To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the VAE-CF model on the MovieLens 20m dataset. For the specifics concerning training and inference, see the Advanced section.

1. Clone the repository. git clone https://github.com/NVIDIA/DeepLearningExamples cd DeepLearningExamples/Tensorflow/Recommendation/VAE_CF

2. Build the VAE TensorFlow NGC container.

   docker build . -t vae
   

3. Launch the VAE-CF TensorFlow Docker container.

   docker run -it --rm --runtime=nvidia -v /data/vae-cf:/data vae /bin/bash
   

4. Downloading the dataset: Here we use the MovieLens 20m dataset.

   • If you do not have the dataset downloaded: Run the commands below to download and extract the MovieLens dataset to the /data/ml-20m/extracted/ folder.

     cd /data
     mkdir ml-20m
     cd ml-20m
     mkdir extracted
     cd extracted
     wget http://files.grouplens.org/datasets/movielens/ml-20m.zip
     unzip ml-20m.zip
     

   • If 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 (like in Step 3 above) by mounting the MovieLens dataset location

     exit
     docker run -it --rm --runtime=nvidia -v /data/vae-cf:/data -v <ml-20m folder path>:/data/ml-20m/extracted/ml-20m vae /bin/bash
     

     where, the unzipped MovieLens dataset is at <ml-20m folder path>

5. Prepare the dataset.

   python prepare_dataset.py
   

6. Start training on 8 GPUs.

   mpirun --bind-to numa --allow-run-as-root -np 8 -H localhost:8 python main.py --train --amp --checkpoint_dir ./checkpoints
   

7. Start validation/evaluation.

   The model is exported to the default model_dir and can be loaded and tested using:

   python main.py --test --amp --checkpoint_dir ./checkpoints