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guide will provide step-by-step details for using multi-node multi-gpu audio transcription with snowflake ml container runtime","title":"Distributed Multi-Node and Multi GPU Audio Transcription with Snowflake ML Container Runtime","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EIn this Quickstart guide, we will walk through how to use \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml\"\u003EContainer Runtime\u003C/a\u003E in \u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E to perform Multi-Node, Multi-GPU audio transcription at limitless scale over multiple audio files.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowflake ML?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/data-cloud/snowflake-ml/\"\u003ESnowflake ML\u003C/a\u003E is the integrated set of capabilities for end-to-end machine learning in a single platform on top of your governed data. Data scientists and ML engineers can easily and securely develop and productionize scalable features and models without any data movement, silos or governance tradeoffs.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Container Runtime?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml\"\u003EContainer Runtime\u003C/a\u003E gives you a flexible container infrastructure that supports building and operationalizing a wide variety of resource-intensive ML workflows entirely within Snowflake.\u003C/p\u003E\n","\u003Cp\u003EContainer Runtime is a set of preconfigured customizable environments built for machine learning on Snowpark Container Services, covering interactive experimentation and batch ML workloads such as model training, hyperparameter tuning, batch inference and fine tuning. They include the most popular machine learning and deep learning frameworks. Container Runtime also provides flexibility the ability to pip install any open-source package of choice.\u003C/p\u003E\n","\u003Cp\u003EKey Features:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EManaged Environment: Focus on your ML projects without the overhead of managing underlying infrastructure.\u003C/li\u003E\u003Cli\u003EScalability: Leverage distributed computing resources to efficiently handle large datasets and complex computations.\u003C/li\u003E\u003Cli\u003EIntegration: Seamlessly combine with Snowflake's ML operations for a cohesive workflow.\u003C/li\u003E\u003Cli\u003EFlexibility: While common ML packages come pre-installed, you have the option to install custom packages as needed.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/container_runtime_for_ml.png\" alt=\"ContainerRuntime For ML\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowflake Notebooks on Container Runtime?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-on-spcs\"\u003ESnowflake Notebooks on Container Runtime\u003C/a\u003E  are a powerful IDE option for building ML models at scale in \u003Ca href=\"/en/data-cloud/snowflake-ml/\"\u003ESnowflake ML\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ESnowflake Notebooks are natively built into Snowsight, and provide everything you need for interactive development, cell by cell execution of Python, Markdown and SQL code. By using Snowflake Notebooks one can increase the productivity since it simplifies connecting to the data and using popular OSS libraries for ML use cases. Notebooks on Container Runtime offer a robust environment with a comprehensive repository of pre-installed CPU and GPU machine learning packages and frameworks, significantly reducing the need for package management and dependency troubleshooting. This allows you to quickly get started with your preferred frameworks and even import models from external sources. Additionally, you can use pip to install any custom package as needed. The runtime also features an optimized data ingestion layer and provides a set of powerful APIs for training and hyperparameter tuning. These APIs extend popular ML packages, enabling you to train models efficiently within Snowflake. At the core of this solution is a Ray-powered distributed compute cluster, giving you seamless access to both CPU and GPU resources. This ensures high performance and optimal infrastructure usage without the need for complex setup or configuration, allowing you to focus solely on your machine learning workloads.\u003C/p\u003E\n","\u003Ch3\u003EWhat are Snowflake ML Jobs?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-ml/ml-jobs/overview\"\u003ESnowflake ML Jobs\u003C/a\u003E is a framework which lets you leverage the Container Runtime\nfrom any environment. You can use the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/jobs\"\u003EML Jobs SDK\u003C/a\u003E to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESubmit and manage jobs for execution using \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ELeverage GPU and high-memory CPU instances for resource-intensive tasks\u003C/li\u003E\u003Cli\u003EUse your preferred development environment (VS Code, external notebooks, etc.)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhether you're looking to productionize your ML workflows or prefer working in\nyour own development environment, Snowflake ML Jobs provides the same powerful\ncapabilities available in Snowflake Notebooks in a more flexible,\nintegration-friendly format.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Container Runtime’s architecture?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/container_runtime_architecture.png\" alt=\"ContainerRuntime Architecture\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat APIs does Container Runtime have to support Unstructured Data Processing?\u003C/h3\u003E\n","\u003Cp\u003EContainer Runtime for ML provides several APIs to handle unstructured data, such as images, text and binary files, from Snowflake Stage and process it in Snowflake's Container Runtime using \u003Ca href=\"https://docs.ray.io/en/latest/data/data.html\"\u003ERay Data\u003C/a\u003E. These APIs enable efficient unstructured data processing and analysis without the need for complex data movement or transformation. At the time of writing this quickstart, Container Runtime for ML has the following APIs for handling unstructured data:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReading into Snowflake:\n\u003Col\u003E\u003Cli\u003ESFStageImageDatasource - This class allows us to load images from Snowflake Stage and pass them forward for distributed processing with Container Runtime for ML.