Getting Start with Recommender System

The Variational Autoencoder (VAE) shown here is an optimized implementation of the architecture first described in Variational Autoencoders for Collaborative Filtering and can be used for recommendation tasks. The main differences between this model and the original one are the performance optimizations, such as using sparse matrices, mixed precision, larger mini-batches and multiple GPUs. These changes enabled us to achieve a significantly higher speed while maintaining the same accuracy. Because of our fast implementation, we've also been able to carry out an extensive hyperparameter search to slightly improve the accuracy metrics.

By using Variational Autoencoder for Collaborative Filtering (VAE-CF), you can quickly train a recommendation model for the collaborative filtering task. The required input data consists of pairs of user-item IDs for each interaction between a user and an item. With a trained model, you can run inference to predict what items a new user most likely to interact with.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPUs. Therefore, researchers can get results 1.9x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model

The model consists of two parts: the encoder and the decoder. The encoder transforms the vector, which contains the interactions for a specific user, into a n-dimensional variational distribution. We can use this variational distribution to obtain the latent representation of a user. This latent representation is then fed into the decoder. The result is a vector of item interaction probabilities for a particular user.

The dataset that we use for training this model is a movie rating dataset. The trained model predicts the rate of a movie for a user based on previous rates of the user for other movies. It predicts what is the probability that the user choose a movie.

Default Configuration

The following features were implemented in this model:

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Sparse matrix support
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Data-parallel multi-GPU training
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Dynamic loss scaling with backoff for tensor cores (mixed precision) training
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Feature support matrix
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The following features are supported by this model:

Feature\tVAE-CF\nHorovod Multi-GPU (NCCL)\tYes\nAutomatic mixed precision (AMP)\tYes

Requirements

This repository contains Dockerfile which extends the Tensorflow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

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NVIDIA Docker
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\n
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TensorFlow NGC container
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\n
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Supported GPUs:
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NVIDIA Volta architecture
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NVIDIA Turing architecture
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NVIDIA Ampere architecture
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Follow the rest of the process from building the container to train and test the model in quick start guide section.

