{"id":10083,"date":"2024-07-29T08:16:05","date_gmt":"2024-07-29T16:16:05","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=10083"},"modified":"2025-04-24T17:02:40","modified_gmt":"2025-04-24T17:02:40","slug":"integration-comet-union-flyte","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/integration-comet-union-flyte\/","title":{"rendered":"How to Use Comet&#8217;s New Integration with Union &#038; Flyte"},"content":{"rendered":"\n<figure class=\"wp-block-image aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"2160\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2.png\" alt=\"graphic showing the comet and union logos to visualize the new integration \" class=\"wp-image-10084\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2.png 3840w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2-300x169.png 300w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2-1024x576.png 1024w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2-768x432.png 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2-1536x864.png 1536w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/Comet-Union-V2-2048x1152.png 2048w\" sizes=\"auto, (max-width: 3840px) 100vw, 3840px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>In the machine learning (ML) and artificial intelligence (AI) domain, managing, tracking, and visualizing model training processes, especially at scale, is a significant challenge.<\/p>\n\n\n\n<p>Union, an optimized and more performant version of the open-source solution Flyte, provides scalability, declarative infrastructure, and data lineage, allowing AI developers to iterate and productionize AI or ML workflows quickly. Comet\u2019s machine learning platform enables seamless tracking, visualization, and management of model training processes, enhancing productivity and insight for data scientists and ML engineers.<\/p>\n\n\n\n<p>Union and Comet are best-of-breed solutions that, when integrated, further enhance the user experience. Without an integration, users must manually set up and manage connectivity, which can be cumbersome and error-prone. Additionally, users would be challenged with manual tracking, which could hinder their overall productivity and effectiveness.<\/p>\n\n\n\n<p>The new Comet Flyte plugin enables you to use Comet\u2019s machine-learning platform to manage, track, and visualize models during training. In this blog post, you\u2019ll learn how to use the Comet plugin on Union.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Flytekit\u2019s Comet Plugin<\/h2>\n\n\n\n<p>In Union, data and compute are fundamental building blocks for developing all workflows. You can train models using machine learning or AI libraries such as PyTorch Lightning or LightGBM. Union is built on Flyte, which uses declarative orchestration to scale any computation easily.<\/p>\n\n\n\n<p>We start with flytekit\u2019s <strong><code>comet_ml_login<\/code><\/strong> decorator, which initializes Comet\u2019s platform with your credentials during a Flyte execution. After decorating your function, the body consists of code you\u2019ll find in Comet\u2019s <a href=\"https:\/\/www.comet.com\/docs\/v2?utm_source=Union_website&amp;utm_medium=referral&amp;utm_content=flyte_integration\">documentation<\/a>:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from flytekit import task\nfrom flytekit.extras.accelerators import L4\nfrom flytekitplugins.comet_ml import comet_ml_login\n\ncomet_ml_secret = Secret(key=\"comet-ml-api-key\")\n\n@task(\ncontainer_image=image, secret_requests=[comet_ml_secret],\nrequests=Resources(cpu=\"8\", gpu=\"1\"),\naccelerator=L4\n)\n@comet_ml_login(\nproject_name=COMET_PROJECT, workspace=COMET_WORKSPACE,\nsecret=comet_ml_secret,\n)\ndef train_lightning(dataset: FlyteDirectory, hidden_layer_size: int):\nfrom pytorch_lightning.loggers import CometLogger\n\ncomet_logger = CometLogger()\ntrainer = Trainer(..., logger=comet_logger)\ntrainer.fit(...)<\/pre>\n\n\n\n<p>The above example uses Flyte\u2019s declarative syntax to run a training script with PyTorch Lightning on a NVIDIA L4 GPU. With Comet\u2019s Lightning integration, the training process is tracked and logged on Comet\u2019s platform. The <strong><code>comet_ml_login<\/code><\/strong> decorator will start the run and configure Union\u2019s UI to show a link to Comet:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"988\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/comet-union-integration-ui.png\" alt=\"product screenshot showing a link to Comet's model training logs linked from within Flyte\" class=\"wp-image-10088\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/comet-union-integration-ui.png 1600w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/comet-union-integration-ui-300x185.png 300w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/comet-union-integration-ui-1024x632.png 1024w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/comet-union-integration-ui-768x474.png 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/comet-union-integration-ui-1536x948.png 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">Elastic GPUs<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">With Flyte\u2019s PyTorch Distributed plugin, <\/span><strong><code>flytekitplugins-kfpytorch<\/code><\/strong><span style=\"font-weight: 400;\">, training jobs can scale to multiple nodes and GPUs with a simple configuration. Together with PyTorch Lightning, you can scale your training jobs:<\/span><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from flytekitplugins.kfpytorch import Elastic\n\n@task(\ntask_config=Elastic(\nnnodes=NUM_NODES,\nnproc_per_node=NUM_DEVICES,\n),\naccelerator=A100,\nrequests=Resources(\nmem=\"32Gi\", cpu=\"48\", gpu=\"2\", ephemeral_storage=\"100Gi\"),\n...