name>'
    #coturn_ref: '<replace-with-your-coturn-env-name>'
    spec:
      ngc_api_key: '${_ngc_api_key}'
      # --- OPTIONAL CONFIGURATION START ---
      app_instance_type: 'g4dn.12xlarge'
      app_instance_data_disk_size_gb: 1024
      foundational_chart:
        org: "nfgnkvuikvjm"
        team: "mdx-v2-0"
        name: "mdx-foundation-sys-svcs"
        version: "v1.3"
      app_chart:
        org: "nfgnkvuikvjm"
        team: "mdx-v2-0"
        name: "mdx-mtmc-app"
        version: "1.0.36"
      app_override_values_file: <abs_path>/mtmc_app_values.yaml
      nvstreamer_app_chart:
        org: "rxczgrvsg8nx"
        team: "vst-1-0"
        name: "nvstreamer"
        version: "0.2.23"
      nvstreamer_app_override_values_file: <abs_path>/nvstreamer_app_values.yaml
      vst_app_chart:
        org: "rxczgrvsg8nx"
        team: "vst-1-0"
        name: "vst"
        version: "1.0.24"
      vst_app_override_values_file: <abs_path>/vst_app_values.yaml
      ds_app_chart:
        org: "nfgnkvuikvjm"
        team: "mdx-v2-0"
        name: "mdx-wdm-ds-app"
        version: "0.0.32"
      wdm_ds_app_override_values_file: <abs_path>/wdm_ds_app_values.yaml
      # ---- OPTIONAL CONFIGURATION END ----
```

--------------------------------

### Download Metropolis GCP One-Click Script using NGC CLI

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_GCP_CSP_Setup_Guide

This command downloads the Metropolis GCP one-click deployment script from the NGC registry. It requires the NGC CLI to be installed and configured with the correct organization and team. The script is versioned and specifies the artifact to download.

```bash
# download the artifact
$ ngc registry resource download-version "nfgnkvuikvjm/mdx-v2-0/metropolis-gcp-one-click-script:0.0.5"

$ cd metropolis-gcp-one-click-script_v0.0.5/
```

--------------------------------

### Deploy and Manage Metropolis Workflows (Bash)

Source: https://docs.nvidia.com/mms/text/Multi_Camera_Sim2Deploy_Azure

This section provides bash commands for deploying, previewing, and uninstalling the Metropolis RTLS and SDG applications on Azure. It includes options for dry runs and skipping specific deployment steps.

```bash
# To view available options
$ ./envbuild.sh or  ./envbuild.sh --help

# To preview changes based on config.yml without actually applying the changes (-d flag will do dry run)
$ ./envbuild.sh install -c all -d 

# To install changes showed in preview option based on deploy-template.yml
$ ./envbuild.sh install -c all

# To make changes only part of deloyment, user can use skip flags(supported skip details in --help options)
$ $ ./envbuild.sh install -c all -i (This option will skip the changes targeted for infra related changes using envbuild script, similarly multiple flags can be at the same time)

# To uninstall the deployed infra and application
$ ./envbuild.sh uninstall -c all

```

--------------------------------

### Delete All Installed Helm Charts

Source: https://docs.nvidia.com/mms/text/FSL_Kubernetes_Deployment

Iterates through all installed Helm charts and deletes them. This script uses `helm ls -q` to get the names of all charts and then `helm delete` for each.

```bash
for helm_chart in `helm ls -q` ; do helm delete $helm_chart ; done
```

--------------------------------

### Start Calibration Monitoring (Scala)

Source: https://docs.nvidia.com/mms/text/MDX_BA_Customization

Usage example for starting the monitoring of calibration changes using `CalibrationEListener.start(config)`. This is part of a distributed system running on Spark executors.

