add device_id param

ini_file.md

@@ -299,8 +299,10 @@
   * batch_size = 8
 * **num_workers**: Number of parallel processes for neural nets
   * num_workers = 5
-* **device**: For torch/huggingface models: select your GPU if you have one
+* **device**: For torch/huggingface models: select your GPU if you have one. Values are either "cpu" or "cuda".
   * device = cpu
+* **device_ids**: For torch/huggingface models: select your GPU if you have multiple. Values are GPU ids (0, 1 or both "0,1").
+  * device_id = 0
 * **patience**: Number of epochs to wait if the result gets better (for early stopping)
   * patience = 5
 * **pretrained_model**: Base model for finetuning/transfer learning. Variants of wav2vec2, Hubert, and WavLM are tested to work.

nkululeko/models/model_tuned.py

@@ -43,10 +43,11 @@ def __init__(self, df_train, df_test, feats_train, feats_test):
         # device = self.util.config_val("MODEL", "device", "cpu")
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         self.batch_size = int(self.util.config_val("MODEL", "batch_size", "8"))
+        self.device_id = self.util.config_val("MODEL", "device_id", "0")
         if self.device != "cpu":
             self.util.debug(f"running on device {self.device}")
             os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-            os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # self.device
+            os.environ["CUDA_VISIBLE_DEVICES"] = self.device_id  # self.device
         self.df_train, self.df_test = df_train, df_test
         self.epoch_num = int(self.util.config_val("EXP", "epochs", 1))
 

tests/exp_emodb_finetune.ini

@@ -1,6 +1,6 @@
 [EXP]
 root = ./tests/results/
-name = test_pretrain
+name = test_pretrain_1
 runs = 1
 epochs = 10
 save = True