Training DLRM and DCN on Cloud TPU (TF 2.x)

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This tutorial shows how to train DLRM and DCN v2 ranking models which can be used for tasks such as click-through rate (CTR) prediction. See the note in Set up to run the DLRM or DCN model to see how to set parameters to train either a DLRM or a DCN v2 ranking model.

The model inputs are numerical and categorical features, and output is a scalar (for example click probability). The model can be trained and evaluated on Cloud TPU. The deep ranking models are both memory intensive (for embedding tables and lookup) and compute intensive for deep networks (MLPs). TPUs are designed for both.

The model uses a TPUEmbedding layer for categorical features. TPU embedding supports large embedding tables with fast lookup, the size of embedding tables scales linearly with the size of a TPU pod. Up to 90 GB embedding tables can be used for TPU v3-8, 5.6 TB for a v3-512 Pod, and 22.4 TB for a v3-2048 TPU Pod.

The model code is in the TensorFlow Recommenders library, while input pipeline, configuration and training loop is described in the TensorFlow Model Garden.

Objectives

• Set up the training environment
• Run the training job using synthetic data
• Verify the output results

Costs

In this document, you use the following billable components of Google Cloud:

• Compute Engine
• Cloud TPU
• Cloud Storage

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Before you begin

Before starting this tutorial, check that your Google Cloud project is correctly set up.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

5. Make sure that billing is enabled for your Google Cloud project.

This walkthrough uses billable components of Google Cloud. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up your TPU resources you create when you've finished with them to avoid unnecessary charges.

Set up your resources

This section provides information on setting up Cloud Storage bucket, VM, and Cloud TPU resources used by this tutorial.

Important: Set up all resources (Compute Engine VM, Cloud TPU, and Cloud Storage bucket) in the same region or zone to reduce network latency and network costs. TPU VMs are located in specific zones. For more information, see TPU regions and zones.

1. Open a Cloud Shell window.

   Open Cloud Shell

2. Create a variable for your project's ID.

   export PROJECT_ID=project-id
   

3. Configure Google Cloud CLI to use the project where you want to create Cloud TPU.

   For more information on the gcloud command, see the Google Cloud CLI Reference.

   gcloud config set project ${PROJECT_ID}
   

   The first time you run this command in a new Cloud Shell VM, an Authorize Cloud Shell page is displayed. Click Authorize at the bottom of the page to allow gcloud to make API calls with your credentials.

4. Create a Service Account for the Cloud TPU project.

   gcloud beta services identity create --service tpu.googleapis.com --project $PROJECT_ID
   

   The command returns a Cloud TPU Service Account with following format:

   [e-mail]
   

5. Create a Cloud Storage bucket using the following command where the --location option specifies the region where the bucket should be created. See the types and zones for more details on zones and regions:

   gcloud storage buckets create gs://bucket-name --project=${PROJECT_ID} --location=europe-west4
   

   This Cloud Storage bucket stores the data you use to train your model and the training results. The gcloud compute tpus tpu-vm tool used in this tutorial sets up default permissions for the Cloud TPU Service Account you set up in the previous step. If you want finer-grain permissions, review the access level permissions.

   The bucket location must be in the same region as your Compute Engine (VM) and your Cloud TPU node.

6. Launch a Compute Engine VM and Cloud TPU using the gcloud command.

   $ gcloud compute tpus tpu-vm create dlrm-dcn-tutorial \
       --zone=europe-west4-a \
       --accelerator-type=v3-8 \
       --version=tpu-vm-tf-2.18.0-se

   Command flag descriptions

   zone

   The zone where you plan to create your Cloud TPU.

   accelerator-type

   The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.

   version

   The Cloud TPU software version.

   Note: The first time you run gcloud compute tpus tpu-vm on a project it can take several minutes to perform startup tasks such as SSH key propagation and API turnup.

7. Connect to the Compute Engine instance using SSH. When you are connected to the VM, your shell prompt changes from username@projectname to username@vm-name:

   gcloud compute tpus tpu-vm ssh dlrm-dcn-tutorial --zone=europe-west4-a
   

   Key Point: From this point on, a prefix of (vm) $ means you should run the command on the Compute Engine VM instance.

Set Cloud Storage bucket variables

Set up the following environment variables, replacing bucket-name with the name of your Cloud Storage bucket:

(vm)$ export STORAGE_BUCKET=gs://bucket-name
(vm)$ export PYTHONPATH="/usr/share/tpu/models/:${PYTHONPATH}"
(vm)$ export EXPERIMENT_NAME=dlrm-exp

Set an environment variable for the TPU name.

  (vm)$ export TPU_NAME=local
  

The training application expects your training data to be accessible in Cloud Storage. The training application also uses your Cloud Storage bucket to store checkpoints during training.

Set up to run the DLRM or DCN model with synthetic data

The model can be trained on various datasets. Two commonly used ones are Criteo Terabyte and Criteo Kaggle. This tutorial trains on synthetic data by setting the flag use_synthetic_data=True.

The synthetic dataset is only useful for understanding how to use a Cloud TPU and validating end-to-end performance. The accuracy numbers and saved model won't be meaningful.

Visit the Criteo Terabyte and Criteo Kaggle websites for information on how to download and preprocess these datasets.

Note: If you want to monitor the model's output and performance, follow the guide to setting up TensorBoard.

1. Install required packages.

   (vm)$ pip3 install tensorflow-recommenders
   (vm)$ pip3 install -r /usr/share/tpu/models/official/requirements.txt
   

2. Change to the script directory.

   (vm)$ cd /usr/share/tpu/models/official/recommendation/ranking

3. Run the training script. This uses a fake, Criteo-like dataset to train the DLRM model. The training takes approximately 20 minutes.

