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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-0b1ca91f36","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Intro to Snowpark Container Services with Snowflake Python APIs"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"ilesh Garish","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Snowpark Container Services","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/snowpark-container-services","tagIcon":""},"isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/intro-to-snowpark-container-services-with-python-api","quickstartHeroForkRepoLink":{"id":"button-7c672b433b","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/intro-to-snowpark-container-services-with-python-api"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Intro to Snowpark Container Services with Snowflake Python APIs","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/intro-to-snowpark-container-services-with-python-api","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-d9e419ecf9","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-84ed5ebf03",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-29addd1f68","description":"Through this quickstart guide, you will explore Snowpark Container Services using Python API","paragraphs":["<!-- ------------------------ ----------------------------------->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThrough this quickstart guide, you will explore \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E, which are now in Public Preview on AWS. You will explore Snowpark Container Services using \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-python-api/snowflake-python-overview\"\u003EPython API\u003C/a\u003E, which is now in Public Preview. You will learn the basic mechanics of working with Snowpark Container Services using Python API and build several introductory services. \u003Cstrong\u003EPlease note: this quickstart assumes some existing knowledge and familiarity with containerization (e.g. Docker) , basic familiarity with container orchestration. and basic familiartity with Python\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowpark Container Services?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/containers.png\" alt=\"Snowpark Container Service\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E is a fully managed container offering that allows you to easily deploy, manage, and scale containerized services, jobs, and functions, all within the security and governance boundaries of Snowflake, and requiring zero data movement. As a fully managed service, SPCS comes with Snowflake’s native security, RBAC support, and built-in configuration and operational best-practices.\u003C/p\u003E\n","\u003Cp\u003ESnowpark Container Services are fully integrated with both Snowflake features and third-party tools, such as Snowflake Virtual Warehouses and Docker, allowing teams to focus on building data applications, and not building or managing infrastructure. Just like all things Snowflake, this managed service allows you to run and scale your container workloads across regions and clouds without the additional complexity of managing a control plane, worker nodes, and also while having quick and easy access to your Snowflake data.\u003C/p\u003E\n","\u003Cp\u003EThe introduction of Snowpark Container Services on Snowflake includes the incorporation of new object types and constructs to the Snowflake platform, namely: images, image registry, image repositories, compute pools, specification files, services, and jobs.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/spcs_dev_to_deploy.png\" alt=\"Development to Deployment\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor more information on these objects, check out \u003Ca href=\"https://medium.com/snowflake/snowpark-container-services-a-tech-primer-99ff2ca8e741\"\u003Ethis article\u003C/a\u003E along with the Snowpark Container Services \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003Edocumentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EHow are customers and partners using Snowpark Container Services today? Containerized services on Snowflake open up the opportunity to host and run long-running services, like front-end web applications, all natively within your Snowflake environment. Customers are now running GPU-enabled machine learning and AI workloads, such as GPU-accelerated model training and open-source Large Language Models (LLMs) as jobs and as service functions, including fine-tuning of these LLMs on your own Snowflake data, without having to move the data to external compute infrastructure. Snowpark Container Services are an excellent path for deploying applications and services that are tightly coupled to the Data Cloud.\u003C/p\u003E\n","\u003Cp\u003ENote that in this quickstart, we will predominantly use the Python API to interact with Snowpark Container Services and their associated objects. Please refer to the \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-overview\"\u003EPython API support\u003C/a\u003E in Public Preview. Refer to the \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-overview\"\u003Edocumentation\u003C/a\u003E for more info.\u003C/p\u003E\n","\u003Ch3\u003EWhat you will learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe basic mechanics of how Snowpark Container Services works\u003C/li\u003E\u003Cli\u003EHow to deploy a long-running service with a UI and use volume mounts to persist changes in the file system\u003C/li\u003E\u003Cli\u003EHow to interact with Snowflake warehouses to run SQL queries from inside of a service\u003C/li\u003E\u003Cli\u003EHow to deploy a Service Function to perform basic calculations\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Ch5\u003E\u003Cstrong\u003EDownload the git repo here: https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services.git\u003C/strong\u003E.\u003C/h5\u003E\n","\u003Cp\u003EYou can simply download the repo as a .zip if you don't have Git installed locally.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.docker.com/products/docker-desktop/\"\u003EDocker Desktop\u003C/a\u003E installed\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.10\u003C/a\u003E installed\n\u003Cul\u003E\u003Cli\u003ENote that you will be creating a Python environment with 3.10 in the \u003Cstrong\u003ESetup the Local Environment\u003C/strong\u003E step\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E(Optional) \u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003E installed\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDownload the git repo here: https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services.git\u003C/strong\u003E. You can simply download the repo as a .zip if you don't have Git installed locally.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003C/li\u003E\u003Cli\u003E(Optional) \u003Ca href=\"https://code.visualstudio.com/\"\u003EVSCode\u003C/a\u003E (recommended) with the \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-docker\"\u003EDocker\u003C/a\u003E, \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=ms-python.python\"\u003EPython\u003C/a\u003E, and \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc\"\u003ESnowflake\u003C/a\u003E extensions installed.\u003C/li\u003E\u003Cli\u003EA non-trial Snowflake account in a supported \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview#available-regions\"\u003EAWS region\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA Snowflake account login with a role that has the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role. If not, you will need to work with your \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E to perform the initial account setup (e.g. creating the \u003Ccode\u003ECONTAINER_USER_ROLE\u003C/code\u003E and granting required privileges, as well as creating the OAuth Security Integration).\u003C/li\u003E\u003Cli\u003EInstall Python API \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-installing\"\u003EPython API Installtion\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA hosted Jupyter Notebook service running inside of Snowpark Container Services with a basic notebook\u003C/li\u003E\u003Cli\u003EA Python REST API to perform basic temperature conversions\u003C/li\u003E\u003Cli\u003EA Snowflake Service Function that leverages the REST API\u003C/li\u003E\u003C/ul\u003E\n<!-- ![e2e_ml_workflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/snowpark-ml-e2e.png) -->\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet up the Snowflake environment\u003C/h2\u003E\n","\u003Cp\u003ERun the following Python API code in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/00_setup.py\"\u003E\u003Ccode\u003E00_setup.py\u003C/code\u003E\u003C/a\u003E using the Snowpark Python Connector and Python API to create the role, database, warehouse, and stage that we need to get started:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# create a SnowflakeConnection instance\nconnection_acct_admin = connect(**CONNECTION_PARAMETERS_ACCOUNT_ADMIN)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_acct_admin)\n\n    # CREATE ROLE CONTAINER_USER_ROLE\n    root.roles.create(Role(\n        name='CONTAINER_USER_ROLE',\n        comment='My role to use container',\n    ))\n\n    # GRANT CREATE DATABASE ON ACCOUNT TO ROLE CONTAINER_USER_ROLE\n    # GRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    # GRANT CREATE COMPUTE POOL ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    # GRANT CREATE INTEGRATION ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    # GRANT MONITOR USAGE ON ACCOUNT TO  ROLE  CONTAINER_USER_ROLE;\n    # GRANT BIND SERVICE ENDPOINT ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('CONTAINER_USER_ROLE'),\n        securable=Securables.current_account,\n        privileges=[Privileges.create_database,\n                    Privileges.create_warehouse,\n                    Privileges.create_compute_pool,\n                    Privileges.create_integration,\n                    Privileges.monitor_usage,\n                    Privileges.bind_service_endpoint\n                    ],\n    ))\n\n    # GRANT IMPORTED PRIVILEGES ON DATABASE snowflake TO ROLE CONTAINER_USER_ROLE;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('CONTAINER_USER_ROLE'),\n        securable=Securables.database('snowflake'),\n        privileges=[Privileges.imported_privileges\n                    ],\n    ))\n\n    # grant role CONTAINER_USER_ROLE to role ACCOUNTADMIN;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('ACCOUNTADMIN'),\n        securable=Securables.role('CONTAINER_USER_ROLE'),\n    ))\n\n    # USE ROLE CONTAINER_USE_ROLE\n    root.session.use_role("CONTAINER_USE_ROLE")\n\n    # CREATE OR REPLACE DATABASE CONTAINER_HOL_DB;\n    root.databases.create(Database(\n        name="CONTAINER_HOL_DB",\n        comment="This is a Container Quick Start Guide database"\n    ), mode=CreateMode.or_replace)\n\n    # CREATE OR REPLACE WAREHOUSE CONTAINER_HOL_WH\n    #   WAREHOUSE_SIZE = XSMALL\n    #   AUTO_SUSPEND = 120\n    #   AUTO_RESUME = TRUE;\n    root.warehouses.create(Warehouse(\n        name="CONTAINER_HOL_WH",\n        warehouse_size="XSMALL",\n        auto_suspend=120,\n        auto_resume="true",\n        comment="This is a Container Quick Start Guide warehouse"\n    ), mode=CreateMode.or_replace)\n\n    # CREATE STAGE IF NOT EXISTS specs\n    # ENCRYPTION = (TYPE='SNOWFLAKE_SSE');\n    root.schemas[CONNECTION_PARAMETERS_ACCOUNT_ADMIN.get("schema")].stages.create(\n        Stage(\n            name="specs",\n            encryption=StageEncryption(type="SNOWFLAKE_SSE")\n    ))\n\n    # CREATE STAGE IF NOT EXISTS volumes\n    # ENCRYPTION = (TYPE='SNOWFLAKE_SSE')\n    # DIRECTORY = (ENABLE = TRUE);\n    root.schemas[CONNECTION_PARAMETERS_ACCOUNT_ADMIN.get("schema")].stages.create(\n        Stage(\n            name="volumes",\n            encryption=StageEncryption(type="SNOWFLAKE_SSE"),\n            directory_table=StageDirectoryTable(enable="true")\n    ))\n    # create collection objects as the entry\nfinally:\n    connection_acct_admin.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the following Python API code in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/01_snowpark_container_services_setup.py\"\u003E\u003Ccode\u003E01_snowpark_container_services_setup.py\u003C/code\u003E\u003C/a\u003E using the Snowpark Python Connector and Python API to create the \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-container-services/additional-considerations-services-jobs#network-egress\"\u003EExternal Access Integration\u003C/a\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E\n# create a SnowflakeConnection instance\nconnection_acct_admin = connect(**CONNECTION_PARAMETERS_ACCOUNT_ADMIN)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_acct_admin)\n\n    connection_acct_admin.cursor().execute("""CREATE OR REPLACE NETWORK RULE ALLOW_ALL_RULE\n        TYPE = 'HOST_PORT'\n        MODE = 'EGRESS'\n        VALUE_LIST= ('0.0.0.0:443', '0.0.0.0:80');""")\n\n    connection_acct_admin.cursor().execute("""CREATE EXTERNAL ACCESS INTEGRATION ALLOW_ALL_EAI\n        ALLOWED_NETWORK_RULES = (ALLOW_ALL_RULE)\n        ENABLED = true;""")\n\n    # GRANT USAGE ON INTEGRATION ALLOW_ALL_EAI TO ROLE CONTAINER_USER_ROLE;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('CONTAINER_USER_ROLE'),\n        securable=Securables.integration("ALLOW_ALL_EAI"),\n        privileges=[Privileges.Usage]\n    ))\n\nfinally:\n        connection_acct_admin.close()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the following Python API code in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/01_snowpark_container_services_setup.py\"\u003E\u003Ccode\u003E01_snowpark_container_services_setup.py\u003C/code\u003E\u003C/a\u003E using the Snowpark Python Connector and Python API to create\nour first \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool\"\u003Ecompute pool\u003C/a\u003E, and our \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-registry-repository\"\u003Eimage repository\u003C/a\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    # USE ROLE CONTAINER_USE_ROLE\n    root.session.use_role("CONTAINER_USE_ROLE")\n\n    # CREATE COMPUTE POOL IF NOT EXISTS CONTAINER_HOL_POOL\n    # MIN_NODES = 1\n    # MAX_NODES = 1\n    # INSTANCE_FAMILY = standard_1;\n    root.compute_pools.create(ComputePool(\n      name="CONTAINER_HOL_POOL",\n      min_nodes=1,\n      max_nodes=1,\n      instance_family="CPU_X64_XS",\n    ))\n\n    # CREATE IMAGE REPOSITORY CONTAINER_HOL_DB.PUBLIC.IMAGE_REPO;\n    root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].image_repositories.create(ImageRepository(\n      name="IMAGE_REPO",\n    ))\n\n    # SHOW IMAGE REPOSITORIES IN SCHEMA CONTAINER_HOL_DB.PUBLIC;\n    itr_data = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].image_repositories.iter()\n    for image_repo in itr_data:\n        print(image_repo)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/user-guide/oauth-custom#create-a-snowflake-oauth-integration\"\u003EOAuth security integration\u003C/a\u003E will allow us to login to our UI-based services using our web browser and Snowflake credentials\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-container-services/additional-considerations-services-jobs#network-egress\"\u003EExternal Access Integration\u003C/a\u003E will allow our services to reach outside of Snowflake to the public internet\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool\"\u003Ecompute pool\u003C/a\u003E is the set of compute resources on which our services will run\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-registry-repository\"\u003Eimage repository\u003C/a\u003E is the location in Snowflake where we will push our Docker images so that our services can use them\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet up your local environment\u003C/h2\u003E\n","\u003Ch3\u003EPython Virtual Environment and SnowCLI\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload and install the miniconda installer from \u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\u003E. (OR, you may use any other Python environment with Python 3.10, for example, \u003Ca href=\"https://virtualenv.pypa.io/en/latest/\"\u003Evirtualenv\u003C/a\u003E).