\u003C/li\u003E\u003Cli\u003ESFStageTextDatasource - This class allows us to load text from Snowflake Stage and use them in downstream distributed calls.\u003C/li\u003E\u003Cli\u003ESFStageBinaryFileDataSource - This class allows us to read content from Snowflake stage as binary files and use them in downstream processors.\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003Cli\u003EWriting to Snowflake:\n\u003Col\u003E\u003Cli\u003ESnowflakeTableDatasink - This class provides snowflake specific \u003Ca href=\"https://docs.ray.io/en/latest/data/api/doc/ray.data.Datasink.html\"\u003ERay data sink APIs\u003C/a\u003E to allow customers write the processed dataset into a snowflake table\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIn this quickstart, we will be using SFStageBinaryFileDataSource and SnowflakeTableDatasink class.\u003C/p\u003E\n","\u003Ch3\u003EWhat model are we using for audio transcription?\u003C/h3\u003E\n","\u003Cp\u003EWhisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper \u003Ca href=\"https://huggingface.co/papers/2212.04356\"\u003ERobust Speech Recognition via Large-Scale Weak Supervision\u003C/a\u003E by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting.\u003C/p\u003E\n","\u003Cp\u003EWhisper large-v3 has the same architecture as the previous \u003Ca href=\"https://huggingface.co/openai/whisper-large\"\u003Elarge\u003C/a\u003E and \u003Ca href=\"https://huggingface.co/openai/whisper-large-v2\"\u003Elarge-v2\u003C/a\u003E models, except for the following minor differences:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EThe spectrogram input uses 128 Mel frequency bins instead of 80\u003C/li\u003E\u003Cli\u003EA new language token for Cantonese\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ESee details here for the \u003Ca href=\"https://huggingface.co/openai/whisper-large-v3\"\u003Emodel\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/multi-node_multi-gpu_inferencing.png\" alt=\"ContainerRuntime Multi Node Multi GPU Inferencing\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA Snowflake Account. Sign up for a 30-day \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Efree trial account\u003C/a\u003E, if required.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFoundational knowledge of Data Science workflows\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E(Optional) Completed \u003Ca href=\"/en/developers/guides/notebook-container-runtime/\"\u003EGetting Started with Snowflake Notebook Container Runtime\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E(Optional) Completed \u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/ml/ml_jobs#getting-started\"\u003EGetting Started with Snowflake ML Jobs\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n","\u003Cp\u003EThis guide will show you:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Snowflake ML Container Runtime to build a distributed, multi-node, multi-GPU solution for audio transcription\u003C/li\u003E\u003Cli\u003EHow to leverage powerful, pre-configured environments with popular ML frameworks, scale your workloads across multiple nodes and GPUs for faster processing\u003C/li\u003E\u003Cli\u003EHow to use Snowflake's APIs to seamlessly handle unstructured data directly from your internal stages\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Notebook that runs Distributed Multi-Node, Multi-GPU Audio Transcription in ML Container Runtime\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup Environment\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003EThis section will walk you through creating various objects. The repository with the source code can be found \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EInitial Setup\u003C/h3\u003E\n","\u003Cp\u003EComplete the following steps to setup your account:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload the Snowflake Objects creation file “step0_setup.sql”  from this \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/blob/main/step0_setup.sql\"\u003Elink\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERoles required to execute commands in file are SYSADMIN and ACCOUNTADMIN.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn Snowsight navigate to Worksheets, click "+" in the top-right corner to create a new Worksheet, and choose "SQL Worksheet".\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the contents from the downloaded file “step0_setup.sql”  in the worksheet\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun all the commands to create Snowflake objects (database, schema, warehouse, compute pools and external access integration).\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EAudio Processing Setup\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003EThis notebook linked below covers the creation of snowflake objects and data loading from a third-party dataset (Audio Files) into snowflake stage. \u003Cstrong\u003EBe sure to comply with the dataset's licensing terms and usage guidelines.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ETo get started, follow these steps:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload the notebook “Audio Processing - Setup.ipynb” from this \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/blob/main/Audio%20Processing%20-%20Setup.ipynb\"\u003Elink\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to Snowsight and change role to \u003Cstrong\u003ESYSADMIN\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003ENotebooks\u003C/strong\u003E in Snowsight\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the top right, click on \u003Cstrong\u003ENotebook\u003C/strong\u003E down arrow and select \u003Cstrong\u003EImport .ipynb\u003C/strong\u003E file from the dropdown menu\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate a new notebook “Audio Processing - Setup.ipynb” with the following settings\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFor the Notebook Location, select MULTINODE_MULTIGPU_MYDB database and AUDIO_TRANSCRIPTION_SCH schema\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect Warehouse – ML_MODEL_WH\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPython Environment - Run On Container\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERuntime - Snowflake ML Runtime CPU 1.0\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECompute Pool - AUDIO_PROCESSING_CP_DATA_DOWNLOAD\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003ECreate Button\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Setup_create_notebook.png\" alt=\"Audio Processing Setup\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick the three dots in the top right > Notebook Settings\u003C/li\u003E\u003Cli\u003EEnable the ALLOW_ALL_INTEGRATION and click SAVE.