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Using Filestore as an accelerator for AI/ML workloads on GKE | Google Cloud Blog
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class="nRhiJb-DARUcf"><div class="Wdmc0c nRhiJb-DbgRPb-wNfPc-cGMI2b"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-BFbNVe-r8s4j-bMElCd dIsJJe" track-name="containers & kubernetes"track-type="tag">Containers & Kubernetes</div><div class="nRhiJb-ObfsIf"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-R6PoUb"></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-EehZO nRhiJb-fmcmS-oXtfBe"><h1 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><span class="FewWi"></span>GKE plus Filestore - Improve training times for AI/ML workloads by up to 37%</div></h1></div></div><div class="nRhiJb-fmcmS-oXtfBe dEogG">February 3, 2024</div></div></section><div class="EKklye"><div class="nRhiJb-DARUcf ZWw7T"><div class="npzWPc"><div class="dzoHJ"><div class="nRhiJb-DX2B6 nRhiJb-DX2B6-OWXEXe-h30Snd"><div class="nRhiJb-j5y3u"><ul class="nRhiJb-Qijihe phRaUe" role="list"><li class="hpHPGf"><a class="nRhiJb-ARYxNe" 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on GKE, improving performance and training time.%0A%0Acloud.google.com/blog/products/containers-kubernetes/using-filestore-as-an-accelerator-for-aiml-workloads-on-gke"track-metadata-module="social icons" target="_blank" rel="noopener"><svg class="nRhiJb-Bz112c nRhiJb-Bz112c-OWXEXe-DX2B6 nRhiJb-Bz112c-OWXEXe-nSuQf" viewBox="0 0 24 24" role="presentation" aria-hidden="true" role="presentation" aria-hidden="true"><path d="M20 4H4c-1.1 0-2 .9-2 2v12c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm-.8 2L12 10.8 4.8 6h14.4zM4 18V7.87l8 5.33 8-5.33V18H4z"></path></svg></a></li></ul></div></div></div></div></div><div><section class="nRhiJb-DARUcf"><div class="nRhiJb-DbgRPb-wNfPc-ma6Yeb nRhiJb-DbgRPb-wNfPc-cGMI2b"><section class="DA9Qj nRhiJb-ObfsIf nRhiJb-fmcmS-oXtfBe"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-ibL1re"></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-II5mzb kWSSdd"><h5 class="cHE8Ub Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">Saikat Roychowdhury</h5><p class="nRhiJb-qJTHM khCp7b">Senior Software Engineer</p></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-II5mzb kWSSdd"><h5 class="cHE8Ub Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">Akshay Ram</h5><p class="nRhiJb-qJTHM khCp7b">Senior Product Manager</p></div></section></div></section><div class="nRhiJb-DARUcf"><div class="nRhiJb-ObfsIf nRhiJb-DbgRPb-wNfPc-ma6Yeb nRhiJb-DbgRPb-qWD73c-cGMI2b"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-ibL1re dzoHJ"><div class="aLW4J" track-metadata-module="left rail"><h5 class="qZDXV Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc">Try Gemini 1.5 models</h5><p class="nRhiJb-cHYyed Mrfygc nRhiJb-BP2Omd-ma6Yeb-KE6vqe nRhiJb-DbgRPb-R6PoUb-cGMI2b">Google's most advanced multimodal models in Vertex AI</p><a class="nRhiJb-LgbsSe nRhiJb-LgbsSe-OWXEXe-CNusmb-o6Shpd " href="https://console.cloud.google.com/freetrial/?redirectPath=/vertex-ai/generative/multimodal/create/text" track-metadata-module="left rail"track-metadata-module_headline="try gemini 15 models" track-name="try it"track-type="button"track-metadata-position="cta"track-metadata-eventdetail="console.cloud.google.com/freetrial/?redirectPath=/vertex-ai/generative/multimodal/create/text">Try it </a></div></div><div class="OYL9D nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf" jsname="tx2NYc"><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><h2><b>Problem statement and use case</b></h2><p>Training AI/ML workloads requires a lot of data, which is often stored in large numbers of small files — think training driverless cars training numerous image data or performing protein analysis, where the training set often consists of numerous small files, sized 100K to 2MB each. When selecting tools for these use-cases, users often turn to Google’s <a href="https://cloud.google.com/storage">Cloud Storage</a>, which provides low latency and high throughput with reasonable price and performance, and optionally use <a href="https://cloud.google.com/storage/docs/gcs-fuse">FUSE</a> as a file interface for portability. However, when the dataset is composed of small files, latency becomes an issue; a training workload can have tens-of-thousands of small file batches per epoch, as well as multiple worker nodes accessing Cloud Storage.