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen a new terminal window, navigate to your Git repo, and execute the following commands in the same terminal window:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the conda environment.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Econda env create -f conda_env.yml\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EActivate the conda environment.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Econda activate snowpark-container-services-hol\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EConfigure your Snowflake CLI connection. The Snowflake CLI is designed to make managing UDFs, stored procedures, and other developer centric configuration files easier and more intuitive to manage. Let's create a new connection using:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection add\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E# follow the wizard prompts to set the following values:\nname : CONTAINER_hol\naccount name: <ORG>-<ACCOUNT-NAME> # e.g. MYORGANIZATION-MYACCOUNT\nusername : <user_name>\npassword : <password>  \nrole: CONTAINER_USER_ROLE  \nwarehouse : CONTAINER_HOL_WH\nDatabase : CONTAINER_HOL_DB  \nSchema : public  \nconnection :   \nport : \nRegion :  \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# test the connection:\nsnow connection test --connection "CONTAINER_hol"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003EStart docker via opening Docker Desktop.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETest that we can successfully login to the image repository we created above, \u003Ccode\u003ECONTAINER_HOL_DB.PUBLIC.IMAGE_REPO\u003C/code\u003E. Run the following using Python API code \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/06_docker_jupyter_service.py\"\u003E\u003Ccode\u003E06_docker_jupyter_service.py\u003C/code\u003E\u003C/a\u003E :\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\n  connection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\n  try:\n\n      root = Root(connection_container_user_role)\n\n      # Get the image repository URL\n      #   use role CONTAINER_user_role;\n      #   show image repositories in schema CONTAINER_HOL_DB.PUBLIC;\n      #   // COPY the repository_url field, e.g. org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo\n      #   ```\n      #   ```bash\n      #   # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo, snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n      repos = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].image_repositories\n      repo = repos["IMAGE_REPO"].fetch()\n\n      # Extract the registry hostname from the repository URL\n      pattern = r'^[^/]+'\n\n      repository_url = repo.repository_url\n      match = re.match(pattern, repository_url)\n      registry_hostname = match.group(0)\n\n      # Docker client\n      client = docker.from_env()\n\n      #   docker login <snowflake_registry_hostname> -u <user_name>\n      #   > prompt for password\n      # Login to the remote registry. Give user name and password for docker login\n      client.login(username = "<username>",\n                          password = "<password>",\n                          registry = registry_hostname,\n                          reauth = True)\n  finally:\n      connection_container_user_role.close()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote the difference between \u003Ccode\u003EREPOSITORY_URL\u003C/code\u003E (\u003Ccode\u003Eorg-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo\u003C/code\u003E) and \u003Ccode\u003ESNOWFLAKE_REGISTRY_HOSTNAME\u003C/code\u003E (\u003Ccode\u003Eorg-account.registry.snowflakecomputing.com\u003C/code\u003E)\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild, Push, and Run the Jupyter Service\u003C/h2\u003E\n","\u003Ch3\u003EBuild and Test the Image Locally\u003C/h3\u003E\n","\u003Cp\u003EThe first service we are going to create is a hosted Jupyter notebook service. First, we will build and test the image locally. In the code repo, there is a \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/src/jupyter-snowpark/dockerfile\"\u003E\u003Ccode\u003E./src/jupyter-snowpark/dockerfile\u003C/code\u003E\u003C/a\u003E with the following contents:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003EFROM python:3.9\nLABEL author=""\n\n#Install packages & python base\nRUN apt-get update && \\\n    apt-get install -y python3-pip\n\nRUN pip3 install JPype1 jupyter pandas numpy seaborn scipy matplotlib seaborn pyNetLogo SALib "snowflake-snowpark-python[pandas]" snowflake-connector-python\n\n#Create a new user for the notebook server , NB RUN instrcution are only ever executed during the buid\nRUN useradd -ms /bin/bash jupyter   \n\n#set the user and working directory \nUSER jupyter\nWORKDIR /home/jupyter \n\n#other system settings\nEXPOSE 8888   \n\n#launch jupyter notebook server. NOTE!  ENTRYPOINT ( or CMD )intrscutions run each time a container is launched!\nENTRYPOINT ["jupyter", "notebook","--allow-root","--ip=0.0.0.0","--port=8888","--no-browser" , "--NotebookApp.token=''", "--NotebookApp.password=''"] \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is just a normal Dockerfile, where we install some packages, change our working directory, expose a port, and then launch our notebook service. There's nothing unique to Snowpark Container Services here!\u003C/p\u003E\n","\u003Cp\u003ELet's build and test the image locally from the terminal. \u003Cstrong\u003ENote it is a best practice to tag your local images with a \u003Ccode\u003Elocal_repository\u003C/code\u003E prefix.\u003C/strong\u003E Often, users will set this to a combination of first initial and last name, e.g. \u003Ccode\u003Ejsmith/my-image:latest\u003C/code\u003E. Navigate to your local clone of \u003Ccode\u003E.../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark\u003C/code\u003E and run a Docker build command using Python code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E\n# Build the Docker Image in the Example\n# cd .../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark\n# docker build --platform=linux/amd64 -t <local_repository>/python-jupyter-snowpark:latest .\nclient.images.build(path='sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark', platform='linux/aarch64', tag='<local_repository>/python-jupyter-snowpark:latest')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the image built successfully:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Check to see if the image is there\n# Verify the image built successfully:\n# docker image list\nclient.images.list()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that our local image has built, let's validate that it runs successfully. From a terminal run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Test running the image\n# docker run -d -p 8888:8888 <local_repository>/python-jupyter-snowpark:latest\ncontainer = client.containers.run(image='<local_repository>/python-jupyter-snowpark:latest', detach=True, ports={8888: 8888})\n\n# Use CURL to test the service\n# Open up a browser and navigate to [localhost:8888/lab](http://localhost:8888/lab) to verify \n# your notebook service is working locally. Once you've verified that the service is working,\n# you can stop the container:\nos.system("""curl -X GET  http://localhost:8888/lab""")\n\n# docker stop python-jupyter-snowpark\ncontainer.stop()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETag and Push the Image\u003C/h3\u003E\n","\u003Cp\u003ENow that we have a local version of our container working, we need to push it to Snowflake so that a Service can access the image. To do this we will create a new tag of the image that points at our image repository in our Snowflake account, and then push said tagged image. From a terminal, run the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Tag it\n    #  # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo, \n    #  snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n    #   docker login <snowflake_registry_hostname> -u <user_name>\n    #   > prompt for password\n    #   docker tag <local_repository>/python-jupyter-snowpark:latest <repository_url>/python-jupyter-snowpark:dev\n        # Grab the image\n    image = next(i for i in client.images.list() if "<local_repository>/python-jupyter-snowpark:latest" in i.tags)\n    image.tag(repository_url, 'dev')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote the difference\u003C/strong\u003E between \u003Ccode\u003EREPOSITORY_URL\u003C/code\u003E (\u003Ccode\u003Eorg-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo\u003C/code\u003E) and \u003Ccode\u003ESNOWFLAKE_REGISTRY_HOSTNAME\u003C/code\u003E (\u003Ccode\u003Eorg-account.registry.snowflakecomputing.com\u003C/code\u003E)\u003C/p\u003E\n","\u003Cp\u003EVerify that the new tagged image exists by running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Check to see if the image is there\n    # Verify the image built successfully:\n    # docker image list\n    client.images.list()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, we need to push our image to Snowflake. From the terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Push the image to the remote registry\n    # docker push <repository_url>/python-jupyter-snowpark:dev\n    client.api.push(repository_url + '/python-jupyter-snowpark:dev')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis may take some time, so you can move on to the next step \u003Cstrong\u003EConfigure and Push the Spec YAML\u003C/strong\u003E while the image is being pushed. Once the \u003Ccode\u003Edocker push\u003C/code\u003E command completes, you can verify that the image exists in your Snowflake Image Repository by running the following Python API code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    # USE ROLE CONTAINER_USER_ROLE;\n    # CALL SYSTEM$REGISTRY_LIST_IMAGES('/CONTAINER_HOL_DB/PUBLIC/IMAGE_REPO');\n    images = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].image_repositories["IMAGE_REPO"].list_images_in_repository()\n    for image in images:\n        print(image)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see your \u003Ccode\u003Epython-jupyter-snowpark\u003C/code\u003E image listed.\u003C/p\u003E\n","\u003Ch3\u003EConfigure and Push the Spec YAML\u003C/h3\u003E\n","\u003Cp\u003EServices in Snowpark Container Services are defined using YAML files. These YAML files configure all of the various parameters, etc. needed to run the containers within your Snowflake account. These YAMLs support a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference\"\u003Elarge number of configurable parameter\u003C/a\u003E, although we will not reference all of them here. Navigate to your local clone of \u003Ccode\u003E.../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark/jupyter-snowpark.yaml\u003C/code\u003E, which should look like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Espec:\n  containers:\n    - name: jupyter-snowpark\n      image: <repository_hostname>/container_hol_db/public/image_repo/python-jupyter-snowpark:dev\n      volumeMounts:\n        - name: jupyter-home\n          mountPath: /home/jupyter\n  endpoints:\n    - name: jupyter-snowpark\n      port: 8888\n      public: true\n  volumes:\n    - name: jupyter-home\n      source: "@volumes/jupyter-snowpark"\n      uid: 1000\n      gid: 1000\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE\u003C/strong\u003E: Update \u003Cstrong\u003E<repository_hostname>\u003C/strong\u003E for your image and save the file.