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Setup_notebook_settings_external_access.png\" alt=\"Audio Processing Setup External Access\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ERun cells in the notebook!\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENotebook will download the audio files from LibriSpeech ASR corpus as noted here: \u003Ca href=\"https://www.openslr.org/resources/12\"\u003Ehttps://www.openslr.org/resources/12\u003C/a\u003E. The notebook also puts these audio files in a snowflake internal stage named AUDIO_FILES_STAGE.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_notebook.png\" alt=\"Audio Processing Setup Notebook\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENote: - "Please note, if you duplicate this notebook you will have to manually enable ALLOW_ALL_INTEGRATION again"\u003C/p\u003E\n","\u003Ch2\u003EAudio Transcription\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003EThe samples shown below demonstrate the distributed inferencing of audio files on Snowflake ML Container Runtime using multiple nodes and multiple GPUs.\u003C/p\u003E\n","\u003Cp\u003EIn this sample, we will be using SFStageBinaryFileDataSource and SnowflakeTableDatasink class. The signature of these two classes are as below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Eclass SFStageBinaryFileDataSource(\n    stage_location: str,\n    database: Optional[str] = None,\n    schema: Optional[str] = None,\n    file_pattern: Optional[str] = None,\n    local_path: Optional[str] = None,\n)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Estage_location\u003C/code\u003E: Stage path\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Edatabase\u003C/code\u003E: Database name. Defaults to notebook session's current database\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eschema\u003C/code\u003E: Schema name. Defaults to notebook session's current database\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Efile_pattern\u003C/code\u003E: The file pattern to filter the files in the stage. It supports Unix shell-style wildcards. Example: "*.flac" will match all files with .png extension Default is to include all files under stage_location\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Elocal_path\u003C/code\u003E: The local path to save the files if you choose to. Defaults to None. None means the file will not be saved to the local disk and the consumption will be through the content file_binary. Note, in a multi-node cluster, this is not recommended, since files will be only available in the main node.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Efrom snowflake.ml.ray.datasink import SnowflakeTableDatasink\n\ndatasink = SnowflakeTableDatasink(\n    table_name="MY_TABLE",\n    database = "MY_DB",\n    schema = "MY_SCHEMA",\n    auto_create_table=True,\n    override=True,\n)\n\n# Write a processed dataset back to a snowflake table\n# For example take the Text API processing example above\nlabel_dataset.write_datasink(datasink, concurrency=4)\n# Now you can query the table in `MY_DB.MY_SCHEMA.MY_TABLE'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAudio Processing and Distributed Inferencing Notebook\u003C/h3\u003E\n","\u003Cp\u003ETo get started, follow these steps:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload the notebook “Audio Processing - Distributed Inferencing.ipynb” from this \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/blob/main/Audio%20Processing%20-%20Distributed%20Inferencing.ipynb\"\u003Elink\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to Snowsight and change role to the \u003Cstrong\u003ESYSADMIN\u003C/strong\u003E role\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003ENotebooks\u003C/strong\u003E in Snowsight\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the top right, click on \u003Cstrong\u003ENotebook\u003C/strong\u003E down arrow and select \u003Cstrong\u003EImport .ipynb\u003C/strong\u003E file from the dropdown menu\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate a new notebook with the following settings\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EFor the Notebook Location, select MULTINODE_MULTIGPU_MYDB database and AUDIO_TRANSCRIPTION_SCH schema\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect Warehouse – ML_MODEL_WH\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPython Environment - Run On Container\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERuntime - Snowflake ML Runtime GPU 1.0\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECompute Pool - AUDIO_PROCESSING_CP_GPU_NV_S_5_NODES\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003ECreate Button\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Distributed_Inferencing_create_notebook.png\" alt=\"Audio Processing Distributed Inferencing Notebook\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick the three dots in the top right > Notebook Settings\u003C/li\u003E\u003Cli\u003EEnable the ALLOW_ALL_INTEGRATION and click SAVE.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Distributed_Inferencing_notebook_settings_external_access.png\" alt=\"Audio Processing Distributed Inferencing Notebook External Access\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ERun cells in the notebook!\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ELets break down step by step in the notebook\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a Ray cluster and scale upto 5 nodes.\u003C/li\u003E\u003Cli\u003EConfigure logging for Ray Cluster and do necessary setups for the Ray Cluster\u003C/li\u003E\u003Cli\u003EVerify the Audio files in the stage uploaded during Step 3.\u003C/li\u003E\u003Cli\u003EDownload the whisper model from OpenAI for distributed inferencing of the audio files\u003C/li\u003E\u003Cli\u003ERun distributed inference on the multi-node, multi-GPU audio transcription\u003C/li\u003E\u003Cli\u003EWrite data to the Snowflake table using Snowflake APIs.