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Make a new folder using mkdir command\n2. Go to the new folder using cd command\n3. Use the wget command to download resources as a zip file inside the folder.\n4. Unzip the zip file using unzip \n\n\n```\nmkdir VAE\ncd VAE\nwget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/vae_for_tensorflow/versions/20.06.0/zip -O vae_for_tensorflow_20.06.0.zip\nunzip vae_for_tensorflow_20.06.0.zip\n\n```\n### Build ngc Container\nRun the next command to make a container using the Dockerfile inside the resource folder that you just downloaded.\ndocker build . -t < tagname>\nYou can tag the container with any name you want. Here we used vae tag.\n\n```\ndocker build . -t vae\n```\n### Run the Container\nRun the next command to start the container. In this run command, we mounted a folder for data inside the container. We will download a movie rating dataset inside this folder.\nBeside, we set port 8888:8888 for this container so we can have access to a jupyterlab inside the container later using this port.\n\n```\ndocker run -it --rm --runtime=nvidia -v /data/vae-cf:/data -p 8888:8888 vae /bin/bash\n\n```\n### Dataset\n\nWe used the MovieLens 20m dataset. The VA-CF model was trained on the MovieLens 20M dataset. MovieLens 20M is a movie rating dataset. It includes 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. the goal of our model is to predict the rate of a new movie for a user considering the previous sets of (movie, rate) of the user. The model will be trained using a dataset of the movie and the rate for the movie. After that, the trained model predicts the rate of a new movie for a user.\n\nIf you do not have the dataset downloaded, run commands below to download and extract the MovieLens dataset to the /data/ml-20m/extracted/ folder.\n\nThe dataset can be preprocessed simply by running: python prepare_dataset.py in the Docker container. By default, the dataset will be stored in the /data directory. If you want to store the data in a different location, you can pass the desired location to the --data_dir argument.\n\n```\nmkdir /data/ml-20m\nmkdir /data/ml-20m/extracted\nwget http://files.grouplens.org/datasets/movielens/ml-20m.zip\nunzip ml-20m.zip\n```\n\nIf you already have the dataset downloaded and unzipped elsewhere, run the below commands to first exit the current VAE-CF Docker container and then restart the VAE-CF Docker Container by mounting the MovieLens dataset location.\n\n```\nexit\ndocker run -it --rm --gpus all -v /data/vae-cf:/data -v :/data/ml-20m/extracted/ml-20m -p 8888:8888 vae /bin/bash\n\n```\n### Train and Test the Model Inside the Container\nNow you have a running container and a dataset inside the container.\nWe run the rest of commands using a jupyter notebook.\nOpen the jupyterlab inside the container.\nAfter that, upload VAE-model notebook and run the cells of the notebook to train, test and inference this model.\n\n```\njupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n```","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_85":232,"_113":233,"_114":-5,"_122":35,"_121":35,"_123":231,"_216":35},{"_85":131,"_113":131,"_114":-5,"_122":35,"_121":35,"_123":231,"_216":35},{"_85":29,"_113":29,"_114":-5,"_122":35,"_121":35,"_123":230,"_216":35},[],[],"-","Latest (first-upload)","nvidia/vae","key","nvidia/resources/vae","resourceId","vae","shortDescription","The Variational Autoencoder for collaborative filtering focuses on providing recommendations.","# Getting Start with Recommender System\n\n\nThe Variational Autoencoder (VAE) shown here is an optimized implementation of the architecture first described in Variational Autoencoders for Collaborative Filtering and can be used for recommendation tasks. The main differences between this model and the original one are the performance optimizations, such as using sparse matrices, mixed precision, larger mini-batches and multiple GPUs. These changes enabled us to achieve a significantly higher speed while maintaining the same accuracy. Because of our fast implementation, we've also been able to carry out an extensive hyperparameter search to slightly improve the accuracy metrics.\n\nBy using Variational Autoencoder for Collaborative Filtering (VAE-CF), you can quickly train a recommendation model for the collaborative filtering task. The required input data consists of pairs of user-item IDs for each interaction between a user and an item. With a trained model, you can run inference to predict what items a new user most likely to interact with.\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPUs. Therefore, researchers can get results 1.9x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n## Model\n\n\nThe model consists of two parts: the encoder and the decoder. The encoder transforms the vector, which contains the interactions for a specific user, into a n-dimensional variational distribution. We can use this variational distribution to obtain the latent representation of a user. This latent representation is then fed into the decoder. The result is a vector of item interaction probabilities for a particular user.\n\nThe dataset that we use for training this model is a movie rating dataset. The trained model predicts the rate of a movie for a user based on previous rates of the user for other movies. It predicts what is the probability that the user choose a movie.\n\n## Default Configuration\nThe following features were implemented in this model:\n\n- > Sparse matrix support\n- > Data-parallel multi-GPU training\n- > Dynamic loss scaling with backoff for tensor cores (mixed precision) training\n- > Feature support matrix\n\n### The following features are supported by this model:\n\nFeature\tVAE-CF\nHorovod Multi-GPU (NCCL)\tYes\nAutomatic mixed precision (AMP)\tYes\n\n## Requirements\nThis repository contains Dockerfile which extends the Tensorflow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n\n- > NVIDIA Docker\n- >TensorFlow NGC container\n- > Supported GPUs:\n> - > NVIDIA Volta architecture\n> - > NVIDIA Turing architecture\n> - > NVIDIA Ampere architecture\n\nFollow the rest of the process from building the container to train and test the model in quick start guide section.","displayName","Getting Start with Recommender System",["D",1680631927998],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/vae","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Tutorials.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[273,274,275,276],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_242":289,"_278":290,"_279":291,"_281":87},{"_242":287,"_278":287,"_279":288,"_281":87},{"_242":284,"_278":284,"_279":285,"_281":87},{"_242":277,"_278":277,"_279":280,"_281":87},"Tutorial","value","filter",{"_282":283,"_278":277},"category","field","type","TensorFlow",{"_282":286,"_278":284},"framework","Recommender system",{"_282":87,"_278":287},"DL","soln_dl",{"_282":292,"_278":290},"technology",{"_155":1468,"_157":1469},"ngcApps",[1421],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1402":1403,"_1404":1405,"_1406":1407,"_1408":1409,"_1410":304,"_109":328,"_1411":1412,"_1413":60,"_1414":90,"_1415":90,"_1416":1417,"_1418":90,"_1419":1420},"amplitude",{"_1399":1400,"_1002":-7,"_1401":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1393":1394,"_1395":1396,"_1397":1398},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1277":410,"_1278":1279,"_1280":1281,"_1282":1283},"announcements",[],"consentState",{"_367":1267,"_1268":90,"_1269":35,"_1270":35,"_1271":1272},"consentConfig",{"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368},"showContactAdmin",1784450728462,[],[],[342,343,344,345,346,347],{"_49":63,"_51":73,"_53":150,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":150,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":150,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":150,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":150,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":348,"_59":60},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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