\u003C/p\u003E\n","\u003Cp\u003ENow that the spec file is updated, we need to push it to our Snowflake Stage so that we can reference it next in our \u003Ccode\u003Ecreate service\u003C/code\u003E statement. We will use Python API to push the yaml file. Run the following using Python API code \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/08_stage_files.py\"\u003E\u003Ccode\u003E08_stage_files.py\u003C/code\u003E\u003C/a\u003E :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    # cd .../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark\n    # snow stage copy ./jupyter-snowpark.yaml @specs --overwrite --connection CONTAINER_hol\n    s = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].stages["SPECS"]\n    s.upload_file("./jupyter-snowpark.yaml", "/", auto_compress=False, overwrite=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can verify that your yaml was pushed successfully by running the following Python API code and verifying that the file is listed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003Econnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n\n    root = Root(connection_container_user_role)\n\n    #USE ROLE CONTAINER_USER_ROLE;\n    #LS @CONTAINER_HOL_DB.PUBLIC.SPECS;\n    stageFiles = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].stages["SPECS"].list_files()\n    for stageFile in stageFiles:\n        print(stageFile)\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate and Test the Service\u003C/h3\u003E\n","\u003Cp\u003EOnce we have successfully pushed our image and our spec YAML, we have all of the components uploaded to Snowflake that we need in order to create our service. There are three components required to create the service: a service name, a compute pool the service will run on, and the spec file that defines the service. Run the following Python script to create our Jupyter service:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # create service CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE\n    # in compute pool CONTAINER_HOL_POOL\n    # from @specs\n    # specification_file='jupyter-snowpark.yaml'\n    # external_access_integrations = (ALLOW_ALL_EAI);\n    s = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].services.create(Service(\n        name="JUPYTER_SNOWPARK_SERVICE",\n        compute_pool="CONTAINER_HOL_POOL",\n        spec=ServiceSpecStageFile(stage="specs", spec_file="jupyter-snowpark.yaml"),\n        external_access_integrations=["ALLOW_ALL_EAI"],\n    ))\n\n    # CALL SYSTEM$GET_SERVICE_STATUS('CONTAINER_HOL_DB.PUBLIC.jupyter_snowpark_service');\n    status = s.get_service_status()\n    print(status)\n\n    # CALL SYSTEM$GET_SERVICE_LOGS('CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE', '0', 'jupyter-snowpark',10);\n    logs = s.get_service_logs("0", "jupyter-snowpark", 10)\n    print(logs)\n\n    # SHOW ENDPOINTS IN SERVICE JUPYTER_SNOWPARK_SERVICE;\n    endpoints = s.get_endpoints()\n    for endpoint in endpoints:\n        print(endpoint)\n\n    # --- After we make a change to our Jupyter notebook,\n    # --- we will suspend and resume the service\n    # --- and you can see that the changes we made in our Notebook are still there!\n    # ALTER SERVICE CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE SUSPEND;\n    s.suspend()\n\n    # ALTER SERVICE CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE RESUME;\n    s.resume()\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThese script is the file \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/02_jupyter_service.py\"\u003E\u003Ccode\u003E02_jupyter_service.py\u003C/code\u003E\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ESince we specified that the \u003Ccode\u003Ejupyter-snowpark\u003C/code\u003E endpoint running on port \u003Ccode\u003E8888\u003C/code\u003E would be \u003Ccode\u003Epublic: true\u003C/code\u003E in our spec YAML, Snowflake is going to generate a URL for our service that can be used to access the service via our Web Browser. To get the URL, once the service is successfully in a \u003Ccode\u003ERUNNING\u003C/code\u003E state, execute the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    # SHOW ENDPOINTS IN SERVICE JUPYTER_SNOWPARK_SERVICE;\n    endpoints = s.get_endpoints()\n    for endpoint in endpoints:\n        print(endpoint)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy the \u003Ccode\u003Ejupyter-snowpark\u003C/code\u003E endpoint URL, and paste it in your browser. You will be asked to login to Snowflake via your username and password, after which you should successfully see your Jupyter instance running, all inside of Snowflake! \u003Cstrong\u003ENote, to access the service\u003C/strong\u003E the user logging in must have the \u003Ccode\u003ECONTAINER_USER_ROLE\u003C/code\u003E AND their default role cannot be \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, \u003Ccode\u003ESECURITYADMIN\u003C/code\u003E, or \u003Ccode\u003EORGADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EUpload and Modify a Jupyter Notebook\u003C/h3\u003E\n","\u003Cp\u003ENotice that in our spec YAML file we mounted the \u003Ccode\u003E@volumes/jupyter-snowpark\u003C/code\u003E internal stage location to our \u003Ccode\u003E/home/jupyter\u003C/code\u003E directory inside of our running container. What this means is that we will use our internal stage \u003Ccode\u003E@volumes\u003C/code\u003E to persist and store artifacts from our container. If you go check out the \u003Ccode\u003E@volumes\u003C/code\u003E stage in Snowsight, you'll see that when we created our \u003Ccode\u003Ejupyter_snowpark_service\u003C/code\u003E, a folder was created in our stage: \u003Ccode\u003E@volumes/jupyter-snowpark\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/stage_volume_mount.png\" alt=\"Stage Volume Mount\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, any file that is uploaded to \u003Ccode\u003E@volumes/jupyter-snowpark\u003C/code\u003E will be available inside of our container in the \u003Ccode\u003E/home/jupyter\u003C/code\u003E directory, and vice versa. Read more about volume mounts in the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference#spec-volumes-field-optional\"\u003Edocumentation\u003C/a\u003E. To test this out, let's upload the sample Jupyter notebook that is in our source code repo at \u003Ccode\u003E.../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark/sample_notebook.ipynb\u003C/code\u003E. To do this you can either\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Ccode\u003Ejupyter-snowpark\u003C/code\u003E directory in Snowsight, click the blue \u003Ccode\u003E+ Files\u003C/code\u003E button and drag/browse to \u003Ccode\u003Esample_notebook.ipynb\u003C/code\u003E. Click Upload. Navigate to your Jupyter service UI in your browser, click the refresh arrow and you should now see your notebook available!\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOR\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUpload \u003Ccode\u003Esample_notebook.ipynb\u003C/code\u003E to \u003Ccode\u003E@volumes/jupyter-snowpark\u003C/code\u003E using SnowCLI\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOR\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUpload \u003Ccode\u003Esample_notebook.ipynb\u003C/code\u003E directly in your Jupyter service  on the home screen by clicking the \u003Ccode\u003EUpload\u003C/code\u003E button. If you now navigate back to \u003Ccode\u003E@volumes/jupyter-snowpark\u003C/code\u003E in Snowsight, our run an \u003Ccode\u003Els @volumes/jupyter-snowpark\u003C/code\u003E SQL command, you should see  your \u003Ccode\u003Esample_notebook.ipynb\u003C/code\u003E file listed. Note you may need to hit the Refresh icon in Snowsight for the file to appear.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhat we've done is now created a Jupyter notebook which we can modify in our service, and the changes will be persisted in the file because it is using a stage-backed volume. Let's take a look at the contents of our \u003Ccode\u003Esample_notebook.ipynb\u003C/code\u003E. Open up the notebook in your Jupyter service:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/jupyter.png\" alt=\"Jupyter Notebook\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe want to pay special attention to the contents of the \u003Ccode\u003Eget_login_token()\u003C/code\u003E function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef get_login_token():\n    with open('/snowflake/session/token', 'r') as f:\n        return f.read()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen you start a service or a job, Snowflake provides credentials to the running containers in the form of an oauth token located at \u003Ccode\u003E/snowflake/session/token\u003C/code\u003E, enabling your container code to use Snowflake connectors for \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/additional-considerations-services-jobs#connecting-to-snowflake-from-inside-a-container\"\u003Econnecting to Snowflake\u003C/a\u003E and executing SQL (similar to any other code on your computer connecting to Snowflake). The provided credentials authenticate as the service role. Snowflake provides some of the information as environment variables in your containers.\u003C/p\u003E\n","\u003Cp\u003EEvery object in Snowflake has an owner role. In the case of a service or job, Snowflake has a concept called a service role (this term applies to both services and jobs). The service role determines what capabilities your service is allowed to perform when interacting with Snowflake. This includes executing SQL, accessing stages, and service-to-service networking.\u003C/p\u003E\n","\u003Cp\u003EWe configure the Snowpark Python Client to connect to our Snowflake account and execute SQL using this oauth token:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = {\n    "account": os.getenv('SNOWFLAKE_ACCOUNT'),\n    "host": os.getenv('SNOWFLAKE_HOST'),\n    "token": get_login_token(),\n    "authenticator": "oauth",\n    "database": "CONTAINER_HOL_DB",\n    "schema": "PUBLIC",\n    "warehouse": "CONTAINER_HOL_WH"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can run a sample query using our Snowpark session!\u003C/p\u003E\n","\u003Cp\u003EWe've successfully built and deployed our Jupyter Notebook service. Now let's move on to a REST API which we will interact with using a Service Function.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild, Push, and Run the Temperature Conversion REST API Service\u003C/h2\u003E\n","\u003Ch3\u003EBuild and Test the Image Locally\u003C/h3\u003E\n","\u003Cp\u003EThe next service we are going to create is a simple REST API that takes in Celsius temperatures and converts them to Fahrenheit- a trivial example. First, we will build and test the image locally. In the code repo, there is a \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/src/convert-api/dockerfile\"\u003E\u003Ccode\u003E./src/convert-api/dockerfile\u003C/code\u003E\u003C/a\u003E with the following contents:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003EFROM python:3.11\n\n# Copy the packages file into the build\nWORKDIR /app\nCOPY ./ /app/\n\n# run the install using the packages manifest file\nRUN pip install --no-cache-dir -r requirements.txt\n\n# Open port 9090\nEXPOSE 9090\n\n# When the container launches run the flask app\nENV FLASK_APP="convert-app.py"\nCMD ["flask", "run", "--host=0.0.0.0", "--port=9090"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is just a normal Dockerfile, where we install some packages, change our working directory, expose a port, and then launch our REST API. Our REST API is defined in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/src/convert-api/convert-app.py\"\u003E\u003Ccode\u003E./src/convert-api/convert-app.py\u003C/code\u003E\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom flask import Flask, request, jsonify\n\napp = Flask(__name__)\n\n# The function to convert Celsius to Fahrenheit\ndef celsius_to_fahrenheit(celsius):\n    return celsius * 9./5 + 32\n\n@app.route('/convert', methods=['POST'])\ndef convert():\n    # Get JSON data from request\n    data = request.get_json()\n\n    # Check if the 'data' key exists in the received JSON\n    if 'data' not in data:\n        return jsonify({'error': 'Missing data key in request'}), 400\n\n    # Extract the 'data' list from the received JSON\n    data_list = data['data']\n\n    # Initialize a list to store converted values\n    converted_data = []\n\n    # Iterate over each item in 'data_list'\n    for item in data_list:\n        # Check if the item is a list with at least two elements\n        if not isinstance(item, list) or len(item) < 2:\n            return jsonify({'error': 'Invalid data format'}), 400\n        \n        # Extract the Celsius value\n        celsius = item[1]\n        \n        # Convert to Fahrenheit and append to 'converted_data'\n        converted_data.append([item[0], celsius_to_fahrenheit(celsius)])\n\n    # Return the converted data as JSON\n    return jsonify({'data': converted_data})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe only thing unique to Snowflake about this container, is that the REST API code expects to receive requests in the format that \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/external-functions-data-format#body-format\"\u003ESnowflake External Function\u003C/a\u003E calls are packaged, and must also package the response in the expected format so that we can use it as a Service Function. \u003Cstrong\u003ENote this is only required if you intend to interact with the API via a SQL function\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003ELet's build and test the image locally from the terminal. **Note it is a best practice to tag your local images with a \u003Ccode\u003Elocal_repository\u003C/code\u003E prefix. Often, users will set this to a combination of first initial and last name, e.g. \u003Ccode\u003Ejsmith/my-image:latest\u003C/code\u003E. Navigate to your local clone of \u003Ccode\u003E.../sfguide-intro-to-snowpark-container-services/src/convert-api\u003C/code\u003E and run a Docker build command using Python. Run the following using Python API code \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/07_docker_rest_service.py\"\u003E\u003Ccode\u003E07_docker_rest_service.py\u003C/code\u003E\u003C/a\u003E :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Build the Docker Image in the Example\n    # cd .../sfguide-intro-to-snowpark-container-services/src/convert-api\n    # docker build --platform=linux/amd64 -t <local_repository>/convert-api:latest .