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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",[],{"_167":172,"_169":173,"_143":-5,"_149":25,"_151":25,"_162":171,"_153":25},{"_167":14,"_169":14,"_143":-5,"_149":25,"_151":25,"_162":171,"_153":25},{"_167":168,"_169":168,"_143":-5,"_149":25,"_151":25,"_162":170,"_153":25},"id","second-upload","name",[],[],"-","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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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. If you do not allow these cookies, then some or all of these services may not function properly.",[541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565],[512,513,514,515,516],{"_351":426,"_182":426,"_141":17,"_324":536},{"_351":400,"_182":400,"_141":17,"_324":531},{"_351":526,"_182":526,"_141":17,"_324":527},{"_351":521,"_182":521,"_141":17,"_324":522},{"_351":517,"_182":517,"_141":17,"_324":518},"nvidia.partners",[519],{"_169":520,"_357":517,"_358":25,"_141":394},"edge","app.hushly.com",[523],{"_169":524,"_357":521,"_358":25,"_141":525},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[528],{"_169":529,"_357":526,"_358":25,"_141":530},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653],[605,606,607,608],{"_351":620,"_182":620,"_141":17,"_324":621},{"_351":615,"_182":615,"_141":17,"_324":616},{"_351":413,"_182":413,"_141":17,"_324":612},{"_351":526,"_182":526,"_141":17,"_324":609},[610],{"_169":611,"_357":526,"_358":64,"_141":530},"_gh_sess",[613],{"_169":614,"_357":413,"_358":64,"_141":420},"YSC","bizible.com",[617],{"_169":618,"_357":615,"_358":25,"_141":619},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[622],{"_169":623,"_357":620,"_358":25,"_141":624},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_169":695,"_357":472,"_358":25,"_141":696},{"_169":680,"_357":694,"_358":64,"_141":681},{"_169":663,"_357":475,"_358":25,"_141":587},{"_169":663,"_357":575,"_358":25,"_141":587},{"_169":692,"_357":472,"_358":25,"_141":693},{"_169":680,"_357":478,"_358":64,"_141":681},{"_169":690,"_357":472,"_358":25,"_141":691},{"_169":680,"_357":475,"_358":64,"_141":681},{"_169":688,"_357":574,"_358":25,"_141":689},{"_169":680,"_357":596,"_358":64,"_141":681},{"_169":660,"_357":475,"_358":25,"_141":662},{"_169":686,"_357":472,"_358":64,"_141":687},{"_169":684,"_357":472,"_358":25,"_141":685},{"_169":682,"_357":472,"_358":64,"_141":683},{"_169":680,"_357":570,"_358":64,"_141":681},{"_169":678,"_357":472,"_358":25,"_141":679},{"_169":676,"_357":472,"_358":25,"_141":677},{"_169":674,"_357":472,"_358":64,"_141":675},{"_169":672,"_357":579,"_358":25,"_141":673},{"_169":660,"_357":570,"_358":25,"_141":662},{"_169":670,"_357":472,"_358":25,"_141":671},{"_169":668,"_357":472,"_358":64,"_141":669},{"_169":666,"_357":472,"_358":25,"_141":667},{"_169":664,"_357":472,"_358":25,"_141":665},{"_169":663,"_357":472,"_358":25,"_141":587},{"_169":660,"_357":661,"_358":25,"_141":662},{"_169":658,"_357":472,"_358":25,"_141":659},{"_169":656,"_357":472,"_358":64,"_141":657},{"_169":654,"_357":472,"_358":25,"_141":655},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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