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENote: "Please note, if you duplicate this notebook you will have to manually enable ALLOW_ALL_INTEGRATION again"\u003C/p\u003E\n","\u003Ch3\u003EAudio Processing and Distributed Inferencing ML Job\u003C/h3\u003E\n","\u003Cp\u003EYou can also leverage Snowflake ML Container Runtime outside of Snowflake Notebooks using ML Jobs. You can run an equivalent workload as an ML Job by following these steps:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EInstall Python 3\u003C/a\u003E in your local environment if needed.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EInstall the ML Job SDK, which is included in \u003Ccode\u003Esnowflake-ml-python\u003C/code\u003E: \u003Ccode\u003Epip install snowflake-ml-python>=1.9.2\u003C/code\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections\"\u003EConfigure a default Snowflake connection\u003C/a\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ETip: You can generate a config for your account from Snowsight using the \u003Ccode\u003EConnect a tool to Snowflake\u003C/code\u003E > \u003Ccode\u003EConfig File\u003C/code\u003E menu items.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/setup_connect_tool.png\" alt=\"\"\u003E \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/setup_config_file.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload the files \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py\"\u003Etranscribe_audio.py\u003C/a\u003E and \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/run_audio_transcription_job.py\"\u003Erun_audio_transcription_job.py\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EInspect the file \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py\"\u003Etranscribe_audio.py\u003C/a\u003E. This is the payload script we will submit and execute inside the Container Runtime using ML Jobs.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun \u003Ccode\u003Epython run_audio_transcription_job.py\u003C/code\u003E in your local environment. This will upload \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py\"\u003Etranscribe_audio.py\u003C/a\u003E and kick off its execution using the \u003Ccode\u003Esubmit_file()\u003C/code\u003E API from the ML Job SDK.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou can monitor the job execution in the Services and Jobs UI in Snowsight\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/services_and_jobs_ui.png\" alt=\"Services and Jobs UI\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's break down what the ML Job workflow is doing:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a Snowflake session from default configuration and set up database context\u003C/li\u003E\u003Cli\u003ESubmit the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py\"\u003Etranscribe_audio.py\u003C/a\u003E script as an ML Job targeting 5 instances with a minimum of 1, scaling elastically based on available compute pool capacity\u003C/li\u003E\u003Cli\u003EConfigure logging for Ray Cluster and do necessary setups for the Ray Cluster\u003C/li\u003E\u003Cli\u003ELoad audio files from Snowflake stage using SFStageBinaryFileDataSource with file pattern filtering\u003C/li\u003E\u003Cli\u003EDownload and initialize the Whisper large-v3 model from OpenAI for distributed inferencing\u003C/li\u003E\u003Cli\u003ERun distributed inference on multi-node, multi-GPU setup using Ray's map_batches with AudioTranscriber actor\u003C/li\u003E\u003Cli\u003EWrite transcribed results to Snowflake table using SnowflakeTableDatasink\u003C/li\u003E\u003Cli\u003EMonitor job execution status and retrieve logs for verification\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EValue Proposition\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003ESnowflake ML Container Runtime offers significant value for distributed multi-node, multi-GPU audio transcription. Here are the key benefits:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeveraging Powerful Models\u003C/strong\u003E: Utilizes state-of-the-art models like Whisper for accurate audio transcription.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGPU Acceleration\u003C/strong\u003E: Supports GPU machine types for faster processing, demonstrated by the example using 5 GPU_NV_S nodes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECost-Effectiveness\u003C/strong\u003E: The demo processed approximately 2700 audio files in just 2 minutes using 5 Small GPU nodes on AWS (specifically GPU_NV_S compute family), each costing 0.57 credits/hour. The cost is derived from the Credit Consumption Table 1 (d) highlighted \u003Ca href=\"/legal-files/CreditConsumptionTable.pdf\"\u003Ehere\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMinimal Credit Consumption\u003C/strong\u003E: This resulted in a very low cost of only (2/60) * 0.57 = 0.019 credits per node, so 0.095 credits for 5 nodes or $0.285 to transcribe 2700 audio files (based on $3 per credit).\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESpeed\u003C/strong\u003E: Rapid transcription of a large number of files, significantly reducing processing time.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScalability\u003C/strong\u003E: Handles large datasets and complex computations efficiently using distributed computing resources.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EManaged Environment\u003C/strong\u003E: Eliminates the overhead of managing underlying infrastructure, allowing users to focus on ML projects.\nIntegration: Seamlessly combines with Snowflake's ML operations for a cohesive workflow.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFlexibility\u003C/strong\u003E: Pre-installed common ML packages with the option to install custom packages as needed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUnstructured Data Processing\u003C/strong\u003E: APIs like SFStageBinaryFileDataSource and SnowflakeTableDatasink enable efficient handling of audio files from Snowflake Stage.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EIn conclusion, running Container Runtime in Snowflake ML and Snowflake Notebooks Container Runtime offers a robust and flexible infrastructure for managing large-scale, advanced data science and machine learning workflows directly within Snowflake. With the ability to install external packages and choose optimal compute resources, including GPU machine types, Container Runtime provides a more versatile environment suited to the needs of data science and ML teams.