\n    client.images.build(path='sfguide-intro-to-snowpark-container-services/src/convert-api', platform='linux/aarch64', tag='<local_repository>/convert-api:latest')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the image built successfully:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Check to see if the image is there\n    # Verify the image built successfully:\n    # docker image list\n    client.images.list()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that our local image has built, let's validate that it runs successfully. From a terminal run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Test running the image\n    # docker run -d -p 9090:9090 <local_repository>/convert-api:latest\n    container = client.containers.run(image='<local_repository>/convert-api:latest', detach=True, ports={9090: 9090})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest our local container endpoint by running the following from a different terminal window:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Use CURL to test the service\n    # curl -X POST -H "Content-Type: application/json" -d '{"data": [[0, 12],[1,19],[2,18],[3,23]]}' http://localhost:9090/convert\n    \n    os.system("""curl -X POST \n                -H "Content-Type: application/json" \n                -d '{"data": [[0, 12],[1,19],[2,18],[3,23]]}' \n                http://localhost:9090/convert """)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should recieve back a JSON object, this will contain the batch id and then the converted value in Fahrenheit:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E{"data":[[0,53.6],[1,66.2],[2,64.4],[3,73.4]]}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce you've verified that the service is working, you can stop the container: \u003Ccode\u003Econtainer.stop()\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ETag and Push the Image\u003C/h3\u003E\n","\u003Cp\u003ENow that we have a local version of our container working, we need to push it to Snowflake so that a Service can access the image. To do this we will create a new tag of the image that points at our image repository in our Snowflake account, and then push said tagged image. From a terminal, run the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E  # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo, snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n      #   docker login <snowflake_registry_hostname> -u <user_name>\n    #   > prompt for password\n    # Login to the remote registry. Give user name and password for docker login\n    client.login(username = "<username>",\n                        password = "<password>",\n                        registry = registry_hostname,\n                        reauth = True)\n\n     # Grab the image\n    image = next(i for i in client.images.list() if "<local_repository>/convert-api:latest" in i.tags)\n\n    # Tag it\n    #  # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo,\n    #  snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n    #   docker login <snowflake_registry_hostname> -u <user_name>\n    #   > prompt for password\n    #  docker tag <local_repository>/convert-api:latest <repository_url>/convert-api:dev\n    image.tag(repository_url, 'dev')\n \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote the difference between \u003Ccode\u003EREPOSITORY_URL\u003C/code\u003E (\u003Ccode\u003Eorg-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo\u003C/code\u003E) and \u003Ccode\u003ESNOWFLAKE_REGISTRY_HOSTNAME\u003C/code\u003E (\u003Ccode\u003Eorg-account.registry.snowflakecomputing.com\u003C/code\u003E)\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EVerify that the new tagged image exists by running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Check to see if the image is there\n    # Verify the image built successfully:\n    # docker image list\n    client.images.list()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, we need to push our image to Snowflake. From the terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E    # Push the image to the remote registry\n    # docker push <repository_url>/convert-api:dev\n    client.api.push(repository_url + '/convert-api:dev')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis may take some time, so you can move on to the next step \u003Cstrong\u003EConfigure and Push the Spec YAML\u003C/strong\u003E while the image is being pushed. Once the \u003Ccode\u003Edocker push\u003C/code\u003E command completes, you can verify that the image exists in your Snowflake Image Repository by running the following Python API code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    # USE ROLE CONTAINER_USER_ROLE;\n    # CALL SYSTEM$REGISTRY_LIST_IMAGES('/CONTAINER_HOL_DB/PUBLIC/IMAGE_REPO');\n    images = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].image_repositories["IMAGE_REPO"].list_images_in_repository()\n    for image in images:\n        print(image)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see your \u003Ccode\u003Econvert-api\u003C/code\u003E image listed.\u003C/p\u003E\n","\u003Ch3\u003EConfigure and Push the Spec YAML\u003C/h3\u003E\n","\u003Cp\u003EServices in Snowpark Container Services are defined using YAML files. These YAML files configure all of the various parameters, etc. needed to run the containers within your Snowflake account. These YAMLs support a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference\"\u003Elarge number of configurable parameter\u003C/a\u003E, although we will not reference all of them here. Navigate to your local clone of \u003Ccode\u003E.../sfguide-intro-to-snowpark-container-services/src/convert-api/convert-api.yaml\u003C/code\u003E, which should look like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Espec:\n  containers:\n    - name: convert-api\n      image: <repository_hostname>/container_hol_db/public/image_repo/convert-api:dev\n  endpoints:\n    - name: convert-api\n      port: 9090\n      public: true\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE\u003C/strong\u003E: Update \u003Cstrong\u003E<repository_hostname>\u003C/strong\u003E for your image and save the file.\u003C/p\u003E\n","\u003Cp\u003ENow that the spec file is updated, we need to push it to our Snowflake Stage so that we can reference it next in our \u003Ccode\u003Ecreate service\u003C/code\u003E statement. We will use Python API to push the yaml file. Run the following using Python API code \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/08_stage_files.py\"\u003E\u003Ccode\u003E08_stage_files.py\u003C/code\u003E\u003C/a\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    # cd .../sfguide-intro-to-snowpark-container-services/src/convert-api\n    # snow stage copy ./convert-api.yaml @specs --overwrite --connection CONTAINER_hol\n    s = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].stages["SPECS"]\n    s.upload_file("./convert-api.yaml", "/", auto_compress=False, overwrite=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can verify that your yaml was pushed successfully by running the Python code and verifying that the file is listed. Run the following using Python API code \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/08_stage_files.py\"\u003E\u003Ccode\u003E08_stage_files.py\u003C/code\u003E\u003C/a\u003E :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n\n    root = Root(connection_container_user_role)\n\n    #USE ROLE CONTAINER_USER_ROLE;\n    #LS @CONTAINER_HOL_DB.PUBLIC.SPECS;\n    stageFiles = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].stages["SPECS"].listFiles()\n    for stageFile in stageFiles:\n        print(stageFile)\n        \nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate and Test the Service\u003C/h3\u003E\n","\u003Cp\u003EOnce we have successfully pushed our image and our spec YAML, we have all of the components uploaded to Snowflake that we need in order to create our service. There are three components required to create the service: a service name, a compute pool the service will run on, and the spec file that defines the service. Run the following Python API script to create our Jupyter service:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # create service CONTAINER_HOL_DB.PUBLIC.convert_api\n    #     in compute pool CONTAINER_HOL_POOL\n    #     from @specs\n    #     specification_file='convert-api.yaml'\n    #     external_access_integrations = (ALLOW_ALL_EAI);\n    s = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].services.create(Service(\n        name="convert_api",\n        compute_pool="CONTAINER_HOL_POOL",\n        spec=ServiceSpecStageFile(stage="specs", spec_file="convert-api.yaml"),\n        external_access_integrations=["ALLOW_ALL_EAI"],\n    ))\n\n    # CALL SYSTEM$GET_SERVICE_STATUS('CONTAINER_HOL_DB.PUBLIC.CONVERT-API');\n    status = s.get_service_status()\n    print(status)\n\n    # CALL SYSTEM$GET_SERVICE_LOGS('CONTAINER_HOL_DB.PUBLIC.CONVERT_API', '0', 'convert-api',10);\n    logs = s.get_service_logs("0", "convert-api", 10)\n    print(logs)\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThese commands are also listed in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/03_rest_service.py\"\u003E\u003Ccode\u003E03_rest_service.py\u003C/code\u003E\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate and Test the Service Function\u003C/h3\u003E\n","\u003Cp\u003EOnce the service is up and running, we will create a Service Function that allows us to call our REST API's function via Python Connector. First, let's create a table and then create a Service function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # CREATE OR REPLACE TABLE WEATHER (\n    #     DATE DATE,\n    #     LOCATION VARCHAR,\n    #     TEMP_C NUMBER,\n    #     TEMP_F NUMBER\n    # );\n    root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].tables.create(\n        Table(\n            name="WEATHER",\n            columns=[\n                    TableColumn(name="DATE", datatype="DATE"),\n                    TableColumn(name="LOCATION", datatype="VARCHAR"),\n                    TableColumn(name="TEMP_C", datatype="NUMBER"),\n                    TableColumn(name="TEMP_F", datatype="NUMBER"),\n                ],\n        ),\n        mode=CreateMode.or_replace\n    )\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, let's insert some sample weather data in the table using Python Connector:\nThese commands are also listed in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/03_rest_service.py\"\u003E\u003Ccode\u003E03_rest_service.py\u003C/code\u003E\u003C/a\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    \n    # Connect as CONTAINER_USE_ROLE\n    connection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\n    connection_container_user_role.cursor().execute("""INSERT INTO weather (DATE, LOCATION, TEMP_C, TEMP_F)\n                        VALUES \n                            ('2023-03-21', 'London', 15, NULL),\n                            ('2023-07-13', 'Manchester', 20, NULL),\n                            ('2023-05-09', 'Liverpool', 17, NULL),\n                            ('2023-09-17', 'Cambridge', 19, NULL),\n                            ('2023-11-02', 'Oxford', 13, NULL),\n                            ('2023-01-25', 'Birmingham', 11, NULL),\n                            ('2023-08-30', 'Newcastle', 21, NULL),\n                            ('2023-06-15', 'Bristol', 16, NULL),\n                            ('2023-04-07', 'Leeds', 18, NULL),\n                            ('2023-10-23', 'Southampton', 12, NULL);""")\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, let's create a function that specifies our \u003Ccode\u003Econvert-api\u003C/code\u003E service's \u003Ccode\u003Econvert-api\u003C/code\u003E endpoint:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # CREATE OR REPLACE FUNCTION convert_udf (input float)\n    # RETURNS float\n    # SERVICE=CONVERT_API      //Snowflake container service\n    # ENDPOINT='convert-api'   //The endpoint within the container\n    # MAX_BATCH_ROWS=5         //limit the size of the batch\n    # AS '/convert';           //The API endpoint\n    root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].functions.create(\n        ServiceFunction(\n        name="convert_udf",\n        arguments=[\n            FunctionArgument(name="input", datatype="REAL")\n        ],\n        returns="REAL",\n        service="CONVERT_API",\n        endpoint="'convert-api'",\n        path="/convert",\n        max_batch_rows=5,\n        ),\n        mode = CreateMode.or_replace)\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can now test our function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E\n    f = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].functions["convert_udf(REAL)"].execute_function([12])\n    print(f)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd even update our table to populate the \u003Ccode\u003ETEMP_F\u003C/code\u003E field using our Service Function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E    \n    connection_container_user_role.cursor().execute("""UPDATE WEATHER\n                    SET TEMP_F = convert_udf(TEMP_C);""")\n\n    for (col1, col2, col3, col4) in connection_container_user_role.cursor().execute("SELECT * FROM WEATHER;"):\n        print('{0} {1} {2} {3}'.format(col1, col2, col3, col4))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E(Optional) Call the Convert Temperature Service Function from our Jupyter Notebook Service\u003C/h3\u003E\n","\u003Cp\u003EOpen up our previously created Jupyter Notebook service and open up our \u003Ccode\u003Esample_notebook.ipynb\u003C/code\u003E. Copy and paste the following code into a new cell at the bottom of the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edf = session.table('weather')\ndf = df.with_column("TEMP_F_SNOWPARK", F.call_udf('convert_udf', df['TEMP_C']))\ndf.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the cell, and you should see the following output dataframe, with our new column \u003Ccode\u003ETEMP_F_SNOWPARK\u003C/code\u003E included:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E----------------------------------------------------------------------\n|"DATE"      |"LOCATION"   |"TEMP_C"  |"TEMP_F"  |"TEMP_F_SNOWPARK"  |\n----------------------------------------------------------------------\n|2023-03-21  |London       |15        |59        |59.0               |\n|2023-07-13  |Manchester   |20        |68        |68.0               |\n|2023-05-09  |Liverpool    |17        |63        |62.6               |\n|2023-09-17  |Cambridge    |19        |66        |66.2               |\n|2023-11-02  |Oxford       |13        |55        |55.4               |\n|2023-01-25  |Birmingham   |11        |52        |51.8               |\n|2023-08-30  |Newcastle    |21        |70        |69.8               |\n|2023-06-15  |Bristol      |16        |61        |60.8               |\n|2023-04-07  |Leeds        |18        |64        |64.4               |\n|2023-10-23  |Southampton  |12        |54        |53.6               |\n----------------------------------------------------------------------\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, save the Jupyter notebook- when you come back to this service in the future, your new code will be saved because of our stage-backed volume mount!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EManaging Services with Python API\u003C/h2\u003E\n","\u003Cp\u003EThere are a number of useful Python API we should explore with respect to controlling the service itself.  