\n)\n@comet_ml_login(\nproject_name=COMET_PROJECT, workspace=COMET_WORKSPACE,\nsecret=comet_ml_secret,\n)\ndef train_lightning(dataset: FlyteDirectory, hidden_layer_size: int):\ncomet_logger = CometLogger()\ntrainer = Trainer(..., logger=comet_logger)<\/pre>\n\n\n\n<p>With the <strong><code>comet_ml_login<\/code><\/strong> decorator, the scaled up training jobs are tracked on Comet\u2019s platform.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"695\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/tracking-flyte-training-jobs-comet.png\" alt=\"ui screenshot of charts visualizing val_loss, loss, and train_loss in Comet for training jobs run in Flyte \" class=\"wp-image-10089\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/tracking-flyte-training-jobs-comet.png 1600w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/tracking-flyte-training-jobs-comet-300x130.png 300w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/tracking-flyte-training-jobs-comet-1024x445.png 1024w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/tracking-flyte-training-jobs-comet-768x334.png 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/tracking-flyte-training-jobs-comet-1536x667.png 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">Scaling with Dynamic Workflows<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">With Flyte\u2019s dynamic workflows, you can quickly launch multiple experiments and track them all on Comet. In this example, you see how to use Flyte\u2019s declarative infrastructure to train multiple models:<\/span><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">@task(...)\n@comet_ml_login(\nproject_name=COMET_PROJECT, workspace=COMET_WORKSPACE,\nsecret=comet_ml_secret,\n)\ndef train_lightning_model(data: FlyteDirectory, hidden_layer_size: int):\ncomet_logger = CometLogger()\ntrainer = Trainer(..., logger=comet_logger)\n\n@dynamic(container_image=image)\ndef main(hidden_layer_sizes: list[int]):\ndataset = get_dataset()\nfor hidden_layer_size in hidden_layer_sizes:\ntrain_lightning_model(\ndataset=dataset, hidden_layer_size=hidden_layer_size)<\/pre>\n\n\n\n<p>In the Union UI, the workflow dynamically scales out to 5 GPU-powered tasks:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"1250\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/union-gpu-tasks.jpg\" alt=\"diagram showing breakout of 5 union gpu tasks \" class=\"wp-image-10090\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/union-gpu-tasks.jpg 1600w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/union-gpu-tasks-300x234.jpg 300w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/union-gpu-tasks-1024x800.jpg 1024w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/union-gpu-tasks-768x600.jpg 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2024\/07\/union-gpu-tasks-1536x1200.jpg 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>PyTorch Lightning\u2019s <strong><code>CometLogger<\/code><\/strong> automatically logs the metrics, hyperparameters, and checkpoints during training. In Comet, you can compare the different runs and evaluate the model\u2019s performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>Union&#8217;s declarative infrastructure and scalable orchestration platform makes it simple to scale up your machine learning or AI workflows and put them in production. With flytekit&#8217;s Comet plugin, you can easily track experiments, visualize results, and debug models. To use the plugin, install it with <strong><code>pip install flytekitplugins-comet-ml<\/code><\/strong>.<\/p>\n\n\n\n<p>Union and Comet offer powerful features independently. This integration significantly enhances their combined capabilities, reducing manual effort, improving efficiency, and ensuring more comprehensive tracking and visualization of AI workflows.<\/p>\n\n\n\n<p>To learn more about Union, contact us at <a href=\"https:\/\/www.union.ai\/demo\">www.union.ai\/demo<\/a>.<br>\nTo learn more about Comet, contact us at <a href=\"https:\/\/www.comet.com\/site\/about-us\/contact-us?utm_source=Union_website&amp;utm_medium=referral&amp;utm_content=flyte_integration\">https:\/\/www.comet.com\/site\/about-us\/contact-us<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In the machine learning (ML) and artificial intelligence (AI) domain, managing, tracking, and visualizing model training processes, especially at scale, is a significant challenge. Union, an optimized and more performant version of the open-source solution Flyte, provides scalability, declarative infrastructure, and data lineage, allowing AI developers to iterate and productionize AI or ML workflows quickly. [&hellip;]<\/p>\n","protected":false},"author":135,"featured_media":10084,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[8,10,23,6,9,7],"tags":[],"coauthors":[106,224],"class_list":["post-10083","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-comet-community-hub","category-industry","category-integrations","category-machine-learning","category-product","category-tutorials"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Use Comet&#039;s New Integration with Union &amp; Flyte<\/title>\n<meta name=\"description\" content=\"The new Comet Flyte plugin enables you to use Comet\u2019s machine-learning platform to manage, track, and visualize models during training.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/integration-comet-union-flyte\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Use Comet&#039;s New Integration with Union &amp; 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