```scala
import com.nvidia.mdx.core.transform.CalibrationEListener

CalibrationEListener.start(config)

```

--------------------------------

### Create Configuration File for NVStreamer Data Load (Bash)

Source: https://docs.nvidia.com/mms/text/Multi_Camera_Sim2Deploy_Azure

This snippet demonstrates how to create a configuration file named 'config.json' to specify parameters required for loading generated data into the NVStreamer microservice. It uses a heredoc in bash to define the JSON structure and its keys, including API keys, data and calibration folder names, and URLs for NVStreamer, VST, and DeepStream microservice charts. Ensure all placeholder values like '<your-ngc-key>' are replaced with actual credentials and folder names.

```bash
$ cat << EOF >> /mnt/sdg-data/config.json
{
"NGC_CLI_API_KEY": "<your-ngc-key>",
"blob_mount_sdg_data_folder": "<videodata-folder-name>",
"blob_mount_sdg_calibration_folder": "<calib-data-folder-name>",
"nvstreamer_chart_url": "https://helm.ngc.nvidia.com/rxczgrvsg8nx/vst-1-0/charts/nvstreamer-0.2.32.tgz",
"vst_chart_url": "https://helm.ngc.nvidia.com/rxczgrvsg8nx/vst-1-0/charts/vst-1.0.30.tgz",
"ds_chart_url": "https://helm.ngc.nvidia.com/nfgnkvuikvjm/mdx-v2-0/charts/mdx-wdm-ds-app-0.0.33.tgz"
}
EOF

```

--------------------------------

### Clean Up Docker Images and All Volumes

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App_overview

Commands to clean up Docker images and all cached volumes, including those related to calibration and deepstream. This provides a thorough cleanup for re-deployments.

```bash
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile e2e down --volumes --rmi all
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile playback down --volumes --rmi all
$ docker volume prune
```

```bash
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml -f mtmc-app/mdx-rtls-app.yml --profile e2e down --volumes --rmi all
$ docker volume prune
```

--------------------------------

### Environment Setup for ReID Fine-Tuning (Conda)

Source: https://docs.nvidia.com/mms/text/MDX_ReIdentificationNet

This snippet outlines the environment setup for fine-tuning ReIdentificationNet models using Conda. It includes creating a new environment, activating it, and installing JupyterLab.

```bash
conda create -n reid_finetuning python=3.11
conda activate reid_finetuning
conda install jupyterlab
```

--------------------------------

### Start Edge Client Connection with WebRTC

Source: https://docs.nvidia.com/mms/text/Edge_to_Cloud_Video_Streaming

This script initiates the connection of an edge client to a hosted MTMC application using WebRTC. It requires Docker to be installed and configured. The script logs into NGC and starts the NVStreamer and VST Proxy containers.

```bash
$ bash ./edgeinstall-webrtc.sh

```

--------------------------------

### Install People Analytics Microservices with Helm

Source: https://docs.nvidia.com/mms/text/MDX_Kubernetes_Deployment_ppl

This command installs the 'mdx-people-analytics-app' Helm chart, which is part of the Occupancy Analytics application. It requires updating a specific override values file with a Google Maps API key and uses NGC credentials for chart installation. This is essential for the Occupancy Analytics Web UI to render Google Map components.

```bash
helm install mdx-people-analytics-app https://helm.ngc.nvidia.com/nfgnkvuikvjm/mdx-v2-0/charts/mdx-people-analytics-app-1.0.54.tgz --username='$oauthtoken' --password=<NGC API KEY> -f application-helm-configs/people-analytics/people-analytics-app-override-values.yaml
```

--------------------------------

### Sample SDG Deployment Output

Source: https://docs.nvidia.com/mms/text/Multi_Camera_Sim2Deploy_AWS_Appendix

This snippet shows a sample output after a successful SDG deployment. It includes resource counts, and detailed information about the provisioned AWS infrastructure, such as S3 bucket details, application instance private IPs, ALB DNS name, and bastion/coturn instance IP addresses.

```bash
Apply complete! Resources: <nn> added, <nn> changed, <nn> destroyed.

