{"data":{"slug":"bmw-innovationlab-bmw-tensorflow-inference-api-cpu","name":"BMW-TensorFlow-Inference-API-CPU","tagline":"Object detection inference API using TensorFlow framework","github_url":"https://github.com/BMW-InnovationLab/BMW-TensorFlow-Inference-API-CPU","owner":"BMW-InnovationLab","repo":"BMW-TensorFlow-Inference-API-CPU","owner_avatar_url":"https://avatars.githubusercontent.com/u/58600135?v=4","primary_language":"Python","stars":178,"forks":48,"topics":["api","bounding-boxes","computer-vision","computervision","cpu","deep-learning","deeplearning","detection-inference-api","docker","docker-ce","docker-container","docker-image","inference","inference-engine","inference-server","object-detection","predictions","rest-api","tensorflow","tensorflow-framework"],"archived":false,"github_pushed_at":"2022-06-28T13:37:59+00:00","maintenance_label":"Dormant","stars_delta_30d":0,"url":"https://www.graphcanon.com/tools/bmw-innovationlab-bmw-tensorflow-inference-api-cpu","markdown_url":"https://www.graphcanon.com/tools/bmw-innovationlab-bmw-tensorflow-inference-api-cpu.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bmw-innovationlab-bmw-tensorflow-inference-api-cpu","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bmw-innovationlab-bmw-tensorflow-inference-api-cpu","description":"This is a repository for an object detection inference API using the Tensorflow framework.","homepage_url":null,"license":"Apache-2.0","open_issues":1,"watchers":13,"ai_summary":"Repository providing an object detection inference API based on the TensorFlow framework for CPU usage. It includes Docker setup and instructions to run the service in various environments.","readme_excerpt":"### Install prerequisites\n\n#### Ubuntu\n\nUse the following command to install docker on Ubuntu:\n\n```sh\nchmod +x install_prerequisites.sh && source install_prerequisites.sh\n```\n\n#### Windows 10\n\nTo [install Docker on Windows](https://docs.docker.com/docker-for-windows/install/), please follow the link.\n\n**P.S: For Windows users, open the Docker Desktop menu by clicking the Docker Icon in the Notifications area. Select Settings, and then Advanced tab to adjust the resources available to Docker Engine.**\n\n---\n\n## Build The Docker Image\n\nIn order to build the project run the following command from the project's root directory:    \n\n```sh\nsudo docker build -t tensorflow_inference_api_cpu -f docker/dockerfile .\n```\n\n---\n\n## Run the docker container\n\nAs mentioned before, this container can be deployed using either **docker** or **docker swarm**.\n\nIf you wish to deploy this API using **docker**, please issue the following run command.\n\nIf you wish to deploy this API using **docker swarm**, please refer to following link [docker swarm documentation](./README-docker_swarm.md). After deploying the API with docker swarm, please consider returning to this documentation for further information about the API endpoints as well as the model structure sections.\n\nTo run the API, go the to the API's directory and run the following:\n\n#### Using Linux based docker:\n\n```sh\nsudo docker run -itv $(pwd)/models:/models -v $(pwd)/models_hash:/models_hash -p <docker_host_port>:4343 tensorflow_inference_api_cpu\n```\n\n#### Using Windows based docker:\n\n```sh\ndocker run -itv ${PWD}/models:/models -v ${PWD}/models_hash:/models_hash -p <docker_host_port>:4343 tensorflow_inference_api_cpu\n```\n\nThe <docker_host_port>  can be any unique port of your choice.\n\nThe API file will be run automatically, and the service will listen to http requests on the chosen port.","github_created_at":"2019-12-11T18:31:52+00:00","created_at":"2026-07-15T11:20:02.309934+00:00","updated_at":"2026-09-20T05:20:42.250122+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"api","name":"api"},{"slug":"bounding-boxes","name":"bounding-boxes"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"cpu","name":"cpu"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"docker","name":"docker"},{"slug":"inference-engine","name":"inference-engine"},{"slug":"object-detection","name":"object-detection"}],"trust":{"provenance":{"is_fork":false,"github_id":227433938,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-20T05:20:38.739Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1544,"last_release_at":"2019-12-11T18:33:18Z","stars_delta_30d":0,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-15T11:20:04.304Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-20T05:20:40.749Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-20T05:20:40.749Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-20T05:20:40.749Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a dedicated object detection model and have the infrastructure setup for Docker to run services on CPU.","For teams that value Apache-2.0 licensing terms, seeking free and open-source solutions.","If your application requires low-latency inference where GPU overhead is unnecessary and cost-saving usage of CPUs is preferred."],"when_not_to_use":["Avoid if deep learning tasks require significant computation power that only a GPU can provide.","Not suitable for projects looking to deploy on cloud services without Docker support, since this tool depends heavily on Docker setup details."],"source":"enrich:decision_facts","observed_at":"2026-07-17T11:38:16.243Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"BMW-TensorFlow-Inference-API-CPU utilises TensorFlow on CPU for object detection tasks, offering Docker container support to streamline deployment across environments."}]}}