Merlin: HugeCTR

HugeCTR is a GPU-accelerated recommender framework designed to distribute training across multiple GPUs and nodes and estimate Click-Through Rates (CTRs). HugeCTR supports model-parallel embedding tables and data-parallel neural networks and their variants such as Wide and Deep Learning (WDL), Deep Cross Network (DCN), DeepFM, and Deep Learning Recommendation Model (DLRM). HugeCTR is a component of NVIDIA Merlin Open Beta, which is used to build large-scale deep learning recommender systems. For additional information, see HugeCTR User Guide.

Design Goals:

• Fast: HugeCTR is a speed-of-light CTR model framework that can outperform popular recommender systems such as TensorFlow (TF).
• Efficient: HugeCTR provides the essentials so that you can efficiently train your CTR model.
• Easy: Regardless of whether you are a data scientist or machine learning practitioner, we've made it easy for anybody to use HugeCTR.

Table of Contents

• Core Features
• Getting Started
• Support and Feedback

Core Features

HugeCTR supports a variety of features, including the following:

• multi-node training
• mixed precision training
• SGD optimizer and learning rate scheduling
• model oversubscription

To learn about our latest enhancements, see our release notes.

Getting Started

If you'd like to quickly train a model using the Python interface, follow these steps:

1. Start a NGC container with your local host directory (/your/host/dir mounted) by running the following command:

   docker run --runtime=nvidia --rm -v /your/host/dir:/your/container/dir -w /your/container/dir -it -u $(id -u):$(id -g) -it nvcr.io/nvidia/merlin/merlin-training:0.5
   

   NOTE: The /your/host/dir directory is just as visible as the /your/container/dir directory. The /your/host/dir directory is also your starting directory.

2. Activate the merlin conda environment by running the following command:

   source activate merlin
   

3. Inside the container, copy the DCN configuration file to our mounted directory (/your/container/dir).

   This config file specifies the DCN model architecture and its optimizer. With any Python use case, the solver clause within the configuration file is not used at all.

4. Generate a synthetic dataset based on the configuration file by running the following command:

   ./data_generator --config-file dcn.json --voc-size-array 39884,39043,17289,7420,20263,3,7120,1543,39884,39043,17289,7420,20263,3,7120,1543,63,63,39884,39043,17289,7420,20263,3,7120,1543 --distribution powerlaw --alpha -1.2
   

5. Write a simple Python code using the hugectr module as shown here:

   # train.py
   import sys
   import hugectr
   from mpi4py import MPI
   
   def train(json_config_file):
     solver_config = hugectr.solver_parser_helper(batchsize = 16384,
                                                  batchsize_eval = 16384,
                                                  vvgpu = [[0,1,2,3,4,5,6,7]],
                                                  repeat_dataset = True)
     sess = hugectr.Session(solver_config, json_config_file)
     sess.start_data_reading()
     for i in range(10000):
       sess.train()
       if (i % 100 == 0):
         loss = sess.get_current_loss()
         print("[HUGECTR][INFO] iter: {}; loss: {}".format(i, loss))
   
   if __name__ == "__main__":
     json_config_file = sys.argv[1]
     train(json_config_file)
   
   

   NOTE: Update the vvgpu (the active GPUs), batchsize, and batchsize_eval parameters according to your GPU system.

6. Train the model by running the following command:

   python train.py dcn.json
   

For additional information, see the HugeCTR User Guide.

Support and Feedback

If you encounter any issues and/or have questions, please file an issue here so that we can provide you with the necessary resolutions and answers. To further advance the Merlin/HugeCTR Roadmap, we encourage you to share all the details regarding your recommender system pipeline using this survey.