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Improve VSD demo guide
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‎docs/dev-guide/vision-system-data/vision-system-data-demo.ipynb

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@@ -49,8 +49,7 @@
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"\n",
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"## Basics and understanding VSS and Vision System Data\n",
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"\n",
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"VSS (Vehicle Signal Specification) primarily resembles the physical structure of the vehicle, so\n",
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"there often isn't a need to repeat branches and data entries (like doors or axles). There isn't one\n",
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"VSS (Vehicle Signal Specification) primarily resembles the physical structure of the vehicle. There isn't one\n",
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"ADAS camera or controller manufacturer in the automotive industry, and OEMs use different vendors or\n",
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"the same OEM may use different vendors for different vehicle models. AWS IoT FleetWise provides an\n",
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"advantage because with VSS standardization, you can bring your own Interface Definition Language\n",
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"\n",
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"ROS 2 messages are modeled using four essential VSS components:\n",
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"\n",
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"- **Branch**: a branch is a tree node that contains other nodes. It can be a simple branch or a\n",
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" struct that has children nodes.\n",
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"- **Branch**: a branch is a tree node that contains other nodes.\n",
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"\n",
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"- **Sensor**: A sensor is a modeled primitive or complex value that is collected from the vehicle.\n",
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" For ROS 2, the ROS topic is modeled as a sensor, such as `/Cameras/Front/Image`.\n",
@@ -157,25 +155,27 @@
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"[here](https://aws.amazon.com/ec2/pricing/on-demand/).\n",
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"\n",
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"1. Launch an EC2 Graviton instance with administrator permissions:\n",
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" [**Launch CloudFormation Template**](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/quickcreate?templateUrl=https%3A%2F%2Faws-iot-fleetwise.s3.us-west-2.amazonaws.com%2Flatest%2Fcfn-templates%2Fvision-system-data-jupyter.yml&stackName=fwdev-jupyter).\n",
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" [**Launch CloudFormation Template**](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/quickcreate?templateUrl=https%3A%2F%2Faws-iot-fleetwise.s3.us-west-2.amazonaws.com%2Flatest%2Fcfn-templates%2Fvision-system-data-jupyter.yml&stackName=vision-system-data-jupyter).\n",
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"1. Enter the **Name** of an existing SSH key pair in your account from\n",
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" [here](https://us-east-1.console.aws.amazon.com/ec2/v2/home?region=us-east-1#KeyPairs:).\n",
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" 1. Do not include the file suffix `.pem`.\n",
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" 1. If you do not have an SSH key pair, you will need to create one and download the corresponding\n",
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" `.pem` file. Be sure to update the file permissions: `chmod 400 <PATH_TO_PEM>`\n",
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"1. **Select the checkbox** next to _'I acknowledge that AWS CloudFormation might create IAM\n",
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" resources with custom names.'_\n",
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"1. Choose **Create stack**.\n",
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"1. Wait until the status of the Stack is **CREATE_COMPLETE**; this can take up to ten minutes.\n",
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"1. For security purposes, the created stack does not expose the jupyter server to the internet.\n",
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" Instead, you will have to create an ssh port forwarding to you EC2 instance from your local\n",
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" machine. Execute the following from a local terminal, replacing `<NAME-OF-YOUR-KEY>` and\n",
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" `<IP-OF-EC2-INSTANCE>`:\n",
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" Instead, you will have to create an SSH port forwarding to you EC2 instance from your local\n",
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" machine. Go to the **Outputs** tab, then copy/paste the given `SSHPortForwarding` command to your\n",
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" local terminal, and adjust the path to your downloaded PEM file as required before executing it:\n",
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" ```bash\n",
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" chmod 400 <NAME-OF-YOUR-KEY>.pem\n",
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" ssh -i <NAME-OF-YOUR-KEY>.pem -N -L 8888:localhost:8888 ubuntu@<IP-OF-EC2-INSTANCE>\n",
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" ssh -i <PATH_TO_PEM> -N -L 8888:localhost:8888 ubuntu@<EC2_PUBLIC_IP_ADDRESS>\n",
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" ```\n",
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"1. With the ssh port forward set up, select the **Outputs** tab, and click on the link corresponding\n",
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" to `WebAccessWithPortForwarding`. This will automatically log you into the server. Note that by\n",
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" default, the `WebAccess` link will not be accessible as the default security group prevents\n",
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" public http access.\n",
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"1. With the SSH port forward set up, select the **Outputs** tab, and click on the link corresponding\n",
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" to `WebAccessWithPortForwarding`. This will automatically log you into the Jupyter Notebook server.\n",
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"1. Once logged in, click on `vision-system-data-demo.ipynb` from the left navigation bar to start\n",
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" the interactive demo"
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" the interactive demo."