Outputs:

S3_Bucket_details = {
  "<bastion_infra key>" = "<S3_Bucket_Name>"
}

app_infra = {
  "<app_infra key>" = {
    "private_ips" = [
      "<private_ip_of_app_instace>",
    ]
  }
}
app_infra = {
  "<app_infra key>" = {
    alb_dns_name = <dns_name_for_aws_lb>
    }
  }
bastion_infra = {
  "<bastion_infra key>" = {
    "private_ip" = "<bastion_instance_private_ip>"
    "public_ip" = "<bastion_instance_public_ip>"
  }
}
coturn_infra = {
  "<coturn_infra key" = {
    "port" = 3478
    "private_ip" = "<coturn_instance_private_ip>"
    "public_ip" = "<coturn_instance_public_ip>"
  }
}
```

--------------------------------

### Access URLs and SDG Details for RTLS + SDG Workflow

Source: https://docs.nvidia.com/mms/text/Multi_Camera_Sim2Deploy_Azure_Appendix

This section displays the accessible URLs for the deployed applications and provides crucial details for connecting to the Isaac Sim environment, including SSH, noVNC, and NoMachine access methods. It also outlines the necessary parameters for establishing these connections.

```text
===========================================================================================
access_urls:
  app:
    app: http://<App-gw-public-ip>:31080/ui/rtls/
    grafana: http://<App-gw-public-ip>:32300/login
    kibana: http://<App-gw-public-ip>:31560/app/kibana
    prometheus: http://<App-gw-public-ip>:30090/graph
sdg_details:
  sdg_info: |
          ************************************************************
          * Isaac Sim is deployed at <IsaacSim-VM-Public-IP> *
          ************************************************************

          * To connect to Isaac Sim via SSH:
          ssh -i <key-path/ssh.pem> -o StrictHostKeyChecking=no ubuntu@<IsaacSim-VM-Public-IP>

          * To connect to Isaac Sim via noVNC:
          1. Open http://<IsaacSim-VM-Public-IP>:6080/vnc.html?host=<IsaacSim-VM-Public-IP>&port=6080 in your browser.
          2. Click "Connect" and use password "<generated-pass>"

          * To connect to Isaac Sim via NoMachine:
          0. Download NoMachine client at https://www.nomachine.com/download, install, and launch it.
          1. Click "Add" button.
          2. Enter Host: "<IsaacSim-VM-Public-IP>".
          3. In "Configuration" > "Use key-based authentication with a key you provide",
             select file "<key-path/ssh.pem>".
          4. Click "Connect" button.
          5. Enter "ubuntu" as the username when prompted.
===========================================================================================
```

--------------------------------

### Behavior Pts API

Source: https://docs.nvidia.com/mms/text/MDX_Analytics_And_Tracking_API

Gets a behavior's start and end presentation time based on the video source, primarily for NVStreamer sources.

```APIDOC
## GET /api/behavior/pts

### Description
This endpoint is used to get a behavior’s start and end presentation time based on the video source. This is mainly used for NVStreamer sources.

### Method
GET

### Endpoint
/api/behavior/pts

### Parameters
#### Query Parameters
- **behavior_id** (string) - Required - The ID of the behavior.
- **video_source_id** (string) - Required - The ID of the video source.

### Request Example
```json
{
  "query": "/api/behavior/pts?behavior_id=beh1&video_source_id=nvstreamer1"
}
```

### Response
#### Success Response (200)
- **start_pts** (integer) - The start presentation time stamp (PTS) of the behavior.
- **end_pts** (integer) - The end presentation time stamp (PTS) of the behavior.

#### Response Example
```json
{
  "start_pts": 1234567890,
  "end_pts": 1234568890
}
```
```

--------------------------------

### Install NVStreamer Microservices with Helm

Source: https://docs.nvidia.com/mms/text/MDX_Kubernetes_Deployment_ppl

This command installs the 'nvstreamer-app' Helm chart, which is used for applications operating on video files. It requires NGC credentials and specifies an override values file for configuration. This is a prerequisite for uploading and managing videos within the NVStreamer UI.