</p><p>To accelerate load times, users need storage that provides low latency and high <i>throughput</i>. Using <a href="https://cloud.google.com/filestore">Filestore</a> as an “accelerator” can help. Filestore provides fast-access file storage with all the benefits of multiple read/write access and a native POSIX interface. You can still leverage Cloud Storage as your primary storage source, and use Filestore to provide cost-effective, low-latency data access for your worker nodes.</p><p>In this blog post, we focus on the important role that Filestore can play in training AI/ML workloads, helping you make informed choices to accelerate your workload performance. Read on to learn how to use this solution according to personas and responsibilities:</p><h2><b>Usage details</b></h2><p>The following screenshots highlight how to use GKE and Filestore for your AI/ML applications. You can find the full source code in <a href="https://github.com/GoogleCloudPlatform/kubernetes-engine-samples/tree/main/demos/vit-training-example" target="_blank">this repository</a>.</p><h3><b>Persona 1: Kubernetes Platform admin staging Filestore for use by data scientists</b></h3><p>The Kubernetes platform admin is responsible for creating infrastructure for data science teams to consume. In this case, the platform admin sets up Filestore using a Kubernetes <a href="https://kubernetes.io/docs/concepts/storage/persistent-volumes/" target="_blank">persistent volume</a> and makes it accessible to data scientists via a <a href="https://jupyter.org/" target="_blank">Jupyter Notebook</a> setup, or if working with multiple users, via <a href="https://jupyter.org/hub" target="_blank">JupyterHub</a>. The data scientist can then simply access the notebook and write code.</p><p>For this specific example, we used the off-the-shelf premium-rwx GKE <a href="https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/filestore-csi-driver#storage-class">StorageClass</a>, which dynamically provisions a <a href="https://cloud.google.com/filestore/docs/service-tiers#basic_hdd_and_basic_ssd_tiers">Filestore Basic SSD</a> instance under the hood. The Jupyter pod specification uses the <a href="https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/filestore-csi-driver">GKE Filestore CSI driver</a> to provision a PersistentVolumeClaim (PVC) which mounts a Filestore share to the Pod. The mounted volume path (which serves as a cache directory for data and models) is exposed as an environment variable to the data scientist (notebook user).</p><p><i>Screenshot 1: Tensorflow deployment with a Filestore volume</i></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><i>Screenshot 2: Filestore Persistent volume claim</i></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_ccgBmxV.max-900x900.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_ccgBmxV.max-900x900.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_ccgBmxV.max-900x900.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_ccgBmxV.max-900x900.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><h3><b>Persona 2: Data scientist accessing data from the Jupyter Notebook</b></h3><p>A data scientist simply wants to focus on running experiments. In this example, we train <a href="https://huggingface.co/google/vit-base-patch16-224-in21k" target="_blank">Google’s Vision Transformer Model (ViT)</a> and load a <a href="https://huggingface.co/datasets/food101" target="_blank">food101 dataset</a> from <a href="https://huggingface.co/" target="_blank">Hugging Face</a>, which is primarily composed of 100k images totalling 5 GiB. We use Hugging Face’s cache feature, which automatically caches the data on the filesystem after first read. The file path needs to be shared with your data science teams as an environment variable. By passing the Filestore path as an environment variable, the data is cached on Filestore. Since the data is cached and not fetched from Cloud Storage, we run two epochs of training on a a2-highgpu-1g machine and compare training times directly from Cloud Storage (baseline), and observe a 37% improvement in training times!</p><p><i>Screenshot 3: Loading dataset from Hugging face and enabling caching</i></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><i>Screenshot 4: Jupyter notebook file explorer with mounted filestore directory</i></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><i>Screenshot 5,6,7: Download model, start training and measure training time</i></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="font-style:italic;vertical-align:baseline">Table 1. Training results </span></p> <div align="left" style="width:100%;overflow-x:auto;width:100%;overflow-x:auto"> <table><colgroup><col><col><col></colgroup> <tbody> <tr> <td style="vertical-align:top;border:1px solid #000000;padding:16px"> <p style="text-align:center"><strong style="vertical-align:baseline">Storage option </strong></p> </td> <td style="vertical-align:bottom;border:1px solid #000000;padding:16px"> <p style="text-align:center"><strong style="vertical-align:baseline">Training Time (Secs)</strong></p> </td> <td style="vertical-align:bottom;border:1px solid #000000;padding:16px"> <p style="text-align:center"><strong style="vertical-align:baseline">Improvement</strong></p> </td> </tr> <tr> <td style="vertical-align:top;border:1px solid #000000;padding:16px"> <p><span style="vertical-align:baseline">Cloud Storage</span></p> </td> <td style="vertical-align:bottom;border:1px solid #000000;padding:16px"> <p style="text-align:center"><span style="vertical-align:baseline">4837</span></p> </td> <td style="vertical-align:bottom;border:1px solid #000000;padding:16px"> </td> </tr> <tr> <td style="vertical-align:top;border:1px solid #000000;padding:16px"> <p><span style="vertical-align:baseline">Filestore Basic SSD (Filestore Zonal High Band preferred)</span></p> </td> <td style="vertical-align:top;border:1px solid #000000;padding:16px"> <p style="text-align:center"><span style="vertical-align:baseline">3006</span></p> </td> <td style="vertical-align:top;border:1px solid #000000;padding:16px"> <p style="text-align:center"><span style="vertical-align:baseline"> 37%</span></p> </td> </tr> </tbody> </table> </div> <h2><strong style="vertical-align:baseline">Usage considerations </strong></h2> <p><span style="vertical-align:baseline">In this blog, we’ve highlighted the benefits of using Filestore as an accelerator in front of Cloud Storage, especially when your dataset is composed of numerous small files. You will be </span><a href="https://cloud.google.com/filestore/pricing"><span style="text-decoration:underline;vertical-align:baseline">billed for Filestore Instances</span></a><span style="vertical-align:baseline"> but the costs of storage may be well worth it when you can accelerate your training times (and GPU resource consumption). When files are larger sizes data directly from Cloud Storage may be a better fit. Choose the best architecture based on your use case. </span></p></span></section><section class="kcBhad"><section class="Fabbec"><span class="WrMNjb">Posted in</span><ul class="FzXI4e"><li class="I4B51b"><a href="https://cloud.google.com/blog/products/containers-kubernetes" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/containers-kubernetes"track-metadata-module="tag list"track-metadata-module_headline="posted in">Containers & Kubernetes</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/products/storage-data-transfer" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/storage-data-transfer"track-metadata-module="tag list"track-metadata-module_headline="posted in">Storage & Data Transfer</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/products/ai-machine-learning" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/ai-machine-learning"track-metadata-module="tag list"track-metadata-module_headline="posted in">AI & Machine Learning</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/topics/developers-practitioners" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/topics/developers-practitioners"track-metadata-module="tag list"track-metadata-module_headline="posted in">Developers & Practitioners</a></li></ul></section></section></div></div></div></div></div><section class="nRhiJb-DARUcf " track-metadata-module="related articles" track-metadata-module_headline="related articles"><div class="nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-DbgRPb-wNfPc-cGMI2b"><h5 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc nRhiJb-DbgRPb-II5mzb-cGMI2b">Related articles</h5><section class="m9cUGf