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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:

\n
\n
Sparse matrix support
\n
\n
\n
\n
Data-parallel multi-GPU training
\n
\n
\n
\n
Dynamic loss scaling with backoff for tensor cores (mixed precision) training
\n
\n
\n
\n
Feature support matrix
\n
\n

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:

\n
\n
NVIDIA Docker
\n
\n
\n
\n
TensorFlow NGC container
\n
\n
\n
\n
Supported GPUs:
\n
\n

\n
\n
\n
\n
NVIDIA Volta architecture
\n
\n
\n
\n
\n
NVIDIA Turing architecture
\n
\n
\n
\n
\n
NVIDIA Ampere architecture
\n
\n
\n
\n

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","policies",[],{"_75":194,"_77":195,"_166":-5,"_172":30,"_174":30,"_186":193,"_176":30},{"_75":192,"_77":192,"_166":-5,"_172":30,"_174":30,"_186":193,"_176":30},{"_75":21,"_77":21,"_166":-5,"_172":30,"_174":30,"_186":191,"_176":30},[],"first-upload",[],"-","Latest (first-upload)","nvidia/vae","key","nvidia/resources/vae","resourceType","resources","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. 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.","nvidiacorp.us-5.evergage.com",[940,941],{"_77":943,"_379":938,"_380":30,"_164":414},{"_77":942,"_379":938,"_380":30,"_164":414},"AWSALBTG","AWSALBTGCORS",[945,946],{"_77":948,"_379":580,"_380":30,"_164":949},{"_77":947,"_379":580,"_380":30,"_164":414},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.","app.hushly.com",[952,953,954],{"_77":957,"_379":950,"_380":30,"_164":414},{"_77":956,"_379":950,"_380":85,"_164":414},{"_77":955,"_379":950,"_380":30,"_164":414},"AWSALBCORS","JSESSIONID","AWSALB",[959],{"_77":911,"_379":405,"_380":30,"_164":912},"hcaptcha.com",[962],{"_77":911,"_379":960,"_380":30,"_164":912},"tracking.intentsify.io",[965],{"_77":966,"_379":963,"_380":30,"_164":967},"userId","Used to track usage of the embedded video player.","static.reo.dev",[970],{"_77":971,"_379":968,"_380":30,"_164":972},"cookietest","Common cookie name could have a number of different origins. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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