Metadata-Version: 2.1 Name: infrastructure Version: 0.0.1 Summary: An empty CDK Python app Home-page: UNKNOWN Author: author License: UNKNOWN Description: # Getting Started Initial Configuration Define 3 names for your infrastructure in config.json: { "ecr-repository-name": "my-ecr-repository", "image-name": "my-image", "stack-id": "my-stack" } In deploy.sh, the REPO_NAME and IMAGE_NAME should match the values in your config.json REPO_NAME=my-ecr-repository # Should match the ecr repository name given in config.json IMAGE_NAME=my-image # Should match the image name given in config.json Define other parameters for your Docker container, such as number of vCPUs and RAM it should consume, in config.json: "cpu": 4096, "memory-limit-mib": 12288, "timeout-minutes": 2 "environment-variables": {} [Fargate CPU and Memory Limit Documentation](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/AWS_Fargate.html) Extending the code to meet your use case: Edit the topics-to-extract list in the config.json. These topics should all be in each rosbag file, as the emr pipeline will wait for all topics to arrive on s3 before processing the next batch Extend the ./service/app/engine.py file to add more complex transformation logic Customizing Input Add prefix and suffix filters for the S3 notifications in config.json deploy.sh with build=true will create an ecr repository in your account, if it does not yet exist, and push your docker image to that repository Then it will execute the CDK command to deploy all infrastructure defined in app.py and ecs_stack.py The `cdk.json` file tells the CDK Toolkit how to execute your app. This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .env directory. To create the virtualenv it assumes that there is a `python3` (or `python` for Windows) executable in your path with access to the `venv` package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually. To manually create a virtualenv on MacOS and Linux: ``` $ python3 -m venv .env ``` After the init process completes and the virtualenv is created, you can use the following step to test deployment ``` $ bash deploy.sh $ bash deploy.sh default synth true ``` To add additional dependencies, for example other CDK libraries, just add them to your `requirements.txt` or `setup.py` file and rerun the `pip install -r requirements.txt` command. ## Useful CDK commands * `bash deploy.sh ls false` list all stacks in the app * `bash deploy.sh synth false` emits the synthesized CloudFormation template * `bash deploy.sh deploy true` build and deploy this stack to your default AWS account/region * `bash deploy.sh diff true` compare deployed stack with current state * `bash deploy.sh docs false` open CDK documentation ## Topics in VSI Rosbag Files Data /as_tx/objects 197 msgs : derived_object_msgs/ObjectWithCovarianceArray /flir_adk/rgb_front_left/image_raw 198 msgs : sensor_msgs/Image /flir_adk/rgb_front_right/image_raw 197 msgs : sensor_msgs/Image /flir_adk/thermal/image_raw 195 msgs : sensor_msgs/Image /gps 980 msgs : visualization_msgs/Marker /imu_raw 986 msgs : sensor_msgs/Imu /muncaster/rgb/detections_only 197 msgs : fusion/image_detections /muncaster/thermal/detections_only 197 msgs : fusion/image_detections /nira_log/tgi 490 msgs : nira_log/tgi /os1_cloud_node/points 197 msgs : sensor_msgs/PointCloud2 /rosout 15 msgs : rosgraph_msgs/Log (2 connections) /vehicle/brake_info_report 493 msgs : dbw_mkz_msgs/BrakeInfoReport /vehicle/brake_report 493 msgs : dbw_mkz_msgs/BrakeReport /vehicle/fuel_level_report 98 msgs : dbw_mkz_msgs/FuelLevelReport /vehicle/gear_report 197 msgs : dbw_mkz_msgs/GearReport /vehicle/gps/fix 9 msgs : sensor_msgs/NavSatFix /vehicle/gps/time 9 msgs : sensor_msgs/TimeReference /vehicle/gps/vel 9 msgs : geometry_msgs/TwistStamped /vehicle/imu/data_raw 986 msgs : sensor_msgs/Imu /vehicle/joint_states 1974 msgs : sensor_msgs/JointState /vehicle/misc_1_report 196 msgs : dbw_mkz_msgs/Misc1Report /vehicle/sonar_cloud 33 msgs : sensor_msgs/PointCloud2 /vehicle/steering_report 988 msgs : dbw_mkz_msgs/SteeringReport /vehicle/surround_report 33 msgs : dbw_mkz_msgs/SurroundReport /vehicle/throttle_info_report 980 msgs : dbw_mkz_msgs/ThrottleInfoReport /vehicle/throttle_report 492 msgs : dbw_mkz_msgs/ThrottleReport /vehicle/tire_pressure_report 19 msgs : dbw_mkz_msgs/TirePressureReport /vehicle/twist 988 msgs : geometry_msgs/TwistStamped /vehicle/wheel_position_report 491 msgs : dbw_mkz_msgs/WheelPositionReport /vehicle/wheel_speed_report 981 msgs : dbw_mkz_msgs/WheelSpeedReport ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This library is licensed under the MIT-0 License. See the LICENSE file. Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers Classifier: License :: OSI Approved :: Apache Software License Classifier: Programming Language :: JavaScript Classifier: Programming Language :: Python :: 3 :: Only Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Topic :: Software Development :: Code Generators Classifier: Topic :: Utilities Classifier: Typing :: Typed Requires-Python: >=3.6 Description-Content-Type: text/markdown