HGev3 nJD2Qe nRhiJb-ObfsIf"><div class=" QaGyvd nRhiJb-kR0ZEf-OWXEXe-GV1x9e-c5RTEf 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,[[97656899,1714255,48887082,97863042,97863170,97442199,97535270,97517172,1706538,97684535,93874004,93778619,48554501,97785988,48830069,97716269,48489830,97656881,48887064,97442181,97517154,97684517,93873986,97785970],null,null,null,null,true],]; window.IJ_valuesCb && window.IJ_valuesCb();</script><script nonce="WEnAKvJTLWBktk7Lx0pI_g">(function(){'use strict';var c=window,d=[];c.aft_counter=d;var e=[],f=0;function _recordIsAboveFold(a){if(!c._isLazyImage(a)&&!a.hasAttribute("data-noaft")&&a.src){var b=(c._isVisible||function(){})(c.document,a);a.setAttribute("data-atf",b);b&&(e.indexOf(a)!==-1||d.indexOf(a)!==-1||a.complete||d.push(a),a.hasAttribute("data-iml")&&(a=Number(a.getAttribute("data-iml")),a>f&&(f=a)))}} c.initAft=function(){f=0;e=Array.prototype.slice.call(document.getElementsByTagName("img")).filter(function(a){return!!a.getAttribute("data-iml")});[].forEach.call(document.getElementsByTagName("img"),function(a){try{_recordIsAboveFold(a)}catch(b){throw b.message=a.hasAttribute("data-iid")?b.message+"\nrecordIsAboveFold error for defer inlined image":b.message+("\nrecordIsAboveFold error for img element with <src: "+a.src+">"),b;}});if(d.length===0)c.onaft(f)};}).call(this); initAft()</script><script class="ds:0" nonce="WEnAKvJTLWBktk7Lx0pI_g">AF_initDataCallback({key: 'ds:0', hash: '1', data:[["Using Filestore as an accelerator for AI/ML workloads on GKE","Using Filestore as an accelerator can accelerate load times for AI/ML workloads running on GKE, improving performance and training time.",[1706922000],"https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-2600x2600.jpg","https://cloud.google.com/blog/products/containers-kubernetes/using-filestore-as-an-accelerator-for-aiml-workloads-on-gke",[1706922006,499430000]],[["Saikat Roychowdhury","Senior Software Engineer"],["Akshay Ram","Senior Product Manager"]],[null,"\u003cscript type\u003d\"application/ld+json\"\u003e{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"@id\":\"https://cloud.google.com/blog/products/containers-kubernetes/using-filestore-as-an-accelerator-for-aiml-workloads-on-gke\",\"headline\":\"Using Filestore as an accelerator for AI/ML workloads on GKE\",\"description\":\"Using Filestore as an accelerator can accelerate load times for AI/ML workloads running on GKE, improving performance and training time.\",\"image\":\"https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-2600x2600.jpg\",\"author\":[{\"@type\":\"Person\",\"name\":\"Saikat Roychowdhury\",\"url\":\"\"},{\"@type\":\"Person\",\"name\":\"Akshay Ram\",\"url\":\"\"}],\"datePublished\":\"2024-02-03\",\"publisher\":{\"@type\":\"Organization\",\"name\":\"Google Cloud\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https://www.gstatic.com/devrel-devsite/prod/v8bb8fa0afe9a8c3a776ebeb25d421bb443344d789b3607754dfabea418b8c4be/cloud/images/cloud-logo.svg\"}},\"url\":\"https://cloud.google.com/blog/products/containers-kubernetes/using-filestore-as-an-accelerator-for-aiml-workloads-on-gke\",\"keywords\":[\"Containers \\u0026 Kubernetes\",\"Storage \\u0026 Data Transfer\",\"AI \\u0026 Machine Learning\",\"Developers \\u0026 Practitioners\"],\"timeRequired\":\"PT4M\"}\u003c/script\u003e"],["GKE plus Filestore - Improve training times for AI/ML workloads by up to 37%"],null,null,[[null,null,[null,[null,"\u003ch2\u003e\u003cb\u003eProblem statement and use case\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTraining AI/ML workloads requires a lot of data, which is often stored in large numbers of small files \u2014 think training driverless cars training numerous image data or performing protein analysis, where the training set often consists of numerous small files, sized 100K to 2MB each. When selecting tools for these use-cases, users often turn to Google\u2019s \u003ca href\u003d\"https://cloud.google.com/storage\"\u003eCloud Storage\u003c/a\u003e, which provides low latency and high throughput with reasonable price and performance, and optionally use \u003ca href\u003d\"https://cloud.google.com/storage/docs/gcs-fuse\"\u003eFUSE\u003c/a\u003e as a file interface for portability. However, when the dataset is composed of small files, latency becomes an issue; a training workload can have tens-of-thousands of small file batches per epoch, as well as multiple worker nodes accessing Cloud Storage.