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.

```bash
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 :/data/ml-20m/extracted/ml-20m vae /bin/bash

where, the unzipped MovieLens dataset is at ``````

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