   Note: To train the DLRM model use dot product feature interaction, that is, interaction: 'dot'. To train the DCN v2 model use interaction: 'cross'

   export EMBEDDING_DIM=32
   
   python3 train.py --mode=train_and_eval \
        --model_dir=${STORAGE_BUCKET}/model_dirs/${EXPERIMENT_NAME} --params_override="
        runtime:
            distribution_strategy: 'tpu'
        task:
            use_synthetic_data: true
            train_data:
                input_path: '${DATA_DIR}/train/*'
                global_batch_size: 16384
            validation_data:
                input_path: '${DATA_DIR}/eval/*'
                global_batch_size: 16384
            model:
                num_dense_features: 13
                bottom_mlp: [512,256,${EMBEDDING_DIM}]
                embedding_dim: ${EMBEDDING_DIM}
                top_mlp: [1024,1024,512,256,1]
                interaction: 'dot'
                vocab_sizes: [39884406, 39043, 17289, 7420, 20263, 3, 7120, 1543, 63,
                    38532951, 2953546, 403346, 10, 2208, 11938, 155, 4, 976, 14,
                    39979771, 25641295, 39664984, 585935, 12972, 108, 36]
        trainer:
            use_orbit: false
            validation_interval: 1000
            checkpoint_interval: 1000
            validation_steps: 500
            train_steps: 1000
            steps_per_loop: 1000
        "
   

This training runs for approximately 10 minutes on a v3-8 TPU. When it completes, you will see messages similar to the following:

I0621 21:32:58.519792 139675269142336 tpu_embedding_v2_utils.py:907] Done with log of TPUEmbeddingConfiguration.
I0621 21:32:58.540874 139675269142336 tpu_embedding_v2.py:389] Done initializing TPU Embedding engine.
1000/1000 [==============================] - 335s 335ms/step - auc: 0.7360 - accuracy: 0.6709 - prediction_mean: 0.4984
- label_mean: 0.4976 - loss: 0.0734 - regularization_loss: 0.0000e+00 - total_loss: 0.0734 - val_auc: 0.7403
- val_accuracy: 0.6745 - val_prediction_mean: 0.5065 - val_label_mean: 0.4976 - val_loss: 0.0749
- val_regularization_loss: 0.0000e+00 - val_total_loss: 0.0749

Model: "ranking"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
tpu_embedding (TPUEmbedding) multiple                  1
_________________________________________________________________
mlp (MLP)                    multiple                  154944
_________________________________________________________________
mlp_1 (MLP)                  multiple                  2131969
_________________________________________________________________
dot_interaction (DotInteract multiple                  0
_________________________________________________________________
ranking_1 (Ranking)          multiple                  0
=================================================================
Total params: 2,286,914
Trainable params: 2,286,914
Non-trainable params: 0
_________________________________________________________________
I0621 21:43:54.977140 139675269142336 train.py:177] Train history: {'auc': [0.7359596490859985],
'accuracy': [0.67094486951828], 'prediction_mean': [0.4983849823474884], 'label_mean': [0.4975697994232178],
'loss': [0.07338511198759079], 'regularization_loss': [0], 'total_loss': [0.07338511198759079],
'val_auc': [0.7402724623680115], 'val_accuracy': [0.6744520664215088], 'val_prediction_mean': [0.5064718723297119],
'val_label_mean': [0.4975748658180237], 'val_loss': [0.07486172765493393],
'val_regularization_loss': [0], 'val_total_loss': [0.07486172765493393]}

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

1. Disconnect from the Compute Engine instance, if you have not already done so:

   (vm)$ exit
   

   Your prompt should now be username@projectname, showing you are in the Cloud Shell.

2. Delete your Cloud TPU resources.

   $ gcloud compute tpus tpu-vm delete dlrm-dcn-tutorial \
     --zone=europe-west4-a
   

3. Verify the resources have been deleted by running gcloud compute tpus tpu-vm list. The deletion might take several minutes. The output from the following command shouldn't include any of the resources created in this tutorial:

   $ gcloud compute tpus tpu-vm list --zone=europe-west4-a

4. Delete your Cloud Storage bucket using the gcloud CLI. Replace bucket-name with the name of your Cloud Storage bucket.

   $ gcloud storage rm gs://bucket-name --recursive
   

What's next

The TensorFlow Cloud TPU tutorials generally train the model using a sample dataset. The results of this training are not usable for inference. To use a model for inference, you can train the data on a publicly available dataset or your own dataset. TensorFlow models trained on Cloud TPUs generally require datasets to be in TFRecord format.

You can use the dataset conversion tool sample to convert an image classification dataset into TFRecord format. If you are not using an image classification model, you will have to convert your dataset to TFRecord format yourself. For more information, see TFRecord and tf.Example.

Hyperparameter tuning

To improve the model's performance with your dataset, you can tune the model's hyperparameters. You can find information about hyperparameters common to all TPU supported models on GitHub. Information about model-specific hyperparameters can be found in the source code for each model. For more information on hyperparameter tuning, see Overview of hyperparameter tuning and Tune hyperparameters.

Inference

Once you have trained your model, you can use it for inference (also called prediction). You can use the Cloud TPU inference converter tool to prepare and optimize a TensorFlow model for inference on Cloud TPU v5e. For more information about inference on Cloud TPU v5e, see Cloud TPU v5e inference introduction.