revert device_id -> device

bagustris · May 27, 2024 · b2c8bd4 · b2c8bd4
1 parent acc4516
commit b2c8bd4
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Showing 2 changed files with 8 additions and 11 deletions.
diff --git a/ini_file.md b/ini_file.md
@@ -299,10 +299,8 @@
   * 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. 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
+* **device**: For torch/huggingface models: select your GPU if you have one. Values are either "cpu" or GPU ids (e.g., 0, 1 or both "0,1"). By default, the GPU/CUDA is used if available, otherwise is CPU.
+  * device = 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. Default is facebook/wav2vec2-large-robust-ft-swbd-300h. 

diff --git a/nkululeko/models/model_tuned.py b/nkululeko/models/model_tuned.py
@@ -21,8 +21,6 @@
     Wav2Vec2PreTrainedModel,
 )
 
-from transformers import AutoConfig, AutoModel
-
 import nkululeko.glob_conf as glob_conf
 from nkululeko.models.model import Model as BaseModel
 from nkululeko.reporting.reporter import Reporter
@@ -43,24 +41,25 @@ 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")
+        # 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"] = self.device_id  # self.device
+            os.environ["CUDA_VISIBLE_DEVICES"] = self.device  # self.device
         self.df_train, self.df_test = df_train, df_test
         self.epoch_num = int(self.util.config_val("EXP", "epochs", 1))
 
         self._init_model()
 
     def _init_model(self):
         model_path = "facebook/wav2vec2-large-robust-ft-swbd-300h"
-        pretrained_model = self.util.config_val("MODEL", "pretrained_model", model_path)
+        pretrained_model = self.util.config_val(
+            "MODEL", "pretrained_model", model_path)
         self.num_layers = None
         self.sampling_rate = 16000
         self.max_duration_sec = 8.0
         self.accumulation_steps = 4
-        
+
         # print finetuning information via debug
         self.util.debug(f"Finetuning from model: {pretrained_model}")
 
@@ -382,7 +381,7 @@ def __init__(self, config):
             setattr(config, 'add_adapter', False)
 
         super().__init__(config)
-        
+
         self.wav2vec2 = Wav2Vec2Model(config)
         self.cat = ModelHead(config)
         self.init_weights()