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]
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},
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{
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"source": [
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"cd\n",
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"curl \"https://awscli.amazonaws.com/awscli-exe-linux-aarch64.zip\" -o \"awscliv2.zip\"\n",
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"unzip -q awscliv2.zip\n",
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"unzip -q -o awscliv2.zip\n",
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"sudo ./aws/install --update\n",
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"if ! ( aws iotfleetwise create-decoder-manifest help | grep -q ros2 ); then\n",
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" echo \"Error: The AWS CLI does not support the vision system data feature.\" >&2\n",
@@ -292,7 +292,10 @@
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"You can either download a pre-built binary, or build it from source.\n",
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"Note: If you want to use your own custom ROS2 messages, you will need to <a href=\"#optional-building-from-source\">build it from source</a>.\n",
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" Note:\n",
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"\n",
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" - If you want to use your own custom ROS2 messages, you will need to <a href=\"#optional-building-from-source\">build it from source</a>.\n",
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" - The command `install-deps-native.sh` below only installs a minimum ROS2 runtime, not the full `ros-galactic-desktop` package.\n",
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"</div>\n",
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"\n",
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"### Downloading the pre-built binary\n",
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}
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},
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"source": [
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"2. **Optional:** If you are interested in viewing the example source data you can also download the file to your local machine and open it in [Foxglove Studio](https://foxglove.dev/).\n",
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"**Optional:** If you are interested in viewing the example source data you can also download the file to your local machine and open it in [Foxglove Studio](https://foxglove.dev/).\n",
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"![Foxglove studio screenshot](foxglove-studio-screenshot.jpg)\n",
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"\n",
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" * Download the ROS2 rosbag file: https://s3.console.aws.amazon.com/s3/object/aws-iot-fleetwise?region=us-east-1&prefix=rosbag2_vision_system_data_demo.db3\n",
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" * Download and install Foxglove Studio: https://foxglove.dev/download\n",
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" * In Foxglove Studio, open the ROS2 rosbag file:\n",
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" * Click 'Open local file'.\n",
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" * Select the `rosbag2_vision_system_data_demo.db3` file.\n",
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" * Click on the 'Layouts' tab on the left.\n",
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" * Click on the 'Import layout' button.\n",
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" * Select the file in this folder `vision-system-data-foxglove-layout.json`.\n",
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" * Click the 'Play' button to playback the recording.\n",
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"* Download the ROS2 rosbag file: https://s3.console.aws.amazon.com/s3/object/aws-iot-fleetwise?region=us-east-1&prefix=rosbag2_vision_system_data_demo.db3\n",
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" * Download and install Foxglove Studio: https://foxglove.dev/download\n",
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" * In Foxglove Studio, open the ROS2 rosbag file:\n",
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" * Click 'Open local file'.\n",
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" * Select the `rosbag2_vision_system_data_demo.db3` file.\n",
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" * Click on the 'Layouts' tab on the left.\n",
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" * Click on the 'Import layout' button.\n",
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" * Select the file in this folder `vision-system-data-foxglove-layout.json`.\n",
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" * Click the 'Play' button to playback the recording.\n",
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"\n",
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"\n",
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"## Creating the necessary files"
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"source": [
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"For this demo, let's use the time-based campaign.\n",
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"\n",
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"Let's first populate the campaign definition with the correct signal catalog ARN and fleet ARN:"
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"Let's first populate the campaign definition with the correct signal catalog ARN and fleet ARN,\n",
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"and a data destination of the S3 bucket in [Apache Parquet](https://parquet.apache.org/) format:\n",
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"Note: To collect data in the JSON format, uncomment the second line.\n",
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"</div>"
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]
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},
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{
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}
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],
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"source": [
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"DATA_FORMAT=\"PARQUET\"\n",
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"# DATA_FORMAT=\"JSON\"\n",
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"\n",
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"CAMPAIGN=`cat ${S3_CAMPAIGN_FILE} \\\n",
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" | jq .name=\\\"${CAMPAIGN_NAME}\\\" \\\n",
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" | jq .signalCatalogArn=\\\"${SIGNAL_CATALOG_ARN}\\\" \\\n",
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" | jq .targetArn=\\\"${FLEET_ARN}\\\"`\n",
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" | jq \".name=\\\"${CAMPAIGN_NAME}\\\"\" \\\n",
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" | jq \".signalCatalogArn=\\\"${SIGNAL_CATALOG_ARN}\\\"\" \\\n",
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" | jq \".targetArn=\\\"${FLEET_ARN}\\\"\" \\\n",
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" | jq \".dataDestinationConfigs=[{ \\\n",
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" s3Config:{ \\\n",
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" bucketArn:\\\"arn:aws:s3:::${S3_BUCKET_NAME}\\\", \\\n",
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" prefix:\\\"${CAMPAIGN_NAME}-s3\\\", \\\n",
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" dataFormat:\\\"${DATA_FORMAT}\\\", \\\n",
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" storageCompressionFormat:\\\"NONE\\\" \\\n",
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" } \\\n",
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" }]\"`\n",
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"echo $CAMPAIGN | jq -C"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To deploy it to the vehicle, run:\n",
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"Note: to collect data in the json format, uncomment the second line.\n",
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"</div>"
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"To create the campaign, run:"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"DATA_FORMAT=\"PARQUET\"\n",
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"# DATA_FORMAT=\"JSON\"\n",
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"\n",
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"aws ${ENDPOINT_URL_OPTION} --region $REGION iotfleetwise create-campaign \\\n",
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" --cli-input-json \"${CAMPAIGN}\" \\\n",
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" --data-destination-configs \"[{\\\"s3Config\\\":{\\\"bucketArn\\\":\\\"arn:aws:s3:::${S3_BUCKET_NAME}\\\",\\\"prefix\\\":\\\"${CAMPAIGN_NAME}-s3\\\",\\\"dataFormat\\\":\\\"${DATA_FORMAT}\\\",\\\"storageCompressionFormat\\\":\\\"NONE\\\"}}]\"| jq -r .arn"
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"aws ${ENDPOINT_URL_OPTION} --region $REGION iotfleetwise create-campaign --cli-input-json \"${CAMPAIGN}\" | jq -r .arn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"After successful deployment, approve your campaign by running:"
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"After successful creation, approve your campaign by running the following command. This will deploy the campaign to the fleet."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To verify that the campaign was deployed to the vehicle, we wait until the vehicle status transitions from `READY` to `HEALTHY`"
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"To verify that the campaign was deployed to the vehicle in the fleet, we wait until the vehicle status transitions from `READY` to `HEALTHY`"
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]
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},
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{

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