\u003c/p\u003e\u003cp\u003eTo accelerate load times, users need storage that provides low latency and high \u003ci\u003ethroughput\u003c/i\u003e. Using \u003ca href\u003d\"https://cloud.google.com/filestore\"\u003eFilestore\u003c/a\u003e as an \u201caccelerator\u201d can help. Filestore provides fast-access file storage with all the benefits of multiple read/write access and a native POSIX interface. You can still leverage Cloud Storage as your primary storage source, and use Filestore to provide cost-effective, low-latency data access for your worker nodes.\u003c/p\u003e\u003cp\u003eIn this blog post, we focus on the important role that Filestore can play in training AI/ML workloads, helping you make informed choices to accelerate your workload performance. Read on to learn how to use this solution according to personas and responsibilities:\u003c/p\u003e\u003ch2\u003e\u003cb\u003eUsage details\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe following screenshots highlight how to use GKE and Filestore for your AI/ML applications. You can find the full source code in \u003ca href\u003d\"https://github.com/GoogleCloudPlatform/kubernetes-engine-samples/tree/main/demos/vit-training-example\" target\u003d\"_blank\"\u003ethis repository\u003c/a\u003e.\u003c/p\u003e\u003ch3\u003e\u003cb\u003ePersona 1: Kubernetes Platform admin staging Filestore for use by data scientists\u003c/b\u003e\u003c/h3\u003e\u003cp\u003eThe Kubernetes platform admin is responsible for creating infrastructure for data science teams to consume. In this case, the platform admin sets up Filestore using a Kubernetes \u003ca href\u003d\"https://kubernetes.io/docs/concepts/storage/persistent-volumes/\" target\u003d\"_blank\"\u003epersistent volume\u003c/a\u003e and makes it accessible to data scientists via a \u003ca href\u003d\"https://jupyter.org/\" target\u003d\"_blank\"\u003eJupyter Notebook\u003c/a\u003e setup, or if working with multiple users, via \u003ca href\u003d\"https://jupyter.org/hub\" target\u003d\"_blank\"\u003eJupyterHub\u003c/a\u003e. The data scientist can then simply access the notebook and write code.\u003c/p\u003e\u003cp\u003eFor this specific example, we used the off-the-shelf premium-rwx GKE \u003ca href\u003d\"https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/filestore-csi-driver#storage-class\"\u003eStorageClass\u003c/a\u003e, which dynamically provisions a \u003ca href\u003d\"https://cloud.google.com/filestore/docs/service-tiers#basic_hdd_and_basic_ssd_tiers\"\u003eFilestore Basic SSD\u003c/a\u003e instance under the hood. The Jupyter pod specification uses the \u003ca href\u003d\"https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/filestore-csi-driver\"\u003eGKE Filestore CSI driver\u003c/a\u003e to provision a PersistentVolumeClaim (PVC) which mounts a Filestore share to the Pod. The mounted volume path (which serves as a cache directory for data and models) is exposed as an environment variable to the data scientist (notebook user).\u003c/p\u003e\u003cp\u003e\u003ci\u003eScreenshot 1: Tensorflow deployment with a Filestore volume\u003c/i\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png 1364w"," 1060px, 1364px","https://storage.googleapis.com/gweb-cloudblog-publish/images/1_ls9F65x.max-1400x1400.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003ci\u003eScreenshot 2: Filestore Persistent volume claim\u003c/i\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/2_ccgBmxV.max-900x900.png",null,null,"https://storage.googleapis.com/gweb-cloudblog-publish/images/2_ccgBmxV.max-900x900.png"],null,3]]],[null,null,[null,[null,"\u003ch3\u003e\u003cb\u003ePersona 2: Data scientist accessing data from the Jupyter Notebook\u003c/b\u003e\u003c/h3\u003e\u003cp\u003eA data scientist simply wants to focus on running experiments. In this example, we train \u003ca href\u003d\"https://huggingface.co/google/vit-base-patch16-224-in21k\" target\u003d\"_blank\"\u003eGoogle\u2019s Vision Transformer Model (ViT)\u003c/a\u003e and load a \u003ca href\u003d\"https://huggingface.co/datasets/food101\" target\u003d\"_blank\"\u003efood101 dataset\u003c/a\u003e from \u003ca href\u003d\"https://huggingface.co/\" target\u003d\"_blank\"\u003eHugging Face\u003c/a\u003e, which is primarily composed of 100k images totalling 5 GiB. We use Hugging Face\u2019s cache feature, which automatically caches the data on the filesystem after first read. The file path needs to be shared with your data science teams as an environment variable. By passing the Filestore path as an environment variable, the data is cached on Filestore. Since the data is cached and not fetched from Cloud Storage, we run two epochs of training on a a2-highgpu-1g machine and compare training times directly from Cloud Storage (baseline), and observe a 37% improvement in training times!