More information on Python API can be found at \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-overview\"\u003ESnowpark Container Services Python API\u003C/a\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EGet the status of your container using ServiceResource.get_service_status():\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003Es = root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].services["JUPYTER_SNOWPARK_SERVICE"]\ns.get_service_status()\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECheck the status of the logs with :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003Es.get_service_logs("0","jupyter-snowpark",10)\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESuspend your container using the Python API\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003Es.suspend()\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EResume your container using the Python API\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003Es.resume()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStop the Services and Suspend the Compute Pool\u003C/h2\u003E\n","\u003Cp\u003EIf you no longer need the services and compute pool up and running, we can stop the services and suspend the compute pool so that we don't incur any cost (Snowpark Container Services bill credits/second based on the compute pool's uptime, similar to Virtual Warehouse billing) run the following from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/05_stop_snowpark_container_services_and_suspend_compute_pool.py\"\u003E\u003Ccode\u003E05_stop_snowpark_container_services_and_suspend_compute_pool.py\u003C/code\u003E\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL STOP ALL;\n    root.compute_pools["CONTAINER_HOL_POOL"].stop_all_services()\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL SUSPEND;\n    root.compute_pools["CONTAINER_HOL_POOL"].suspend()\n\nfinally:\n    connection_container_user_role.close()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you want to clean up and remove ALL of the objects you created during this quickstart, run the following from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/04_teardown.py\"\u003E\u003Ccode\u003E04_cleanup.py\u003C/code\u003E\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Python\"\u003E# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL STOP ALL;\n    root.compute_pools["CONTAINER_HOL_POOL"].stop_all_services()\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL SUSPEND;\n    root.compute_pools["CONTAINER_HOL_POOL"].suspend()\n\n    # DROP SERVICE CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE;\n    root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].services["JUPYTER_SNOWPARK_SERVICE"].delete()\n\n    # DROP SERVICE CONTAINER_HOL_DB.PUBLIC.CONVERT_API;\n    root.databases["CONTAINER_HOL_DB"].schemas["PUBLIC"].services["CONVERT_API"].delete()\n\n    # DROP COMPUTE POOL CONTAINER_HOL_POOL;\n    root.compute_pools["CONTAINER_HOL_POOL"].delete()\n\n    # DROP DATABASE CONTAINER_HOL_DB;\n    root.databases["CONTAINER_HOL_DB"].delete()\n\n    # DROP WAREHOUSE CONTAINER_HOL_WH;\n    root.warehouses["CONTAINER_HOL_WH"].delete()\n\n    # create a SnowflakeConnection instance\n    connection_acct_admin = connect(**CONNECTION_PARAMETERS_ACCOUNT_ADMIN)\n\n    # create a root as the entry point for all object\n    root = Root(connection_acct_admin)\n\n    try:\n        # DROP ROLE CONTAINER_USER_ROLE;\n        root.roles["CONTAINER_USER_ROLE"].delete()\n    finally:\n        connection_acct_admin.close()\n\nfinally:\n    connection_container_user_role.close()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations, you have successfully completed this quickstart! Through this quickstart, we were able to create and manage long-running services using Snowflake's managed Snowpark Container Services. These services run entirely within your Snowflake account boundary using a managed container orchestration service- keeping your data secure, and making development to deployment incredibly easy.\u003C/p\u003E\n","\u003Cp\u003EFor more information, check out the resources below:\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/commands-snowpark-container-services\"\u003ESnowpark Container Services SQL Commands\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/snowpark-container-services-a-tech-primer-99ff2ca8e741\"\u003ESnowpark Container Services - A Tech Primer\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.youtube.com/watch?v=DelaJBm0UgI\"\u003EBuilding Advanced ML with Snowpark Container Services - Summit 2023\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.youtube.com/watch?v=u98YTgCelYg\"\u003ESnowpark Container Services with NVIDIA\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services\"\u003EQuickstart GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/blog/snowpark-container-services-deploy-genai-full-stack-apps/\"\u003ESnowflake Announces Snowpark Container Services\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->"],"title":"Intro to Snowpark Container Services with Snowflake Python APIs","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ -----------------------------------\u003E\n## Overview \n\nThrough this quickstart guide, you will explore [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview), which are now in Public Preview on AWS. You will explore Snowpark Container Services using [Python API](https://docs.snowflake.com/en/developer-guide/snowflake-python-api/snowflake-python-overview), which is now in Public Preview. You will learn the basic mechanics of working with Snowpark Container Services using Python API and build several introductory services. **Please note: this quickstart assumes some existing knowledge and familiarity with containerization (e.g. Docker) , basic familiarity with container orchestration. and basic familiartity with Python**\n\n### What is Snowpark Container Services?\n\n![Snowpark Container Service](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/containers.png)\n\n[Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview) is a fully managed container offering that allows you to easily deploy, manage, and scale containerized services, jobs, and functions, all within the security and governance boundaries of Snowflake, and requiring zero data movement. As a fully managed service, SPCS comes with Snowflake’s native security, RBAC support, and built-in configuration and operational best-practices.\n\nSnowpark Container Services are fully integrated with both Snowflake features and third-party tools, such as Snowflake Virtual Warehouses and Docker, allowing teams to focus on building data applications, and not building or managing infrastructure. Just like all things Snowflake, this managed service allows you to run and scale your container workloads across regions and clouds without the additional complexity of managing a control plane, worker nodes, and also while having quick and easy access to your Snowflake data.\n\nThe introduction of Snowpark Container Services on Snowflake includes the incorporation of new object types and constructs to the Snowflake platform, namely: images, image registry, image repositories, compute pools, specification files, services, and jobs.\n\n![Development to Deployment](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/spcs_dev_to_deploy.png)\n\nFor more information on these objects, check out [this article](https://medium.com/snowflake/snowpark-container-services-a-tech-primer-99ff2ca8e741) along with the Snowpark Container Services [documentation](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview).\n\nHow are customers and partners using Snowpark Container Services today? Containerized services on Snowflake open up the opportunity to host and run long-running services, like front-end web applications, all natively within your Snowflake environment. Customers are now running GPU-enabled machine learning and AI workloads, such as GPU-accelerated model training and open-source Large Language Models (LLMs) as jobs and as service functions, including fine-tuning of these LLMs on your own Snowflake data, without having to move the data to external compute infrastructure. Snowpark Container Services are an excellent path for deploying applications and services that are tightly coupled to the Data Cloud.\n\nNote that in this quickstart, we will predominantly use the Python API to interact with Snowpark Container Services and their associated objects. Please refer to the [Python API support](https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-overview) in Public Preview. Refer to the [documentation](https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-overview) for more info.\n\n### What you will learn \n- The basic mechanics of how Snowpark Container Services works\n- How to deploy a long-running service with a UI and use volume mounts to persist changes in the file system\n- How to interact with Snowflake warehouses to run SQL queries from inside of a service\n- How to deploy a Service Function to perform basic calculations\n\n### Prerequisites\n##### **Download the git repo here: https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services.git**. \nYou can simply download the repo as a .zip if you don't have Git installed locally.\n\n- [Docker Desktop](https://www.docker.com/products/docker-desktop/) installed\n- [Python 3.10](https://www.python.org/downloads/) installed\n    - Note that you will be creating a Python environment with 3.10 in the **Setup the Local Environment** step\n- (Optional) [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) installed\n    \u003E**Download the git repo here: https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services.git**. You can simply download the repo as a .zip if you don't have Git installed locally.\n- (Optional) [VSCode](https://code.visualstudio.com/) (recommended) with the [Docker](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-docker), [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python), and [Snowflake](https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc) extensions installed.\n- A non-trial Snowflake account in a supported [AWS region](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview#available-regions).\n- A Snowflake account login with a role that has the `ACCOUNTADMIN` role. If not, you will need to work with your `ACCOUNTADMIN` to perform the initial account setup (e.g. creating the `CONTAINER_USER_ROLE` and granting required privileges, as well as creating the OAuth Security Integration).\n- Install Python API [Python API Installtion](https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-installing)\n\n### What You’ll Build \n- A hosted Jupyter Notebook service running inside of Snowpark Container Services with a basic notebook\n- A Python REST API to perform basic temperature conversions\n- A Snowflake Service Function that leverages the REST API\n\n\u003C!-- ![e2e_ml_workflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/snowpark-ml-e2e.png) --\u003E\n\n\u003C!-- ------------------------ --\u003E\n## Set up the Snowflake environment\n\nRun the following Python API code in [`00_setup.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/00_setup.py) using the Snowpark Python Connector and Python API to create the role, database, warehouse, and stage that we need to get started:\n```Python API\n# create a SnowflakeConnection instance\nconnection_acct_admin = connect(**CONNECTION_PARAMETERS_ACCOUNT_ADMIN)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_acct_admin)\n\n    # CREATE ROLE CONTAINER_USER_ROLE\n    root.roles.create(Role(\n        name='CONTAINER_USER_ROLE',\n        comment='My role to use container',\n    ))\n\n    # GRANT CREATE DATABASE ON ACCOUNT TO ROLE CONTAINER_USER_ROLE\n    # GRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    # GRANT CREATE COMPUTE POOL ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    # GRANT CREATE INTEGRATION ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    # GRANT MONITOR USAGE ON ACCOUNT TO  ROLE  CONTAINER_USER_ROLE;\n    # GRANT BIND SERVICE ENDPOINT ON ACCOUNT TO ROLE CONTAINER_USER_ROLE;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('CONTAINER_USER_ROLE'),\n        securable=Securables.current_account,\n        privileges=[Privileges.create_database,\n                    Privileges.create_warehouse,\n                    Privileges.create_compute_pool,\n                    Privileges.create_integration,\n                    Privileges.monitor_usage,\n                    Privileges.bind_service_endpoint\n                    ],\n    ))\n\n    # GRANT IMPORTED PRIVILEGES ON DATABASE snowflake TO ROLE CONTAINER_USER_ROLE;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('CONTAINER_USER_ROLE'),\n        securable=Securables.database('snowflake'),\n        privileges=[Privileges.imported_privileges\n                    ],\n    ))\n\n    # grant role CONTAINER_USER_ROLE to role ACCOUNTADMIN;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('ACCOUNTADMIN'),\n        securable=Securables.role('CONTAINER_USER_ROLE'),\n    ))\n\n    # USE ROLE CONTAINER_USE_ROLE\n    root.session.use_role(\"CONTAINER_USE_ROLE\")\n\n    # CREATE OR REPLACE DATABASE CONTAINER_HOL_DB;\n    root.databases.create(Database(\n        name=\"CONTAINER_HOL_DB\",\n        comment=\"This is a Container Quick Start Guide database\"\n    ), mode=CreateMode.or_replace)\n\n    # CREATE OR REPLACE WAREHOUSE CONTAINER_HOL_WH\n    #   WAREHOUSE_SIZE = XSMALL\n    #   AUTO_SUSPEND = 120\n    #   AUTO_RESUME = TRUE;\n    root.warehouses.create(Warehouse(\n        name=\"CONTAINER_HOL_WH\",\n        warehouse_size=\"XSMALL\",\n        auto_suspend=120,\n        auto_resume=\"true\",\n        comment=\"This is a Container Quick Start Guide warehouse\"\n    ), mode=CreateMode.or_replace)\n\n    # CREATE STAGE IF NOT EXISTS specs\n    # ENCRYPTION = (TYPE='SNOWFLAKE_SSE');\n    root.schemas[CONNECTION_PARAMETERS_ACCOUNT_ADMIN.get(\"schema\")].stages.create(\n        Stage(\n            name=\"specs\",\n            encryption=StageEncryption(type=\"SNOWFLAKE_SSE\")\n    ))\n\n    # CREATE STAGE IF NOT EXISTS volumes\n    # ENCRYPTION = (TYPE='SNOWFLAKE_SSE')\n    # DIRECTORY = (ENABLE = TRUE);\n    root.schemas[CONNECTION_PARAMETERS_ACCOUNT_ADMIN.get(\"schema\")].stages.create(\n        Stage(\n            name=\"volumes\",\n            encryption=StageEncryption(type=\"SNOWFLAKE_SSE\"),\n            directory_table=StageDirectoryTable(enable=\"true\")\n    ))\n    # create collection objects as the entry\nfinally:\n    connection_acct_admin.close()\n\n```\n\nRun the following Python API code in [`01_snowpark_container_services_setup.