```bash
helm install nvstreamer-app https://helm.ngc.nvidia.com/rxczgrvsg8nx/vst-1-0/charts/nvstreamer-0.2.32.tgz --username='$oauthtoken' --password=<NGC API KEY> -f application-helm-configs/people-analytics/nvstreamer-with-ingress-values.yaml
```

--------------------------------

### Restart DeepStream Microservice

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App_overview

Command to restart the DeepStream microservice, which is part of the Perception component. This is useful for reprocessing video data from the beginning.

```bash
docker restart mdx-deepstream
```

--------------------------------

### Install OpenVPN Edge Client

Source: https://docs.nvidia.com/mms/text/Edge_to_Cloud_Video_Streaming

This script installs and configures the OpenVPN client on the edge device to connect to the hosted environment. Ensure the 'profile.ovpn' file is in the current directory and the 'videos' directory has correct permissions before running. This setup is crucial for streaming video data from the edge to the cloud application.

```bash
$ sudo bash ./edgeinstall-openvpn.sh

```

--------------------------------

### Multi-Camera Tracking Component

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App_overview

This component consumes processed data (behaviors, trajectories, embeddings) and clusters them based on re-identification features and spatio-temporal information. It operates in live mode, consuming data from Kafka in micro-batches.

```APIDOC
## Multi-Camera Tracking

### Description
Consumes processed behavioral data and clusters it using re-identification embeddings and spatio-temporal information. Operates in live mode, processing micro-batches from Kafka.

### Workflow
- Consumes raw data processed into behaviors.
- Clusters behaviors based on re-identification feature embeddings and spatio-temporal information.
- Merges clustering results with existing IDs.

### Notes
- Pipeline workflow and configuration are detailed in the Multi-Camera Fusion microservice section.
```

--------------------------------

### Gracefully Shut Down Services with Docker Compose

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App_overview

Commands to gracefully stop and shut down all services using Docker Compose with specified profiles. This is crucial for a clean termination of running applications and their associated containers.

```bash
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile e2e down
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile playback down
```

```bash
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml -f mtmc-app/mdx-rtls-app.yml --profile e2e down
```

--------------------------------

### Check Kubernetes Pod Status

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_GCP_CSP_Setup_Guide

Command to execute within the application instance terminal to check the status of Kubernetes pods. All pods should eventually be in the 'Running' state after a successful deployment.

```bash
$ kubectl get pods

```

--------------------------------

### Clean Up Docker Images and Volumes Selectively

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App_overview

Commands to clean up Docker images and selectively remove cached volumes, excluding those related to 'deepstream' and 'calibration'. This helps manage disk space while preserving specific data.

```bash
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile e2e down --rmi all && docker volume rm `docker volume ls -q| grep -v 'deepstream|calibration'`
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile playback down --rmi all && docker volume rm `docker volume ls -q| grep -v 'deepstream|calibration'`
```

```bash
$ docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml -f mtmc-app/mdx-rtls-app.yml --profile e2e down --rmi all && docker volume rm `docker volume ls -q| grep -v 'deepstream|calibration'`
```

--------------------------------

### Deploy Template Configuration (YAML)

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_GCP_CSP_Setup_Guide

The deploy-template.yml file is a YAML configuration used to define the infrastructure for setting up projects and environments on GCP. It allows for the specification of provider configurations, coturn-infra, and multiple environments within a single project.

```yaml
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.

```

--------------------------------

### Deploy Multi-Camera Tracking App (Playback)

Source: https://docs.nvidia.com/mms/text/MDX_Multi_Camera_Tracking_App

Deploys the Multi-Camera Tracking app in playback mode, which omits NVStreamer, VST, Perception, and UI, using a playback module with saved metadata instead. This offers a lightweight pipeline for investigating data flow or replaying saved data. It uses Docker Compose with the '--profile playback' argument.

```bash
docker compose -f foundational/mdx-foundational.yml -f mtmc-app/mdx-mtmc-app.yml --profile playback up -d --pull always --build --force-recreate
```

--------------------------------

### Prepare Secrets Script (Bash)

Source: https://docs.nvidia.com/mms/text/MDX_APPS_One_Click_GCP_CSP_Setup_Guide

The secrets.sh script is used to securely store sensitive information like SSH public keys and NGC API keys. It exports these as environment variables, preventing them from being committed to version control.