\u003C/p\u003E\n","\u003Cp\u003EReady for more? After you complete this quickstart, you can try one of the following more advanced quickstarts:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/end-to-end-ml-workflow/\"\u003EBuild an End-to-End ML Workflow in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/scale-embeddings-with-snowflake-notebooks-on-container-runtime/\"\u003EScale Embeddings with Snowflake Notebooks on Container Runtime\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-running-distributed-pytorch-models-on-snowflake/\"\u003EGetting Started with Running Distributed PyTorch Models on Snowflake\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/defect-detection-using-distributed-pytorch-with-snowflake-notebooks/\"\u003EDefect Detection Using Distributed PyTorch With Snowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe key features of Snowflake Notebooks with Container Runtime in \u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E to perform Multi-Node, Multi-GPU audio transcription at limitless scale over multiple audio files.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Quickstarts\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/train-an-xgboost-model-with-gpus-using-snowflake-notebooks/\"\u003ETrain an XGBoost model with GPUs in Snowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/defect-detection-using-distributed-pytorch-with-snowflake-notebooks/\"\u003EDefect Detection Using Distributed PyTorch With Snowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/scale-embeddings-with-snowflake-notebooks-on-container-runtime/\"\u003EScale Embeddings with Snowflake Notebooks on Container Runtime\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-running-distributed-pytorch-models-on-snowflake/\"\u003EGetting Started with Running Distributed PyTorch Models on Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/intro-to-machine-learning-with-snowpark-ml-for-python/\"\u003EGetting Started with Snowflake ML\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml\"\u003EDocumentation: Container Runtime for ML\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003EDocumentation: Snowflake Model Registry\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container\"\u003EDocumentation: Model Serving in SPCS\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/notebook-container-runtime/\"\u003EIntro Quickstart: Getting Started with Snowflake Notebook Container Runtime\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/data-cloud/snowflake-ml/\"\u003ESnowflake ML Webpage\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/LIMITEDACCESS/snowsight-notebooks/ui-snowsight-notebooks-runtime\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n\n\nIn this Quickstart guide, we will walk through how to use [Container Runtime](https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml) in [Snowflake ML](http://www.snowflake.com/ml) to perform Multi-Node, Multi-GPU audio transcription at limitless scale over multiple audio files.\n\n### What is Snowflake ML?\n\n[Snowflake ML](/en/data-cloud/snowflake-ml/) is the integrated set of capabilities for end-to-end machine learning in a single platform on top of your governed data. Data scientists and ML engineers can easily and securely develop and productionize scalable features and models without any data movement, silos or governance tradeoffs.\n\n### What is Container Runtime?\n\n[Container Runtime](https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml) gives you a flexible container infrastructure that supports building and operationalizing a wide variety of resource-intensive ML workflows entirely within Snowflake. \n\nContainer Runtime is a set of preconfigured customizable environments built for machine learning on Snowpark Container Services, covering interactive experimentation and batch ML workloads such as model training, hyperparameter tuning, batch inference and fine tuning. They include the most popular machine learning and deep learning frameworks. Container Runtime also provides flexibility the ability to pip install any open-source package of choice.\n\nKey Features:\n\n* Managed Environment: Focus on your ML projects without the overhead of managing underlying infrastructure.  \n* Scalability: Leverage distributed computing resources to efficiently handle large datasets and complex computations.  \n* Integration: Seamlessly combine with Snowflake's ML operations for a cohesive workflow.  \n* Flexibility: While common ML packages come pre-installed, you have the option to install custom packages as needed.\n\n![ContainerRuntime For ML](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/container_runtime_for_ml.png)\n\n### What is Snowflake Notebooks on Container Runtime?\n\n[Snowflake Notebooks on Container Runtime](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-on-spcs)  are a powerful IDE option for building ML models at scale in [Snowflake ML](/en/data-cloud/snowflake-ml/). \n\nSnowflake Notebooks are natively built into Snowsight, and provide everything you need for interactive development, cell by cell execution of Python, Markdown and SQL code. By using Snowflake Notebooks one can increase the productivity since it simplifies connecting to the data and using popular OSS libraries for ML use cases. Notebooks on Container Runtime offer a robust environment with a comprehensive repository of pre-installed CPU and GPU machine learning packages and frameworks, significantly reducing the need for package management and dependency troubleshooting. This allows you to quickly get started with your preferred frameworks and even import models from external sources. Additionally, you can use pip to install any custom package as needed. The runtime also features an optimized data ingestion layer and provides a set of powerful APIs for training and hyperparameter tuning. These APIs extend popular ML packages, enabling you to train models efficiently within Snowflake. At the core of this solution is a Ray-powered distributed compute cluster, giving you seamless access to both CPU and GPU resources. This ensures high performance and optimal infrastructure usage without the need for complex setup or configuration, allowing you to focus solely on your machine learning workloads.