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/01_snowpark_container_services_setup.py) using the Snowpark Python Connector and Python API to create the [External Access Integration](https://docs.snowflake.com/developer-guide/snowpark-container-services/additional-considerations-services-jobs#network-egress) \n```Python API\n\n# create a SnowflakeConnection instance\nconnection_acct_admin = connect(**CONNECTION_PARAMETERS_ACCOUNT_ADMIN)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_acct_admin)\n\n    connection_acct_admin.cursor().execute(\"\"\"CREATE OR REPLACE NETWORK RULE ALLOW_ALL_RULE\n        TYPE = 'HOST_PORT'\n        MODE = 'EGRESS'\n        VALUE_LIST= ('0.0.0.0:443', '0.0.0.0:80');\"\"\")\n\n    connection_acct_admin.cursor().execute(\"\"\"CREATE EXTERNAL ACCESS INTEGRATION ALLOW_ALL_EAI\n        ALLOWED_NETWORK_RULES = (ALLOW_ALL_RULE)\n        ENABLED = true;\"\"\")\n\n    # GRANT USAGE ON INTEGRATION ALLOW_ALL_EAI TO ROLE CONTAINER_USER_ROLE;\n    root.grants.grant(Grant(\n        grantee=Grantees.role('CONTAINER_USER_ROLE'),\n        securable=Securables.integration(\"ALLOW_ALL_EAI\"),\n        privileges=[Privileges.Usage]\n    ))\n\nfinally:\n        connection_acct_admin.close()\n```\n\nRun the following Python API code in [`01_snowpark_container_services_setup.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/01_snowpark_container_services_setup.py) using the Snowpark Python Connector and Python API to create\nour first [compute pool](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool), and our [image repository](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-registry-repository)\n```Python API\n    # USE ROLE CONTAINER_USE_ROLE\n    root.session.use_role(\"CONTAINER_USE_ROLE\")\n\n    # CREATE COMPUTE POOL IF NOT EXISTS CONTAINER_HOL_POOL\n    # MIN_NODES = 1\n    # MAX_NODES = 1\n    # INSTANCE_FAMILY = standard_1;\n    root.compute_pools.create(ComputePool(\n      name=\"CONTAINER_HOL_POOL\",\n      min_nodes=1,\n      max_nodes=1,\n      instance_family=\"CPU_X64_XS\",\n    ))\n\n    # CREATE IMAGE REPOSITORY CONTAINER_HOL_DB.PUBLIC.IMAGE_REPO;\n    root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].image_repositories.create(ImageRepository(\n      name=\"IMAGE_REPO\",\n    ))\n\n    # SHOW IMAGE REPOSITORIES IN SCHEMA CONTAINER_HOL_DB.PUBLIC;\n    itr_data = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].image_repositories.iter()\n    for image_repo in itr_data:\n        print(image_repo)\n```\n- The [OAuth security integration](https://docs.snowflake.com/en/user-guide/oauth-custom#create-a-snowflake-oauth-integration) will allow us to login to our UI-based services using our web browser and Snowflake credentials\n- The [External Access Integration](https://docs.snowflake.com/developer-guide/snowpark-container-services/additional-considerations-services-jobs#network-egress) will allow our services to reach outside of Snowflake to the public internet\n- The [compute pool](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool) is the set of compute resources on which our services will run\n- The [image repository](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-registry-repository) is the location in Snowflake where we will push our Docker images so that our services can use them\n\n\u003C!-- ------------------------ --\u003E\n## Set up your local environment\n\n### Python Virtual Environment and SnowCLI\n\n- Download and install the miniconda installer from [https://conda.io/miniconda.html](https://conda.io/miniconda.html). (OR, you may use any other Python environment with Python 3.10, for example, [virtualenv](https://virtualenv.pypa.io/en/latest/)).\n\n- Open a new terminal window, navigate to your Git repo, and execute the following commands in the same terminal window:\n\n  1. Create the conda environment.\n  ```\n  conda env create -f conda_env.yml\n  ```\n\n  2. Activate the conda environment.\n  ```\n  conda activate snowpark-container-services-hol\n  ```\n\n  3. Configure your Snowflake CLI connection. The Snowflake CLI is designed to make managing UDFs, stored procedures, and other developer centric configuration files easier and more intuitive to manage. Let's create a new connection using:\n  ```bash\n  snow connection add\n  ```\n  ```yaml\n  # follow the wizard prompts to set the following values:\n  name : CONTAINER_hol\n  account name: \u003CORG\u003E-\u003CACCOUNT-NAME\u003E # e.g. MYORGANIZATION-MYACCOUNT\n  username : \u003Cuser_name\u003E\n  password : \u003Cpassword\u003E  \n  role: CONTAINER_USER_ROLE  \n  warehouse : CONTAINER_HOL_WH\n  Database : CONTAINER_HOL_DB  \n  Schema : public  \n  connection :   \n  port : \n  Region :  \n  ```\n  ```bash\n  # test the connection:\n  snow connection test --connection \"CONTAINER_hol\"\n  ```\n\n  6. Start docker via opening Docker Desktop.\n  \n  7. Test that we can successfully login to the image repository we created above, `CONTAINER_HOL_DB.PUBLIC.IMAGE_REPO`. Run the following using Python API code [`06_docker_jupyter_service.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/06_docker_jupyter_service.py) :\n  ```Python API\n  # Connect as CONTAINER_USE_ROLE\n    connection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\n    try:\n\n        root = Root(connection_container_user_role)\n\n        # Get the image repository URL\n        #   use role CONTAINER_user_role;\n        #   show image repositories in schema CONTAINER_HOL_DB.PUBLIC;\n        #   // COPY the repository_url field, e.g. org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo\n        #   ```\n        #   ```bash\n        #   # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo, snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n        repos = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].image_repositories\n        repo = repos[\"IMAGE_REPO\"].fetch()\n\n        # Extract the registry hostname from the repository URL\n        pattern = r'^[^/]+'\n\n        repository_url = repo.repository_url\n        match = re.match(pattern, repository_url)\n        registry_hostname = match.group(0)\n\n        # Docker client\n        client = docker.from_env()\n\n        #   docker login \u003Csnowflake_registry_hostname\u003E -u \u003Cuser_name\u003E\n        #   \u003E prompt for password\n        # Login to the remote registry. Give user name and password for docker login\n        client.login(username = \"\u003Cusername\u003E\",\n                            password = \"\u003Cpassword\u003E\",\n                            registry = registry_hostname,\n                            reauth = True)\n    finally:\n        connection_container_user_role.close()\n  ```\n  **Note the difference between `REPOSITORY_URL` (`org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo`) and `SNOWFLAKE_REGISTRY_HOSTNAME` (`org-account.registry.snowflakecomputing.com`)**\n\n\u003C!-- ------------------------ --\u003E\n## Build, Push, and Run the Jupyter Service\n\n### Build and Test the Image Locally\n\nThe first service we are going to create is a hosted Jupyter notebook service. First, we will build and test the image locally. In the code repo, there is a [`./src/jupyter-snowpark/dockerfile`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/src/jupyter-snowpark/dockerfile) with the following contents:\n```python\nFROM python:3.9\nLABEL author=\"\"\n\n#Install packages & python base\nRUN apt-get update && \\\n    apt-get install -y python3-pip\n\nRUN pip3 install JPype1 jupyter pandas numpy seaborn scipy matplotlib seaborn pyNetLogo SALib \"snowflake-snowpark-python[pandas]\" snowflake-connector-python\n\n#Create a new user for the notebook server , NB RUN instrcution are only ever executed during the buid\nRUN useradd -ms /bin/bash jupyter   \n\n#set the user and working directory \nUSER jupyter\nWORKDIR /home/jupyter \n\n#other system settings\nEXPOSE 8888   \n\n#launch jupyter notebook server. NOTE!  ENTRYPOINT ( or CMD )intrscutions run each time a container is launched!\nENTRYPOINT [\"jupyter\", \"notebook\",\"--allow-root\",\"--ip=0.0.0.0\",\"--port=8888\",\"--no-browser\" , \"--NotebookApp.token=''\", \"--NotebookApp.password=''\"] \n```\nThis is just a normal Dockerfile, where we install some packages, change our working directory, expose a port, and then launch our notebook service. There's nothing unique to Snowpark Container Services here! \n\nLet's build and test the image locally from the terminal. **Note it is a best practice to tag your local images with a `local_repository` prefix.** Often, users will set this to a combination of first initial and last name, e.g. `jsmith/my-image:latest`. Navigate to your local clone of `.../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark` and run a Docker build command using Python code:\n```bash\n\n# Build the Docker Image in the Example\n# cd .../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark\n# docker build --platform=linux/amd64 -t \u003Clocal_repository\u003E/python-jupyter-snowpark:latest .\nclient.images.build(path='sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark', platform='linux/aarch64', tag='\u003Clocal_repository\u003E/python-jupyter-snowpark:latest')\n\n```\nVerify the image built successfully:\n```bash\n# Check to see if the image is there\n# Verify the image built successfully:\n# docker image list\nclient.images.list()\n```\nNow that our local image has built, let's validate that it runs successfully. From a terminal run:\n```bash\n# Test running the image\n# docker run -d -p 8888:8888 \u003Clocal_repository\u003E/python-jupyter-snowpark:latest\ncontainer = client.containers.run(image='\u003Clocal_repository\u003E/python-jupyter-snowpark:latest', detach=True, ports={8888: 8888})\n\n# Use CURL to test the service\n# Open up a browser and navigate to [localhost:8888/lab](http://localhost:8888/lab) to verify \n# your notebook service is working locally. Once you've verified that the service is working,\n# you can stop the container:\nos.system(\"\"\"curl -X GET  http://localhost:8888/lab\"\"\")\n\n# docker stop python-jupyter-snowpark\ncontainer.stop()\n```\n\n### Tag and Push the Image\nNow that we have a local version of our container working, we need to push it to Snowflake so that a Service can access the image. To do this we will create a new tag of the image that points at our image repository in our Snowflake account, and then push said tagged image. From a terminal, run the following:\n```bash\n    # Tag it\n    #  # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo, \n    #  snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n    #   docker login \u003Csnowflake_registry_hostname\u003E -u \u003Cuser_name\u003E\n    #   \u003E prompt for password\n    #   docker tag \u003Clocal_repository\u003E/python-jupyter-snowpark:latest \u003Crepository_url\u003E/python-jupyter-snowpark:dev\n        # Grab the image\n    image = next(i for i in client.images.list() if \"\u003Clocal_repository\u003E/python-jupyter-snowpark:latest\" in i.tags)\n    image.tag(repository_url, 'dev')\n```\n  **Note the difference** between `REPOSITORY_URL` (`org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo`) and `SNOWFLAKE_REGISTRY_HOSTNAME` (`org-account.registry.snowflakecomputing.com`)\n\nVerify that the new tagged image exists by running:\n```bash\n    # Check to see if the image is there\n    # Verify the image built successfully:\n    # docker image list\n    client.images.list()\n```\nNow, we need to push our image to Snowflake. From the terminal:\n```bash\n    # Push the image to the remote registry\n    # docker push \u003Crepository_url\u003E/python-jupyter-snowpark:dev\n    client.api.push(repository_url + '/python-jupyter-snowpark:dev')\n```\nThis may take some time, so you can move on to the next step **Configure and Push the Spec YAML** while the image is being pushed. Once the `docker push` command completes, you can verify that the image exists in your Snowflake Image Repository by running the following Python API code:\n```Python API\n    # USE ROLE CONTAINER_USER_ROLE;\n    # CALL SYSTEM$REGISTRY_LIST_IMAGES('/CONTAINER_HOL_DB/PUBLIC/IMAGE_REPO');\n    images = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].image_repositories[\"IMAGE_REPO\"].list_images_in_repository()\n    for image in images:\n        print(image)\n```\nYou should see your `python-jupyter-snowpark` image listed.\n\n### Configure and Push the Spec YAML\nServices in Snowpark Container Services are defined using YAML files. These YAML files configure all of the various parameters, etc. needed to run the containers within your Snowflake account. These YAMLs support a [large number of configurable parameter](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference), although we will not reference all of them here. Navigate to your local clone of `.../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark/jupyter-snowpark.yaml`, which should look like this:\n\n```yaml\nspec:\n  containers:\n    - name: jupyter-snowpark\n      image: \u003Crepository_hostname\u003E/container_hol_db/public/image_repo/python-jupyter-snowpark:dev\n      volumeMounts:\n        - name: jupyter-home\n          mountPath: /home/jupyter\n  endpoints:\n    - name: jupyter-snowpark\n      port: 8888\n      public: true\n  volumes:\n    - name: jupyter-home\n      source: \"@volumes/jupyter-snowpark\"\n      uid: 1000\n      gid: 1000\n\n```\n\n**NOTE**: Update **\u003Crepository_hostname\u003E** for your image and save the file.\n\nNow that the spec file is updated, we need to push it to our Snowflake Stage so that we can reference it next in our `create service` statement. We will use Python API to push the yaml file. Run the following using Python API code [`08_stage_files.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/08_stage_files.py) :\n\n```Python API\n    # cd .../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark\n    # snow stage copy ./jupyter-snowpark.yaml @specs --overwrite --connection CONTAINER_hol\n    s = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].stages[\"SPECS\"]\n    s.upload_file(\"./jupyter-snowpark.yaml\", \"/\", auto_compress=False, overwrite=True)\n```\nYou can verify that your yaml was pushed successfully by running the following Python API code and verifying that the file is listed:\n```Python API\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n\n    root = Root(connection_container_user_role)\n\n    #USE ROLE CONTAINER_USER_ROLE;\n    #LS @CONTAINER_HOL_DB.PUBLIC.SPECS;\n    stageFiles = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].stages[\"SPECS\"].list_files()\n    for stageFile in stageFiles:\n        print(stageFile)\n\nfinally:\n    connection_container_user_role.close()\n\n```\n\n### Create and Test the Service\nOnce we have successfully pushed our image and our spec YAML, we have all of the components uploaded to Snowflake that we need in order to create our service. There are three components required to create the service: a service name, a compute pool the service will run on, and the spec file that defines the service. Run the following Python script to create our Jupyter service:\n```Python API\n\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # create service CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE\n    # in compute pool CONTAINER_HOL_POOL\n    # from @specs\n    # specification_file='jupyter-snowpark.yaml'\n    # external_access_integrations = (ALLOW_ALL_EAI);\n    s = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].services.create(Service(\n        name=\"JUPYTER_SNOWPARK_SERVICE\",\n        compute_pool=\"CONTAINER_HOL_POOL\",\n        spec=ServiceSpecStageFile(stage=\"specs\", spec_file=\"jupyter-snowpark.yaml\"),\n        external_access_integrations=[\"ALLOW_ALL_EAI\"],\n    ))\n\n    # CALL SYSTEM$GET_SERVICE_STATUS('CONTAINER_HOL_DB.PUBLIC.jupyter_snowpark_service');\n    status = s.get_service_status()\n    print(status)\n\n    # CALL SYSTEM$GET_SERVICE_LOGS('CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE', '0', 'jupyter-snowpark',10);\n    logs = s.get_service_logs(\"0\", \"jupyter-snowpark\", 10)\n    print(logs)\n\n    # SHOW ENDPOINTS IN SERVICE JUPYTER_SNOWPARK_SERVICE;\n    endpoints = s.get_endpoints()\n    for endpoint in endpoints:\n        print(endpoint)\n\n    # --- After we make a change to our Jupyter notebook,\n    # --- we will suspend and resume the service\n    # --- and you can see that the changes we made in our Notebook are still there!