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Ensire works on CUDA for extra speed

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This commit is contained in:
William Jeynes
2026-03-17 23:14:50 +00:00
parent 8052d5c7ba
commit 886b9a7d5d
1 changed files with 92 additions and 81 deletions
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supporting/RAGAS_Service/train_roberta.py
+44 -33
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@@ -113,56 +113,61 @@ def compute_metrics(eval_pred):
"recall": recall_score(labels, preds, average="weighted", zero_division=0),
}
texts, labels = load_dataset_from_csv("../../data/classify.csv")
def main():
torch.multiprocessing.set_start_method('fork')
print("CUDA available:", torch.cuda.is_available())
print("CUDA device count:", torch.cuda.device_count())
print("Current device:", torch.cuda.current_device() if torch.cuda.is_available() else "CPU")
texts, labels = load_dataset_from_csv("../../data/classify.csv")
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaForSequenceClassification.from_pretrained(
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaForSequenceClassification.from_pretrained(
model_name,
num_labels=NUM_CLASSES
)
)
for param in model.roberta.parameters():
for param in model.roberta.parameters():
param.requires_grad = False
for param in model.roberta.encoder.layer[-3:].parameters():
for param in model.roberta.encoder.layer[-3:].parameters():
param.requires_grad = True
print("Dataset size:", len(texts))
print("Label distribution:")
print(Counter(labels))
print("Dataset size:", len(texts))
print("Label distribution:")
print(Counter(labels))
train_texts, val_texts, train_labels, val_labels = train_test_split(
train_texts, val_texts, train_labels, val_labels = train_test_split(
texts,
labels,
test_size=0.2,
random_state=42
)
)
class_weights = compute_class_weight(
class_weights = compute_class_weight(
class_weight="balanced",
classes=np.unique(train_labels),
y=train_labels
)
)
class_weights = torch.tensor(class_weights, dtype=torch.float)
print("Class weights:", class_weights)
class_weights = torch.tensor(class_weights, dtype=torch.float)
print("Class weights:", class_weights)
train_encodings = tokenizer(
train_encodings = tokenizer(
train_texts,
truncation=True,
padding=True,
max_length=256
)
)
val_encodings = tokenizer(
val_encodings = tokenizer(
val_texts,
truncation=True,
padding=True,
max_length=256
)
)
class TextDataset(torch.utils.data.Dataset):
class TextDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
@@ -178,7 +183,7 @@ class TextDataset(torch.utils.data.Dataset):
def __len__(self):
return len(self.labels)
training_args = TrainingArguments(
training_args = TrainingArguments(
output_dir="./results",
learning_rate=1e-5,
per_device_train_batch_size=8,
@@ -189,28 +194,34 @@ training_args = TrainingArguments(
save_strategy="epoch",
metric_for_best_model="f1",
greater_is_better=True,
dataloader_pin_memory=False
)
dataloader_num_workers=4,
dataloader_pin_memory=True
)
train_dataset = TextDataset(train_encodings, train_labels)
train_dataset = TextDataset(train_encodings, train_labels)
val_dataset = TextDataset(val_encodings, val_labels)
val_dataset = TextDataset(val_encodings, val_labels)
trainer = WeightedTrainer(
trainer = WeightedTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
compute_metrics=compute_metrics,
class_weights=class_weights
)
)
trainer.train()
trainer.train()
metrics = trainer.evaluate()
print("Final evaluation metrics:")
for k, v in metrics.items():
metrics = trainer.evaluate()
print("Final evaluation metrics:")
for k, v in metrics.items():
print(f"{k}: {v}")
trainer.save_model("./roberta_classifier")
tokenizer.save_pretrained("./roberta_classifier")
trainer.save_model("./roberta_classifier")
tokenizer.save_pretrained("./roberta_classifier")
if __name__ == "__main__":
main()