\n\n### What are Snowflake ML Jobs?\n\n[Snowflake ML Jobs](https://docs.snowflake.com/developer-guide/snowflake-ml/ml-jobs/overview) is a framework which lets you leverage the Container Runtime\nfrom any environment. You can use the [ML Jobs SDK](https://docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/jobs) to:\n\n* Submit and manage jobs for execution using [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview)\n* Leverage GPU and high-memory CPU instances for resource-intensive tasks\n* Use your preferred development environment (VS Code, external notebooks, etc.)\n\nWhether you're looking to productionize your ML workflows or prefer working in\nyour own development environment, Snowflake ML Jobs provides the same powerful\ncapabilities available in Snowflake Notebooks in a more flexible,\nintegration-friendly format.\n\n### What is Container Runtime’s architecture?\n\n![ContainerRuntime Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/container_runtime_architecture.png)\n\n### What APIs does Container Runtime have to support Unstructured Data Processing?\n\nContainer Runtime for ML provides several APIs to handle unstructured data, such as images, text and binary files, from Snowflake Stage and process it in Snowflake's Container Runtime using [Ray Data](https://docs.ray.io/en/latest/data/data.html). These APIs enable efficient unstructured data processing and analysis without the need for complex data movement or transformation. At the time of writing this quickstart, Container Runtime for ML has the following APIs for handling unstructured data:\n\n1. Reading into Snowflake:   \n   1. SFStageImageDatasource \\- This class allows us to load images from Snowflake Stage and pass them forward for distributed processing with Container Runtime for ML.  \n   2. SFStageTextDatasource \\- This class allows us to load text from Snowflake Stage and use them in downstream distributed calls.  \n   3. SFStageBinaryFileDataSource \\- This class allows us to read content from Snowflake stage as binary files and use them in downstream processors.  \n2. Writing to Snowflake:   \n   1. SnowflakeTableDatasink \\- This class provides snowflake specific [Ray data sink APIs](https://docs.ray.io/en/latest/data/api/doc/ray.data.Datasink.html) to allow customers write the processed dataset into a snowflake table\n\nIn this quickstart, we will be using SFStageBinaryFileDataSource and SnowflakeTableDatasink class.\n\n### What model are we using for audio transcription?\n\nWhisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://huggingface.co/papers/2212.04356) by Alec Radford et al. from OpenAI. Trained on \\\u003E5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting.\n\nWhisper large-v3 has the same architecture as the previous [large](https://huggingface.co/openai/whisper-large) and [large-v2](https://huggingface.co/openai/whisper-large-v2) models, except for the following minor differences:\n\n1. The spectrogram input uses 128 Mel frequency bins instead of 80  \n2. A new language token for Cantonese\n\nSee details here for the [model](https://huggingface.co/openai/whisper-large-v3)\n\n![ContainerRuntime Multi Node Multi GPU Inferencing](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/multi-node_multi-gpu_inferencing.png)\n\n### Prerequisites\n\n* A Snowflake Account. Sign up for a 30-day [free trial account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides), if required.\n\n* Foundational knowledge of Data Science workflows\n\n* (Optional) Completed [Getting Started with Snowflake Notebook Container Runtime](/en/developers/guides/notebook-container-runtime/)\n\n* (Optional) Completed [Getting Started with Snowflake ML Jobs](https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/ml/ml_jobs#getting-started)\n\n### What You Will Learn\n\nThis guide will show you:\n- How to use Snowflake ML Container Runtime to build a distributed, multi-node, multi-GPU solution for audio transcription\n- How to leverage powerful, pre-configured environments with popular ML frameworks, scale your workloads across multiple nodes and GPUs for faster processing\n- How to use Snowflake's APIs to seamlessly handle unstructured data directly from your internal stages\n\n### What You Will Build\n\n* A Snowflake Notebook that runs Distributed Multi-Node, Multi-GPU Audio Transcription in ML Container Runtime\n\n## Setup Environment\n\u003C!-- ------------------------ --\u003E\n\n\nThis section will walk you through creating various objects. The repository with the source code can be found [here](https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime).\n\n### Initial Setup\n\nComplete the following steps to setup your account:\n\n* Download the Snowflake Objects creation file “step0\\_setup.sql”  from this [link](https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/blob/main/step0_setup.sql). \n\n* Roles required to execute commands in file are SYSADMIN and ACCOUNTADMIN.\n\n* In Snowsight navigate to Worksheets, click \"+\" in the top-right corner to create a new Worksheet, and choose \"SQL Worksheet\".\n\n* Paste the contents from the downloaded file “step0\\_setup.sql”  in the worksheet\n\n* Run all the commands to create Snowflake objects (database, schema, warehouse, compute pools and external access integration).\n\n\n## Audio Processing Setup\n\u003C!-- ------------------------ --\u003E\n\n\nThis notebook linked below covers the creation of snowflake objects and data loading from a third-party dataset (Audio Files) into snowflake stage. **Be sure to comply with the dataset's licensing terms and usage guidelines.**\n\nTo get started, follow these steps: \n\n* Download the notebook “Audio Processing \\- Setup.ipynb” from this [link](https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/blob/main/Audio%20Processing%20-%20Setup.ipynb)\n\n* Navigate to Snowsight and change role to **SYSADMIN**\n\n* Navigate to **Projects** \\\u003E **Notebooks** in Snowsight\n\n* On the top right, click on **Notebook** down arrow and select **Import .ipynb** file from the dropdown menu\n\n* Create a new notebook “Audio Processing \\- Setup.ipynb” with the following settings\n\n * For the Notebook Location, select MULTINODE\\_MULTIGPU\\_MYDB database and AUDIO\\_TRANSCRIPTION\\_SCH schema\n\n  * Select Warehouse – ML\\_MODEL\\_WH\n\n  * Python Environment \\- Run On Container\n\n  * Runtime \\- Snowflake ML Runtime CPU 1.0\n\n  * Compute Pool \\- AUDIO\\_PROCESSING\\_CP\\_DATA\\_DOWNLOAD\n\n  * Click on **Create Button**\n\n![Audio Processing Setup](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Setup_create_notebook.png)\n\n* Click the three dots in the top right \\\u003E Notebook Settings  \n* Enable the ALLOW\\_ALL\\_INTEGRATION and click SAVE.