```bash
#!/bin/bash

# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.

# _ssh_public_key -> Your public ssh key's content
export _ssh_public_key='<replace_content_between_quotes_with_your_value>'
# _ngc_api_key -> Your ngc api key value
export _ngc_api_key='<replace_content_between_quotes_with_your_value>'
# _turnserver_password -> Password for turn server
# This is not required if you are using 'use_twilio: true' in deploy-template.yml
export _turnserver_password='<replace_content_between_quotes_with_your_value>'

```

--------------------------------

### Platform and Application Configuration for MMS Text

Source: https://docs.nvidia.com/mms/text/Multi_Camera_Sim2Deploy_AWS_Appendix

Sets up platform and application specific configurations, including namespaces, port mappings, and NGC CLI API keys. It also defines Helm value directories, data URLs for SDG, and ISAAC SIM specific image and configuration settings.

```yaml
  platform:
    configs:
      platform_namespace: 'platform'
      grafana_port: 'grafana'
      prometheus_port: 'prometheus'
      ngc_cli_api_key: "{{ lookup('env', 'NGC_CLI_API_KEY') }}"
  app:
    configs:
      app_namespace: 'app'
      app_port: 'app'
      ngc_cli_api_key: "{{ lookup('env', 'NGC_CLI_API_KEY') }}"

      helm_values_dir:
        source_directory: "{{ playbook_dir }}/k8s-helm-values/MTMC-RTLS-SDG"
        destination_directory: "/mnt/helm-values"

      sdg_data:
        ngc_res_sdg_data_url: "nfgnkvuikvjm/mdx-v2-0/metropolis-apps-sample-input-data:v2.1-06132024"
        ngc_k8s_values_res_url: "nfgnkvuikvjm/mdx-v2-0/metropolis-apps-k8s-deployment:v2.1-06142024"

      isaacsim_configs:
        isaac_image: 'nvcr.io/nvidia/isaac-sim:4.0.0'
        oige: 'no'
        orbit: 'no'
        isaaclab: 'no'

    apps_state:
      cleanup_app: 'true'
      mtmc_app: 'false'
      pa_app: 'false'
      rtls_app: 'false'
      retail_app: 'false'
      rtls_sdg_app: 'true'

    helm_configs:
      app_chart_url: https://helm.ngc.nvidia.com/nfgnkvuikvjm/mdx-v2-0
      nvstreamer_app_chart_url: https://helm.ngc.nvidia.com/rxczgrvsg8nx/vst-1-0
      vst_app_chart_url: https://helm.ngc.nvidia.com/rxczgrvsg8nx/vst-1-0
      wdm_ds_app_chart_url: https://helm.ngc.nvidia.com/nfgnkvuikvjm/mdx-v2-0

      app_chart_version: 1.0.37
      nvstreamer_app_chart_version: 0.2.32
      vst_app_chart_version: 1.0.30
      wdm_ds_app_chart_version: 0.0.33
```

--------------------------------

### Install Edge VST with Docker (Predefined RTSP Streams)

Source: https://docs.nvidia.com/mms/text/Edge_to_Cloud_Video_Streaming_Manual_Steps

Command to run the Edge VST Docker container with predefined RTSP streams. This method involves modifying `rtsp_streams.json` and specifying it as an argument, along with the VST config file. It uses host networking and GPU resources.

```bash
sudo docker run -it -v <absolute_host_path_of_configs_dir>/:/home/vst/vst_release/configs/ --net=host --gpus all -e ADAPTOR=vst_rtsp nvcr.io/rxczgrvsg8nx/vst-1-0/vst:v1.2.33_x86_64 --vstConfigFile /home/vst/vst_release/configs/vst_config.json --rtspStreamsFile /home/vst/vst_release/configs/rtsp_streams.json
```
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