\n    # ALTER SERVICE CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE SUSPEND;\n    s.suspend()\n\n    # ALTER SERVICE CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE RESUME;\n    s.resume()\n\nfinally:\n    connection_container_user_role.close()\n\n```\nThese script is the file [`02_jupyter_service.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/02_jupyter_service.py).\n\nSince we specified that the `jupyter-snowpark` endpoint running on port `8888` would be `public: true` in our spec YAML, Snowflake is going to generate a URL for our service that can be used to access the service via our Web Browser. To get the URL, once the service is successfully in a `RUNNING` state, execute the following:\n```Python API\n    # SHOW ENDPOINTS IN SERVICE JUPYTER_SNOWPARK_SERVICE;\n    endpoints = s.get_endpoints()\n    for endpoint in endpoints:\n        print(endpoint)\n```\nCopy the `jupyter-snowpark` endpoint URL, and paste it in your browser. You will be asked to login to Snowflake via your username and password, after which you should successfully see your Jupyter instance running, all inside of Snowflake! **Note, to access the service** the user logging in must have the `CONTAINER_USER_ROLE` AND their default role cannot be `ACCOUNTADMIN`, `SECURITYADMIN`, or `ORGADMIN`.\n\n### Upload and Modify a Jupyter Notebook\nNotice that in our spec YAML file we mounted the `@volumes/jupyter-snowpark` internal stage location to our `/home/jupyter` directory inside of our running container. What this means is that we will use our internal stage `@volumes` to persist and store artifacts from our container. If you go check out the `@volumes` stage in Snowsight, you'll see that when we created our `jupyter_snowpark_service`, a folder was created in our stage: `@volumes/jupyter-snowpark`\n\n![Stage Volume Mount](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/stage_volume_mount.png)\n\nNow, any file that is uploaded to `@volumes/jupyter-snowpark` will be available inside of our container in the `/home/jupyter` directory, and vice versa. Read more about volume mounts in the [documentation](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference#spec-volumes-field-optional). To test this out, let's upload the sample Jupyter notebook that is in our source code repo at `.../sfguide-intro-to-snowpark-container-services/src/jupyter-snowpark/sample_notebook.ipynb`. To do this you can either\n- Click on the `jupyter-snowpark` directory in Snowsight, click the blue `+ Files` button and drag/browse to `sample_notebook.ipynb`. Click Upload. Navigate to your Jupyter service UI in your browser, click the refresh arrow and you should now see your notebook available!\n\nOR\n- Upload `sample_notebook.ipynb` to `@volumes/jupyter-snowpark` using SnowCLI\n\nOR\n- Upload `sample_notebook.ipynb` directly in your Jupyter service  on the home screen by clicking the `Upload` button. If you now navigate back to `@volumes/jupyter-snowpark` in Snowsight, our run an `ls @volumes/jupyter-snowpark` SQL command, you should see  your `sample_notebook.ipynb` file listed. Note you may need to hit the Refresh icon in Snowsight for the file to appear.\n\nWhat we've done is now created a Jupyter notebook which we can modify in our service, and the changes will be persisted in the file because it is using a stage-backed volume. Let's take a look at the contents of our `sample_notebook.ipynb`. Open up the notebook in your Jupyter service:\n\n![Jupyter Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/intro-to-snowpark-container-services-with-python-api/jupyter.png)\n\nWe want to pay special attention to the contents of the `get_login_token()` function:\n```python\ndef get_login_token():\n    with open('/snowflake/session/token', 'r') as f:\n        return f.read()\n```\nWhen you start a service or a job, Snowflake provides credentials to the running containers in the form of an oauth token located at `/snowflake/session/token`, enabling your container code to use Snowflake connectors for [connecting to Snowflake](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/additional-considerations-services-jobs#connecting-to-snowflake-from-inside-a-container) and executing SQL (similar to any other code on your computer connecting to Snowflake). The provided credentials authenticate as the service role. Snowflake provides some of the information as environment variables in your containers.\n\nEvery object in Snowflake has an owner role. In the case of a service or job, Snowflake has a concept called a service role (this term applies to both services and jobs). The service role determines what capabilities your service is allowed to perform when interacting with Snowflake. This includes executing SQL, accessing stages, and service-to-service networking.\n\nWe configure the Snowpark Python Client to connect to our Snowflake account and execute SQL using this oauth token:\n```python\nconnection_parameters = {\n    \"account\": os.getenv('SNOWFLAKE_ACCOUNT'),\n    \"host\": os.getenv('SNOWFLAKE_HOST'),\n    \"token\": get_login_token(),\n    \"authenticator\": \"oauth\",\n    \"database\": \"CONTAINER_HOL_DB\",\n    \"schema\": \"PUBLIC\",\n    \"warehouse\": \"CONTAINER_HOL_WH\"\n}\n```\n\nNow we can run a sample query using our Snowpark session!\n\nWe've successfully built and deployed our Jupyter Notebook service. Now let's move on to a REST API which we will interact with using a Service Function.\n\u003C!-- ------------------------ --\u003E\n## Build, Push, and Run the Temperature Conversion REST API Service\n\n### Build and Test the Image Locally\n\nThe next service we are going to create is a simple REST API that takes in Celsius temperatures and converts them to Fahrenheit- a trivial example. First, we will build and test the image locally. In the code repo, there is a [`./src/convert-api/dockerfile`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/src/convert-api/dockerfile) with the following contents:\n```python\nFROM python:3.11\n\n# Copy the packages file into the build\nWORKDIR /app\nCOPY ./ /app/\n\n# run the install using the packages manifest file\nRUN pip install --no-cache-dir -r requirements.txt\n\n# Open port 9090\nEXPOSE 9090\n\n# When the container launches run the flask app\nENV FLASK_APP=\"convert-app.py\"\nCMD [\"flask\", \"run\", \"--host=0.0.0.0\", \"--port=9090\"]\n```\nThis is just a normal Dockerfile, where we install some packages, change our working directory, expose a port, and then launch our REST API. Our REST API is defined in [`./src/convert-api/convert-app.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/src/convert-api/convert-app.py):\n```python\nfrom flask import Flask, request, jsonify\n\napp = Flask(__name__)\n\n# The function to convert Celsius to Fahrenheit\ndef celsius_to_fahrenheit(celsius):\n    return celsius * 9./5 + 32\n\n@app.route('/convert', methods=['POST'])\ndef convert():\n    # Get JSON data from request\n    data = request.get_json()\n\n    # Check if the 'data' key exists in the received JSON\n    if 'data' not in data:\n        return jsonify({'error': 'Missing data key in request'}), 400\n\n    # Extract the 'data' list from the received JSON\n    data_list = data['data']\n\n    # Initialize a list to store converted values\n    converted_data = []\n\n    # Iterate over each item in 'data_list'\n    for item in data_list:\n        # Check if the item is a list with at least two elements\n        if not isinstance(item, list) or len(item) \u003C 2:\n            return jsonify({'error': 'Invalid data format'}), 400\n        \n        # Extract the Celsius value\n        celsius = item[1]\n        \n        # Convert to Fahrenheit and append to 'converted_data'\n        converted_data.append([item[0], celsius_to_fahrenheit(celsius)])\n\n    # Return the converted data as JSON\n    return jsonify({'data': converted_data})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n```\n\nThe only thing unique to Snowflake about this container, is that the REST API code expects to receive requests in the format that [Snowflake External Function](https://docs.snowflake.com/en/sql-reference/external-functions-data-format#body-format) calls are packaged, and must also package the response in the expected format so that we can use it as a Service Function. **Note this is only required if you intend to interact with the API via a SQL function**.\n\nLet's build and test the image locally from the terminal. **Note it is a best practice to tag your local images with a `local_repository` prefix. Often, users will set this to a combination of first initial and last name, e.g. `jsmith/my-image:latest`. Navigate to your local clone of `.../sfguide-intro-to-snowpark-container-services/src/convert-api` and run a Docker build command using Python. Run the following using Python API code [`07_docker_rest_service.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/07_docker_rest_service.py) :\n```bash\n    # Build the Docker Image in the Example\n    # cd .../sfguide-intro-to-snowpark-container-services/src/convert-api\n    # docker build --platform=linux/amd64 -t \u003Clocal_repository\u003E/convert-api:latest .\n    client.images.build(path='sfguide-intro-to-snowpark-container-services/src/convert-api', platform='linux/aarch64', tag='\u003Clocal_repository\u003E/convert-api:latest')\n```\nVerify the image built successfully:\n```bash\n    # Check to see if the image is there\n    # Verify the image built successfully:\n    # docker image list\n    client.images.list()\n```\nNow that our local image has built, let's validate that it runs successfully. From a terminal run:\n```bash\n    # Test running the image\n    # docker run -d -p 9090:9090 \u003Clocal_repository\u003E/convert-api:latest\n    container = client.containers.run(image='\u003Clocal_repository\u003E/convert-api:latest', detach=True, ports={9090: 9090})\n```\nTest our local container endpoint by running the following from a different terminal window:\n```bash\n    # Use CURL to test the service\n    # curl -X POST -H \"Content-Type: application/json\" -d '{\"data\": [[0, 12],[1,19],[2,18],[3,23]]}' http://localhost:9090/convert\n    \n    os.system(\"\"\"curl -X POST \n                -H \"Content-Type: application/json\" \n                -d '{\"data\": [[0, 12],[1,19],[2,18],[3,23]]}' \n                http://localhost:9090/convert \"\"\")\n\n```\nYou should recieve back a JSON object, this will contain the batch id and then the converted value in Fahrenheit:\n```bash\n{\"data\":[[0,53.6],[1,66.2],[2,64.4],[3,73.4]]}\n```\nOnce you've verified that the service is working, you can stop the container: `container.stop()`.\n\n### Tag and Push the Image\nNow that we have a local version of our container working, we need to push it to Snowflake so that a Service can access the image. To do this we will create a new tag of the image that points at our image repository in our Snowflake account, and then push said tagged image. From a terminal, run the following:\n```bash\n  # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo, snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n      #   docker login \u003Csnowflake_registry_hostname\u003E -u \u003Cuser_name\u003E\n    #   \u003E prompt for password\n    # Login to the remote registry. Give user name and password for docker login\n    client.login(username = \"\u003Cusername\u003E\",\n                        password = \"\u003Cpassword\u003E\",\n                        registry = registry_hostname,\n                        reauth = True)\n\n     # Grab the image\n    image = next(i for i in client.images.list() if \"\u003Clocal_repository\u003E/convert-api:latest\" in i.tags)\n\n    # Tag it\n    #  # e.g. if repository_url = org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo,\n    #  snowflake_registry_hostname = org-account.registry.snowflakecomputing.com\n    #   docker login \u003Csnowflake_registry_hostname\u003E -u \u003Cuser_name\u003E\n    #   \u003E prompt for password\n    #  docker tag \u003Clocal_repository\u003E/convert-api:latest \u003Crepository_url\u003E/convert-api:dev\n    image.tag(repository_url, 'dev')\n \n```\n**Note the difference between `REPOSITORY_URL` (`org-account.registry.snowflakecomputing.com/container_hol_db/public/image_repo`) and `SNOWFLAKE_REGISTRY_HOSTNAME` (`org-account.registry.snowflakecomputing.com`)**\n\nVerify that the new tagged image exists by running:\n```bash\n    # Check to see if the image is there\n    # Verify the image built successfully:\n    # docker image list\n    client.images.list()\n```\nNow, we need to push our image to Snowflake. From the terminal:\n```bash\n    # Push the image to the remote registry\n    # docker push \u003Crepository_url\u003E/convert-api:dev\n    client.api.push(repository_url + '/convert-api:dev')\n```\nThis may take some time, so you can move on to the next step **Configure and Push the Spec YAML** while the image is being pushed. Once the `docker push` command completes, you can verify that the image exists in your Snowflake Image Repository by running the following Python API code:\n```Python API\n    # USE ROLE CONTAINER_USER_ROLE;\n    # CALL SYSTEM$REGISTRY_LIST_IMAGES('/CONTAINER_HOL_DB/PUBLIC/IMAGE_REPO');\n    images = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].image_repositories[\"IMAGE_REPO\"].list_images_in_repository()\n    for image in images:\n        print(image)\n```\nYou should see your `convert-api` image listed.