\u003c/p\u003e\u003cp\u003e\u003ci\u003eScreenshot 3: Loading dataset from Hugging face and enabling caching\u003c/i\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png 1600w"," 1060px, 1600px","https://storage.googleapis.com/gweb-cloudblog-publish/images/3_vQZlUsb.max-1600x1600.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003ci\u003eScreenshot 4: Jupyter notebook file explorer with mounted filestore directory\u003c/i\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png 1024w, https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png 1024w"," 1024px, 1024px","https://storage.googleapis.com/gweb-cloudblog-publish/images/4_oysXS0v.max-1100x1100.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003ci\u003eScreenshot 5,6,7: Download model, start training and measure training time\u003c/i\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png 697w, https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png 697w"," 697px, 697px","https://storage.googleapis.com/gweb-cloudblog-publish/images/5_wQxQfRj.max-700x700.png"],null,3]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png 1079w"," 1060px, 1079px","https://storage.googleapis.com/gweb-cloudblog-publish/images/6_IOT16Ht.max-1100x1100.png"],null,3]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png 1077w"," 1060px, 1077px","https://storage.googleapis.com/gweb-cloudblog-publish/images/7_ijvWgx8.max-1100x1100.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"font-style:italic;vertical-align:baseline\"\u003eTable 1. Training results\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cdiv align\u003d\"left\" style\u003d\"width:100%;overflow-x:auto;width:100%;overflow-x:auto\"\u003e\n\u003ctable\u003e\u003ccolgroup\u003e\u003ccol\u003e\u003ccol\u003e\u003ccol\u003e\u003c/colgroup\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style\u003d\"vertical-align:top;border:1px solid #000000;padding:16px\"\u003e\n\u003cp style\u003d\"text-align:center\"\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eStorage option\u00a0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style\u003d\"vertical-align:bottom;border:1px solid #000000;padding:16px\"\u003e\n\u003cp style\u003d\"text-align:center\"\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eTraining Time (Secs)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style\u003d\"vertical-align:bottom;border:1px solid #000000;padding:16px\"\u003e\n\u003cp style\u003d\"text-align:center\"\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eImprovement\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style\u003d\"vertical-align:top;border:1px solid #000000;padding:16px\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eCloud Storage\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style\u003d\"vertical-align:bottom;border:1px solid #000000;padding:16px\"\u003e\n\u003cp style\u003d\"text-align:center\"\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e4837\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style\u003d\"vertical-align:bottom;border:1px solid #000000;padding:16px\"\u003e\u00a0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style\u003d\"vertical-align:top;border:1px solid #000000;padding:16px\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eFilestore Basic SSD (Filestore Zonal High Band preferred)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style\u003d\"vertical-align:top;border:1px solid #000000;padding:16px\"\u003e\n\u003cp style\u003d\"text-align:center\"\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e3006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style\u003d\"vertical-align:top;border:1px solid #000000;padding:16px\"\u003e\n\u003cp style\u003d\"text-align:center\"\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e 