\n\n![Audio Processing Setup External Access](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Setup_notebook_settings_external_access.png)\n\n* Run cells in the notebook\\!\n\n* Notebook will download the audio files from LibriSpeech ASR corpus as noted here: [https://www.openslr.org/resources/12](https://www.openslr.org/resources/12). The notebook also puts these audio files in a snowflake internal stage named AUDIO\\_FILES\\_STAGE.\n\n![Audio Processing Setup Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_notebook.png)\n\n\nNote: - \"Please note, if you duplicate this notebook you will have to manually enable ALLOW_ALL_INTEGRATION again\"\n\n## Audio Transcription\n\u003C!-- ------------------------ --\u003E\n\n\nThe samples shown below demonstrate the distributed inferencing of audio files on Snowflake ML Container Runtime using multiple nodes and multiple GPUs.\n\nIn this sample, we will be using SFStageBinaryFileDataSource and SnowflakeTableDatasink class. The signature of these two classes are as below:\n\n```py\nclass SFStageBinaryFileDataSource(\n    stage_location: str,\n    database: Optional[str] = None,\n    schema: Optional[str] = None,\n    file_pattern: Optional[str] = None,\n    local_path: Optional[str] = None,\n)\n```\n\n* `stage_location`: Stage path  \n* `database`: Database name. Defaults to notebook session's current database   \n* `schema`: Schema name. Defaults to notebook session's current database   \n* `file_pattern`: The file pattern to filter the files in the stage. It supports Unix shell-style wildcards. Example: \"\\*.flac\" will match all files with .png extension Default is to include all files under stage\\_location  \n* `local_path`: The local path to save the files if you choose to. Defaults to None. None means the file will not be saved to the local disk and the consumption will be through the content file\\_binary. Note, in a multi-node cluster, this is not recommended, since files will be only available in the main node. \n\n```py\nfrom snowflake.ml.ray.datasink import SnowflakeTableDatasink\n\ndatasink = SnowflakeTableDatasink(\n    table_name=\"MY_TABLE\",\n    database = \"MY_DB\",\n    schema = \"MY_SCHEMA\",\n    auto_create_table=True,\n    override=True,\n)\n\n# Write a processed dataset back to a snowflake table\n# For example take the Text API processing example above\nlabel_dataset.write_datasink(datasink, concurrency=4)\n# Now you can query the table in `MY_DB.MY_SCHEMA.MY_TABLE'\n```\n\n\n### Audio Processing and Distributed Inferencing Notebook\n\nTo get started, follow these steps:\n\n* Download the notebook “Audio Processing \\- Distributed Inferencing.ipynb” from this [link](https://github.com/Snowflake-Labs/sfguide-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/blob/main/Audio%20Processing%20-%20Distributed%20Inferencing.ipynb)\n\n* Navigate to Snowsight and change role to the **SYSADMIN** role\n\n* Navigate to **Projects** \\\u003E **Notebooks** in Snowsight\n\n* On the top right, click on **Notebook** down arrow and select **Import .ipynb** file from the dropdown menu\n\n* Create a new notebook with the following settings\n\n  * For the Notebook Location, select MULTINODE\\_MULTIGPU\\_MYDB database and AUDIO\\_TRANSCRIPTION\\_SCH schema\n\n  * Select Warehouse – ML\\_MODEL\\_WH\n\n  * Python Environment \\- Run On Container\n\n  * Runtime \\- Snowflake ML Runtime GPU 1.0\n\n  * Compute Pool \\- AUDIO\\_PROCESSING\\_CP\\_GPU\\_NV\\_S\\_5\\_NODES\n\n  * Click on **Create Button**\n\n![Audio Processing Distributed Inferencing Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Distributed_Inferencing_create_notebook.png)\n\n* Click the three dots in the top right \\\u003E Notebook Settings  \n* Enable the ALLOW\\_ALL\\_INTEGRATION and click SAVE.\n\n\n![Audio Processing Distributed Inferencing Notebook External Access](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/Audio_processing_Distributed_Inferencing_notebook_settings_external_access.png)\n\n* Run cells in the notebook\\!\n\n      Lets break down step by step in the notebook\n\n- Create a Ray cluster and scale upto 5 nodes.  \n- Configure logging for Ray Cluster and do necessary setups for the Ray Cluster  \n- Verify the Audio files in the stage uploaded during Step 3\\.  \n- Download the whisper model from OpenAI for distributed inferencing of the audio files  \n- Run distributed inference on the multi-node, multi-GPU audio transcription  \n- Write data to the Snowflake table using Snowflake APIs.\n\n\nNote: \"Please note, if you duplicate this notebook you will have to manually enable ALLOW_ALL_INTEGRATION again\"\n\n### Audio Processing and Distributed Inferencing ML Job\n\nYou can also leverage Snowflake ML Container Runtime outside of Snowflake Notebooks using ML Jobs. You can run an equivalent workload as an ML Job by following these steps:\n\n* [Install Python 3](https://www.python.org/downloads/) in your local environment if needed.\n\n* Install the ML Job SDK, which is included in `snowflake-ml-python`: `pip install snowflake-ml-python\u003E=1.9.2`\n\n* [Configure a default Snowflake connection](https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections)\n\n  \u003E Tip: You can generate a config for your account from Snowsight using the `Connect a tool to Snowflake` \u003E `Config File` menu items.\n    ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/setup_connect_tool.png) ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/setup_config_file.png)\n\n* Download the files [transcribe_audio.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py) and [run_audio_transcription_job.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/run_audio_transcription_job.py).\n\n* Inspect the file [transcribe_audio.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py). This is the payload script we will submit and execute inside the Container Runtime using ML Jobs.