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ers tutorials create a function that returns bigquery results create a function that returns spanner results integrate with cloud databases codelabs build and test build sources to containers build functions to containers local testing serve http requests deploy services deploy container images continuous deployment from git deploy from source code deploy from compose use the cloud run remote mcp server deploy functions serve web traffic mapping custom domains serving static assets with cdn serving traffic from multiple regions automate failover with service health enable session affinity frontend proxying using nginx manage services view copy or delete services view or delete revisions traffic migration gradual rollouts rollbacks configure services overview capacity memory limits cpu limits gpu gpu configuration gpu performance best practices request timeout maximum concurrent requests about maximum concurrent requests per instance configure maximum concurrent requests billing optimize service configurations with recommender environment container port and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes cifs smb ephemeral disk execution environment sandboxes container health checks http 2 requests secrets service identity scaling about instance autoscaling for services maximum instances about maximum instances for services configure maximum instances minimum instances configure custom scaling controls manual scaling metadata description labels tags source deploy configurations supported language runtimes and base images configure automatic base image updates build environment variables build service account build worker pools invoke and trigger services invoke with https requests host a webhook target stream with websockets overview build a websocket chat service tutorial invoke asynchronously invoke services on a schedule create a workflow invoke services as part of a workflow connect a series of services from cloud functions and cloud run tutorial execute asynchronous tasks call a service from a pub sub push subscription trigger service from pub sub integrate image processing into pub sub sample tutorial trigger from events create triggers with eventarc pub sub triggers create pub sub eventarc triggers trigger functions from pub sub using eventarc trigger functions from routed log entries cloud storage triggers create triggers with cloud storage trigger services from cloud storage using eventarc trigger functions from cloud storage using eventarc firestore triggers create triggers with firestore trigger functions from events in a firestore database connect with other services using grpc best practices general development tips for services cost optimization optimize java services optimize python services optimize node js services load testing best practices understand zonal redundancy functions best practices overview configure event driven function retries execute job tasks to completion create jobs execute jobs execute jobs execute scheduled jobs execute jobs from workflows configure jobs container entrypoint cpu limits memory limits gpu gpu configuration gpu best practices environment variables container health checks volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk labels maximum retries parallelism secrets service identity task timeout tags manage jobs view or delete jobs view or stop job executions best practices jobs retries and checkpoints cost optimization perform continuous background work deploy worker pools deploy worker pools deploy worker pools from source code manage worker pools view or delete worker pools view or delete worker pool revisions instance splits and rollbacks configure worker pools capacity memory limits cpu limits gpu gpu configuration gpu best practices environment container and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk container health checks secrets service identity instance count metadata description labels scale based on external metrics autoscale worker pools with external metrics kafka autoscaler host github runners with worker pools autoscale worker pools based on prometheus metrics autoscale worker pools with pub sub pull subscriptions automate scaling with workflows cost optimization configure networking best practices for cloud run networking configure private networking send traffic to vpc network overview direct vpc register private ips for worker pools using cloud dns dual stack ipv4 and ipv6 migrate standard vpc connector to direct vpc vpc connectors send traffic to shared vpc network overview direct vpc migrate shared vpc connector to direct vpc connectors in service projects connectors in host project static outbound ip address network security restrict endpoint ingress services use vpc service controls vpc sc cloud service mesh secure security design overview authenticate requests overview allow public access custom audiences authenticate developers service to service authenticate users end user authentication tutorial secure your resources access control with iam configure iap for cloud run introduction to service identity protect services with cloud armor use binary authorization use cloud run threat detection use customer managed encryption keys manage custom constraints for projects view software supply chain security insights secure cloud run services tutorial multi tenant platforms running untrusted code monitor and log monitoring and logging overview view built in metrics write prometheus metrics write opentelemetry metrics log and view logs audit logging error reporting use distributed tracing for services run ai solutions overview explore resources ai agents overview build and deploy a2a agents overview deploy a2a agents build and deploy adk agents build and deploy n8n agents mcp servers overview build and deploy a remote mcp server tools code execution browser automation inference with gpus overview services run llm inference on cloud run gpus with ollama run agents with gemma 4 models on cloud run run opencv on cloud run with gpu acceleration run llm inference on cloud run gpus with hugging face transformers js jobs fine tune llms using gpus with cloud run jobs run batch inference using gpus with cloud run jobs gpu accelerated video transcoding with ffmpeg ai assisted development and vibe coding introduction to cloud run for ai assisted developers cookbook migrate an existing web service from app engine from cloud run functions 1st gen from aws lambda from heroku from cloud foundry migration overview choose an oci compliant strategy migrate to oci containers migrate configuration sample migration spring music from vmware tanzu from a vm using migrate to containers from kubernetes to gke troubleshoot introduction troubleshoot errors local troubleshooting tutorial known issues samples all cloud run code samples all cloud run functions code samples code samples for all products ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation application hosting cloud run guides send feedback optimize python applications for cloud run stay organized with collections save and categorize content based on your preferences this guide describes optimizations for cloud run services written in the python programming language along with background information to help you understand the tradeoffs involved in some of the optimizations the information on this page supplements the general optimization tips which also apply to python many of the best practices and optimizations in common python web based application revolve around handling concurrent requests both thread based and non blocking i o reducing response latency using connection pooling and batching non critical functions for example sending traces and metrics to background tasks optimize the container image optimize the container image to reduce load and startup times using these methods minimize files you load at startup optimize the wsgi server minimize files you load at startup to optimize startup time load only the required files at startup and reduce their size for large files consider the following options store large files such as ai models in your container for faster access consider loading these files after startup or at runtime consider configuring cloud storage volume mounts for large files that are not critical at startup such as media assets import only the required submodules from any heavy dependencies or import modules when required in your code instead of loading them at application startup optimize the wsgi server python has standardized the way that applications can interact with web servers by the implementation of the wsgi standard pep 3333 one of the more common wsgi servers is gunicorn which is used in much of the sample documentation optimize gunicorn add the following cmd to the dockerfile to optimize the invocation of gunicorn cmd exec gunicorn bind port workers 1 threads 8 timeout 0 main app if you are considering changing these settings adjust the number of workers and threads on a per application basis for example try to use a number of workers equal to the cores available and make sure there is a performance improvement then adjust the number of threads setting too many workers or threads can have a negative impact such as longer cold start latency more consumed memory smaller requests per second etc by default gunicorn spawns workers and listens on the specified port when starting up even before evaluating your application code in this case you should set up custom startup probes for your service since the cloud run default startup probe immediately marks a container instance as healthy as soon as it starts to listen on port if you want to change this behavior you can invoke gunicorn with the preload setting to evaluate your application code before listening this can help to identify serious runtime bugs at deploy time save memory resources you should consider what your application is preloading before adding this other wsgi servers you are not restricted to using gunicorn for running python in containers you can use any wsgi or asgi web server as long as the container listens on http port port as per the container runtime contract common alternatives include uwsgi uvicorn and waitress for example given file named main py containing the app object the following invocations would start a wsgi server uwsgi pip install pyuwsgi uwsgi http port s tmp app sock manage script name mount app main app uvicorn pip install uvicorn uvicorn port port host 0 0 0 0 main app waitress pip install waitress waitress serve port port main app these can either be added as a cmd exec line in a dockerfile or as a web entry in procfile when using google cloud s buildpacks optimize applications in your cloud run service code you can also optimize for faster startup times and memory usage reduce threads you can optimize memory by reducing the number of threads by using non blocking reactive strategies and avoiding background activities also avoid writing to the file system as mentioned in the general tips page if you want to support background activities in your cloud run service set your cloud run service to instance based billing so you can run background activities outside of requests and still have cpu access reduce startup tasks python web based applications can have many tasks to complete during startup such as preloading data warming up the cache and establishing connection pools when executed sequentially these tasks can be slow however if you want them to execute in parallel increase the number of cpu cores cloud run sends a real user request to trigger a cold start instance users who have a request assigned to a newly started instance might experience long delays improve security with slimline base images to improve security for your application use a slimline base image with fewer packages and libraries if you choose not to install python from source within your containers use an official python base image from docker hub these images are based on the debian operating system if you are using the python image from docker hub consider using the slim version these images are smaller because they don t include a number of packages that would be used to build wheels which you might not need to do for your application the python image comes with the gnu c compiler preprocessor and core utilities to identify the ten largest packages in a base image run the following command docker_image python or python slim docker run rm docker_image dpkg query wf installed size t package t description n sort n tail n10 column t s t because there are fewer of these low level packages the slim based images also offer less attack surface for potential vulnerabilities some of these images might not include the elements required to build wheels from source you can add specific packages back in by adding a run apt install line to your dockerfile for more information see using system packages in cloud run there are also options for non debian based containers the python alpine option might result in a much smaller container but many python packages might not have pre compiled wheels that support alpine based systems support is improving see pep 656 but continues to vary also consider using the distroless base image which doesn t contain any package managers shells or any other programs use pythonunbuffered environment variable for logging to see unbuffered logs from your python application set the environment variable pythonunbuffered when you set this variable stdout and stderr data is immediately visible in the container logs instead of being held in a buffer until a certain amount of data has accumulated or the stream is closed what s next for more tips see general tips migrating an existing service send feedback except as otherwise noted the content of this page is licensed under the creative commons attribution 4 0 license and code samples are licensed under the apache 2 0 license for details see the google developers site policies java is a registered trademark of oracle and or its affiliates last updated 2026 07 17 utc need to tell us more easy to understand easytounderstand thumb up solved my problem solvedmyproblem thumb up other otherup thumb up hard to understand hardtounderstand thumb down incorrect information or sample code incorrectinformationorsamplecode thumb down missing the information samples i need missingtheinformationsamplesineed thumb down other otherdown 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