37%\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eUsage considerations\u00a0\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIn this blog, we\u2019ve highlighted the benefits of using Filestore as an accelerator in front of Cloud Storage, especially when your dataset is composed of numerous small files. You will be \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/filestore/pricing\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ebilled for Filestore Instances\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e but the costs of storage may be well worth it when you can accelerate your training times (and GPU resource consumption). When files are larger sizes data directly from Cloud Storage may be a better fit. Choose the best architecture based on your use case. \u003c/span\u003e\u003c/p\u003e"]]]],[["Security \u0026 Identity","Make IAM for GKE easier to use with Workload Identity Federation","Workload Identity Federation for GKE is now even easier to use with deeper IAM integration. Here\u2019s what you need to know.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/17_-_Security__Identity_NrORvDT.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/17_-_Security__Identity_NrORvDT.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/17_-_Security__Identity_NrORvDT.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/17_-_Security__Identity_NrORvDT.max-700x700.jpg"],null,4,null,"https://cloud.google.com/blog/products/identity-security/make-iam-for-gke-easier-to-use-with-workload-identity-federation",null,1,[["Cynthia Thomas","Product Manager"],["Shaun Liu","Product Manager"]],null,"55618"],["Partners","Google Cloud NetApp Volumes now available for OpenShift on Google Cloud","With Google Cloud NetApp Volumes in OpenShift, you get the benefits of a managed storage service with enterprise-grade NetApp storage capabilities.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/topics/partners/netapp-volumes-now-available-for-openshift-on-google-cloud",null,1,[["Pablo Osinaga","Product Manager"],["Sean Derrington","Group Product Manager, Storage"]],null,"55603"],["Containers \u0026 Kubernetes","Empower your teams with self-service Kubernetes using GKE fleets and Argo CD","Set up a team-centric multi-cluster infrastructure with Google Kubernetes Engine (GKE) fleets and Argo CD, plus Connect Gateway and Workload Identity.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/containers-kubernetes/empower-your-teams-with-self-service-kubernetes-using-gke-fleets-and-argo-cd",null,1,[["Jiayi Bao","Software Engineer, GKE"],["Nick Eberts","Product Manager, GKE"]],null,"55563"],["Containers \u0026 Kubernetes","Data loading best practices for AI/ML inference on GKE","When running AI/ML inference workloads on GKE, learn how to accelerate data loading for inference serving containers and downloading models + weights.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/containers-kubernetes/improve-data-loading-times-for-ml-inference-apps-on-gke",null,1,[["Brian Kaufman","Senior Product Manager GKE AI/ML"],["Akshay Ram","Group Product Manager GKE AI/ML"]],null,"55570"]],null,"Containers \u0026 Kubernetes",["Try Gemini 1.5 models","Google's most advanced multimodal models in Vertex AI","Try it","https://console.cloud.google.com/freetrial/?redirectPath\u003d/vertex-ai/generative/multimodal/create/text"],[["Containers \u0026 Kubernetes","https://cloud.google.com/blog/products/containers-kubernetes","containers-kubernetes"],["Storage \u0026 Data Transfer","https://cloud.google.com/blog/products/storage-data-transfer","storage-data-transfer"],["AI \u0026 Machine Learning","https://cloud.google.com/blog/products/ai-machine-learning","ai-machine-learning"],["Developers \u0026 Practitioners","https://cloud.google.com/blog/topics/developers-practitioners","developers"]],null,null,4], sideChannel: {}});</script><script id="wiz_jd" nonce="WEnAKvJTLWBktk7Lx0pI_g">if (window['_wjdc']) {const wjd = {}; window['_wjdc'](wjd); delete window['_wjdc'];}</script><script aria-hidden="true" id="WIZ-footer" nonce="WEnAKvJTLWBktk7Lx0pI_g">window.wiz_progress&&window.wiz_progress(); window.stopScanForCss&&window.stopScanForCss(); ccTick('bl');</script></body></html><footer id="ZCHFDb"><footer 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