\n\n* Run `python run_audio_transcription_job.py` in your local environment. This will upload [transcribe_audio.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py) and kick off its execution using the `submit_file()` API from the ML Job SDK.\n\n* You can monitor the job execution in the Services and Jobs UI in Snowsight\n\n![Services and Jobs UI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/services_and_jobs_ui.png)\n\nLet's break down what the ML Job workflow is doing:\n\n- Create a Snowflake session from default configuration and set up database context\n- Submit the [transcribe_audio.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-distributed-multi-node-multi-gpu-audio-transcription-with-snowflake-ml-container-runtime/scripts/transcribe_audio.py) script as an ML Job targeting 5 instances with a minimum of 1, scaling elastically based on available compute pool capacity\n- Configure logging for Ray Cluster and do necessary setups for the Ray Cluster\n- Load audio files from Snowflake stage using SFStageBinaryFileDataSource with file pattern filtering\n- Download and initialize the Whisper large-v3 model from OpenAI for distributed inferencing\n- Run distributed inference on multi-node, multi-GPU setup using Ray's map_batches with AudioTranscriber actor\n- Write transcribed results to Snowflake table using SnowflakeTableDatasink\n- Monitor job execution status and retrieve logs for verification\n\n\n## Value Proposition\n\u003C!-- ------------------------ --\u003E\n\n\nSnowflake ML Container Runtime offers significant value for distributed multi-node, multi-GPU audio transcription. Here are the key benefits:\n\n**Leveraging Powerful Models**: Utilizes state-of-the-art models like Whisper for accurate audio transcription.\n\n**GPU Acceleration**: Supports GPU machine types for faster processing, demonstrated by the example using 5 GPU_NV_S nodes.\n\n**Cost-Effectiveness**: The demo processed approximately 2700 audio files in just 2 minutes using 5 Small GPU nodes on AWS (specifically GPU_NV_S compute family), each costing 0.57 credits/hour. The cost is derived from the Credit Consumption Table 1 (d) highlighted [here](/legal-files/CreditConsumptionTable.pdf)\n\n**Minimal Credit Consumption**: This resulted in a very low cost of only (2/60) * 0.57 = 0.019 credits per node, so 0.095 credits for 5 nodes or $0.285 to transcribe 2700 audio files (based on $3 per credit).\n\n**Speed**: Rapid transcription of a large number of files, significantly reducing processing time.\n\n**Scalability**: Handles large datasets and complex computations efficiently using distributed computing resources.\n\n**Managed Environment**: Eliminates the overhead of managing underlying infrastructure, allowing users to focus on ML projects.\nIntegration: Seamlessly combines with Snowflake's ML operations for a cohesive workflow.\n\n**Flexibility**: Pre-installed common ML packages with the option to install custom packages as needed.\n\n**Unstructured Data Processing**: APIs like SFStageBinaryFileDataSource and SnowflakeTableDatasink enable efficient handling of audio files from Snowflake Stage.\n\n\n## Conclusion and Resources\n\nIn conclusion, running Container Runtime in Snowflake ML and Snowflake Notebooks Container Runtime offers a robust and flexible infrastructure for managing large-scale, advanced data science and machine learning workflows directly within Snowflake. With the ability to install external packages and choose optimal compute resources, including GPU machine types, Container Runtime provides a more versatile environment suited to the needs of data science and ML teams.\n\nReady for more? After you complete this quickstart, you can try one of the following more advanced quickstarts:\n\n* [Build an End-to-End ML Workflow in Snowflake](/en/developers/guides/end-to-end-ml-workflow/)  \n* [Scale Embeddings with Snowflake Notebooks on Container Runtime](/en/developers/guides/scale-embeddings-with-snowflake-notebooks-on-container-runtime/)  \n* [Getting Started with Running Distributed PyTorch Models on Snowflake](/en/developers/guides/getting-started-with-running-distributed-pytorch-models-on-snowflake/).  \n* [Defect Detection Using Distributed PyTorch With Snowflake Notebooks](/en/developers/guides/defect-detection-using-distributed-pytorch-with-snowflake-notebooks/)\n\n### What You Learned\n\n* The key features of Snowflake Notebooks with Container Runtime in [Snowflake ML](http://www.snowflake.com/ml) to perform Multi-Node, Multi-GPU audio transcription at limitless scale over multiple audio files.\n\n### Related Quickstarts\n\n* [Train an XGBoost model with GPUs in Snowflake Notebooks](/en/developers/guides/train-an-xgboost-model-with-gpus-using-snowflake-notebooks/)  \n* [Defect Detection Using Distributed PyTorch With Snowflake Notebooks](/en/developers/guides/defect-detection-using-distributed-pytorch-with-snowflake-notebooks/)  \n* [Scale Embeddings with Snowflake Notebooks on Container Runtime](/en/developers/guides/scale-embeddings-with-snowflake-notebooks-on-container-runtime/)  \n* [Getting Started with Running Distributed PyTorch Models on Snowflake](/en/developers/guides/getting-started-with-running-distributed-pytorch-models-on-snowflake/)  \n* [Getting Started with Snowflake ML](/en/developers/guides/intro-to-machine-learning-with-snowpark-ml-for-python/)\n\n### Related Resources\n\n* [Documentation: Container Runtime for ML](https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml)  \n* [Documentation: Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview)  \n* [Documentation: Model Serving in SPCS](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container)  \n* [Intro Quickstart: Getting Started with Snowflake Notebook Container Runtime](/en/developers/guides/notebook-container-runtime/)  \n* [Snowflake ML Webpage](/en/data-cloud/snowflake-ml/)\n* [Documentation](https://docs.snowflake.com/LIMITEDACCESS/snowsight-notebooks/ui-snowsight-notebooks-runtime)\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-0cb2440a6f","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":{"id":"container-5fd6325218","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c385ec8107","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-02-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-200872fc35","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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