\n\n### Configure and Push the Spec YAML\nServices in Snowpark Container Services are defined using YAML files. These YAML files configure all of the various parameters, etc. needed to run the containers within your Snowflake account. These YAMLs support a [large number of configurable parameter](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference), although we will not reference all of them here. Navigate to your local clone of `.../sfguide-intro-to-snowpark-container-services/src/convert-api/convert-api.yaml`, which should look like this:\n\n```yaml\nspec:\n  containers:\n    - name: convert-api\n      image: \u003Crepository_hostname\u003E/container_hol_db/public/image_repo/convert-api:dev\n  endpoints:\n    - name: convert-api\n      port: 9090\n      public: true\n```\n\n**NOTE**: Update **\u003Crepository_hostname\u003E** for your image and save the file.\n\nNow that the spec file is updated, we need to push it to our Snowflake Stage so that we can reference it next in our `create service` statement. We will use Python API to push the yaml file. Run the following using Python API code [`08_stage_files.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/08_stage_files.py)\n```Python API\n    # cd .../sfguide-intro-to-snowpark-container-services/src/convert-api\n    # snow stage copy ./convert-api.yaml @specs --overwrite --connection CONTAINER_hol\n    s = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].stages[\"SPECS\"]\n    s.upload_file(\"./convert-api.yaml\", \"/\", auto_compress=False, overwrite=True)\n```\nYou can verify that your yaml was pushed successfully by running the Python code and verifying that the file is listed. Run the following using Python API code [`08_stage_files.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/08_stage_files.py) :\n```Python API\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n\n    root = Root(connection_container_user_role)\n\n    #USE ROLE CONTAINER_USER_ROLE;\n    #LS @CONTAINER_HOL_DB.PUBLIC.SPECS;\n    stageFiles = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].stages[\"SPECS\"].listFiles()\n    for stageFile in stageFiles:\n        print(stageFile)\n        \nfinally:\n    connection_container_user_role.close()\n\n```\n\n### Create and Test the Service\nOnce we have successfully pushed our image and our spec YAML, we have all of the components uploaded to Snowflake that we need in order to create our service. There are three components required to create the service: a service name, a compute pool the service will run on, and the spec file that defines the service. Run the following Python API script to create our Jupyter service:\n```Python API\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # create service CONTAINER_HOL_DB.PUBLIC.convert_api\n    #     in compute pool CONTAINER_HOL_POOL\n    #     from @specs\n    #     specification_file='convert-api.yaml'\n    #     external_access_integrations = (ALLOW_ALL_EAI);\n    s = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].services.create(Service(\n        name=\"convert_api\",\n        compute_pool=\"CONTAINER_HOL_POOL\",\n        spec=ServiceSpecStageFile(stage=\"specs\", spec_file=\"convert-api.yaml\"),\n        external_access_integrations=[\"ALLOW_ALL_EAI\"],\n    ))\n\n    # CALL SYSTEM$GET_SERVICE_STATUS('CONTAINER_HOL_DB.PUBLIC.CONVERT-API');\n    status = s.get_service_status()\n    print(status)\n\n    # CALL SYSTEM$GET_SERVICE_LOGS('CONTAINER_HOL_DB.PUBLIC.CONVERT_API', '0', 'convert-api',10);\n    logs = s.get_service_logs(\"0\", \"convert-api\", 10)\n    print(logs)\n\nfinally:\n    connection_container_user_role.close()\n\n```\nThese commands are also listed in [`03_rest_service.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/03_rest_service.py)\n\n### Create and Test the Service Function\nOnce the service is up and running, we will create a Service Function that allows us to call our REST API's function via Python Connector. First, let's create a table and then create a Service function:\n```Python API\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # CREATE OR REPLACE TABLE WEATHER (\n    #     DATE DATE,\n    #     LOCATION VARCHAR,\n    #     TEMP_C NUMBER,\n    #     TEMP_F NUMBER\n    # );\n    root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].tables.create(\n        Table(\n            name=\"WEATHER\",\n            columns=[\n                    TableColumn(name=\"DATE\", datatype=\"DATE\"),\n                    TableColumn(name=\"LOCATION\", datatype=\"VARCHAR\"),\n                    TableColumn(name=\"TEMP_C\", datatype=\"NUMBER\"),\n                    TableColumn(name=\"TEMP_F\", datatype=\"NUMBER\"),\n                ],\n        ),\n        mode=CreateMode.or_replace\n    )\n\nfinally:\n    connection_container_user_role.close()\n\n```\n\nNow, let's insert some sample weather data in the table using Python Connector:\nThese commands are also listed in [`03_rest_service.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/03_rest_service.py)\n``` Python Connector\n    \n    # Connect as CONTAINER_USE_ROLE\n    connection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\n    connection_container_user_role.cursor().execute(\"\"\"INSERT INTO weather (DATE, LOCATION, TEMP_C, TEMP_F)\n                        VALUES \n                            ('2023-03-21', 'London', 15, NULL),\n                            ('2023-07-13', 'Manchester', 20, NULL),\n                            ('2023-05-09', 'Liverpool', 17, NULL),\n                            ('2023-09-17', 'Cambridge', 19, NULL),\n                            ('2023-11-02', 'Oxford', 13, NULL),\n                            ('2023-01-25', 'Birmingham', 11, NULL),\n                            ('2023-08-30', 'Newcastle', 21, NULL),\n                            ('2023-06-15', 'Bristol', 16, NULL),\n                            ('2023-04-07', 'Leeds', 18, NULL),\n                            ('2023-10-23', 'Southampton', 12, NULL);\"\"\")\n\nfinally:\n    connection_container_user_role.close()\n\n```\nNow, let's create a function that specifies our `convert-api` service's `convert-api` endpoint:\n```Python API\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # CREATE OR REPLACE FUNCTION convert_udf (input float)\n    # RETURNS float\n    # SERVICE=CONVERT_API      //Snowflake container service\n    # ENDPOINT='convert-api'   //The endpoint within the container\n    # MAX_BATCH_ROWS=5         //limit the size of the batch\n    # AS '/convert';           //The API endpoint\n    root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].functions.create(\n        ServiceFunction(\n        name=\"convert_udf\",\n        arguments=[\n            FunctionArgument(name=\"input\", datatype=\"REAL\")\n        ],\n        returns=\"REAL\",\n        service=\"CONVERT_API\",\n        endpoint=\"'convert-api'\",\n        path=\"/convert\",\n        max_batch_rows=5,\n        ),\n        mode = CreateMode.or_replace)\n\nfinally:\n    connection_container_user_role.close()\n\n```\nWe can now test our function:\n```Python Connector\n\n    f = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].functions[\"convert_udf(REAL)\"].execute_function([12])\n    print(f)\n\n```\nAnd even update our table to populate the `TEMP_F` field using our Service Function:\n```Python Connector\n    \n    connection_container_user_role.cursor().execute(\"\"\"UPDATE WEATHER\n                    SET TEMP_F = convert_udf(TEMP_C);\"\"\")\n\n    for (col1, col2, col3, col4) in connection_container_user_role.cursor().execute(\"SELECT * FROM WEATHER;\"):\n        print('{0} {1} {2} {3}'.format(col1, col2, col3, col4))\n\n```\n\n### (Optional) Call the Convert Temperature Service Function from our Jupyter Notebook Service\nOpen up our previously created Jupyter Notebook service and open up our `sample_notebook.ipynb`. Copy and paste the following code into a new cell at the bottom of the notebook:\n```python\ndf = session.table('weather')\ndf = df.with_column(\"TEMP_F_SNOWPARK\", F.call_udf('convert_udf', df['TEMP_C']))\ndf.show()\n```\n\nRun the cell, and you should see the following output dataframe, with our new column `TEMP_F_SNOWPARK` included:\n```python\n----------------------------------------------------------------------\n|\"DATE\"      |\"LOCATION\"   |\"TEMP_C\"  |\"TEMP_F\"  |\"TEMP_F_SNOWPARK\"  |\n----------------------------------------------------------------------\n|2023-03-21  |London       |15        |59        |59.0               |\n|2023-07-13  |Manchester   |20        |68        |68.0               |\n|2023-05-09  |Liverpool    |17        |63        |62.6               |\n|2023-09-17  |Cambridge    |19        |66        |66.2               |\n|2023-11-02  |Oxford       |13        |55        |55.4               |\n|2023-01-25  |Birmingham   |11        |52        |51.8               |\n|2023-08-30  |Newcastle    |21        |70        |69.8               |\n|2023-06-15  |Bristol      |16        |61        |60.8               |\n|2023-04-07  |Leeds        |18        |64        |64.4               |\n|2023-10-23  |Southampton  |12        |54        |53.6               |\n----------------------------------------------------------------------\n```\n\nNow, save the Jupyter notebook- when you come back to this service in the future, your new code will be saved because of our stage-backed volume mount!\n\u003C!-- ------------------------ --\u003E\n## Managing Services with Python API\n\nThere are a number of useful Python API we should explore with respect to controlling the service itself.  More information on Python API can be found at [Snowpark Container Services Python API](https://docs.snowflake.com/developer-guide/snowflake-python-api/snowflake-python-overview) \n\n1. Get the status of your container using ServiceResource.get_service_status():\n\n    ```Python API\n    s = root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].services[\"JUPYTER_SNOWPARK_SERVICE\"]\n    s.get_service_status()\n    ```\n\n2. Check the status of the logs with :\n\n    ```Python API\n    s.get_service_logs(\"0\",\"jupyter-snowpark\",10)\n    ```\n\n3. Suspend your container using the Python API\n\n    ```Python API\n    s.suspend()\n    ```\n\n4. Resume your container using the Python API\n\n  ```Python API\n  s.resume()\n  ```\n\n\u003C!-- ------------------------ --\u003E\n## Stop the Services and Suspend the Compute Pool\n\nIf you no longer need the services and compute pool up and running, we can stop the services and suspend the compute pool so that we don't incur any cost (Snowpark Container Services bill credits/second based on the compute pool's uptime, similar to Virtual Warehouse billing) run the following from [`05_stop_snowpark_container_services_and_suspend_compute_pool.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/05_stop_snowpark_container_services_and_suspend_compute_pool.py):\n```Python API\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL STOP ALL;\n    root.compute_pools[\"CONTAINER_HOL_POOL\"].stop_all_services()\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL SUSPEND;\n    root.compute_pools[\"CONTAINER_HOL_POOL\"].suspend()\n\nfinally:\n    connection_container_user_role.close()\n\n```\n\nIf you want to clean up and remove ALL of the objects you created during this quickstart, run the following from [`04_cleanup.py`](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services/blob/main/04_teardown.py):\n```Python API\n# Connect as CONTAINER_USE_ROLE\nconnection_container_user_role = connect(**CONNECTION_PARAMETERS_CONTAINER_USER_ROLE)\n\ntry:\n    # create a root as the entry point for all object\n    root = Root(connection_container_user_role)\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL STOP ALL;\n    root.compute_pools[\"CONTAINER_HOL_POOL\"].stop_all_services()\n\n    # ALTER COMPUTE POOL CONTAINER_HOL_POOL SUSPEND;\n    root.compute_pools[\"CONTAINER_HOL_POOL\"].suspend()\n\n    # DROP SERVICE CONTAINER_HOL_DB.PUBLIC.JUPYTER_SNOWPARK_SERVICE;\n    root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].services[\"JUPYTER_SNOWPARK_SERVICE\"].delete()\n\n    # DROP SERVICE CONTAINER_HOL_DB.PUBLIC.CONVERT_API;\n    root.databases[\"CONTAINER_HOL_DB\"].schemas[\"PUBLIC\"].services[\"CONVERT_API\"].delete()\n\n    # DROP COMPUTE POOL CONTAINER_HOL_POOL;\n    root.compute_pools[\"CONTAINER_HOL_POOL\"].delete()\n\n    # DROP DATABASE CONTAINER_HOL_DB;\n    root.databases[\"CONTAINER_HOL_DB\"].delete()\n\n    # DROP WAREHOUSE CONTAINER_HOL_WH;\n    root.warehouses[\"CONTAINER_HOL_WH\"].delete()\n\n    # create a SnowflakeConnection instance\n    connection_acct_admin = connect(**CONNECTION_PARAMETERS_ACCOUNT_ADMIN)\n\n    # create a root as the entry point for all object\n    root = Root(connection_acct_admin)\n\n    try:\n        # DROP ROLE CONTAINER_USER_ROLE;\n        root.roles[\"CONTAINER_USER_ROLE\"].delete()\n    finally:\n        connection_acct_admin.close()\n\nfinally:\n    connection_container_user_role.close()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\nCongratulations, you have successfully completed this quickstart! Through this quickstart, we were able to create and manage long-running services using Snowflake's managed Snowpark Container Services. These services run entirely within your Snowflake account boundary using a managed container orchestration service- keeping your data secure, and making development to deployment incredibly easy.\n\nFor more information, check out the resources below:\n\n### Related Resources\n- [Snowpark Container Services Documentation](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview)\n- [Snowpark Container Services SQL Commands](https://docs.snowflake.com/en/sql-reference/commands-snowpark-container-services)\n- [Snowpark Container Services - A Tech Primer](https://medium.com/snowflake/snowpark-container-services-a-tech-primer-99ff2ca8e741)\n- [Building Advanced ML with Snowpark Container Services - Summit 2023](https://www.youtube.com/watch?v=DelaJBm0UgI)\n- [Snowpark Container Services with NVIDIA](https://www.youtube.com/watch?v=u98YTgCelYg)\n- [Quickstart GitHub](https://github.com/Snowflake-Labs/sfguide-intro-to-snowpark-container-services)\n- [Snowflake Announces Snowpark Container Services](/blog/snowpark-container-services-deploy-genai-full-stack-apps/)\n\n\u003C!-- ------------------------ --\u003E\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-3fd1264ff2","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-8becfc71df",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-059edf05d4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-d2fb9df2a9","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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