[Ranking] Updating training scripts for ranking model.

PiperOrigin-RevId: 385629372

official/recommendation/ranking/README.md

@@ -111,6 +111,7 @@ export TPU_NAME=my-dlrm-tpu
 export EXPERIMENT_NAME=my_experiment_name
 export BUCKET_NAME="gs://my_dlrm_bucket"
 export DATA_DIR="${BUCKET_NAME}/data"
+export EMBEDDING_DIM=32
 
 python3 models/official/recommendation/ranking/train.py --mode=train_and_eval \
 --model_dir=${BUCKET_NAME}/model_dirs/${EXPERIMENT_NAME} --params_override="
@@ -126,8 +127,8 @@ task:
         global_batch_size: 16384
     model:
         num_dense_features: 13
-        bottom_mlp: [512,256,128]
-        embedding_dim: 128
+        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,
@@ -135,8 +136,8 @@ task:
             39979771, 25641295, 39664984, 585935, 12972, 108, 36]
 trainer:
     use_orbit: true
-    validation_interval: 90000
-    checkpoint_interval: 100000
+    validation_interval: 85352
+    checkpoint_interval: 85352
     validation_steps: 5440
     train_steps: 256054
     steps_per_loop: 1000
@@ -154,6 +155,8 @@ Training on GPUs are similar to TPU training. Only distribution strategy needs
 to be updated and number of GPUs provided (for 4 GPUs):
 
 ```shell
+export EMBEDDING_DIM=8
+
 python3 official/recommendation/ranking/train.py --mode=train_and_eval \
 --model_dir=${BUCKET_NAME}/model_dirs/${EXPERIMENT_NAME} --params_override="
 runtime: