show training speed

llama_lora/ui/finetune/style.css

@@ -315,6 +315,7 @@
     padding: var(--size-1) var(--size-2);
     font-size: var(--text-sm);
     font-family: var(--font-mono);
+    text-align: right;
 }
 #finetune_training_status .progress-block .status {
     white-space: pre-wrap;
@@ -328,6 +329,7 @@
     z-index: var(--layer-2);
     width: var(--size-full);
     padding: 8px 0;
+    text-align: center;
 }
 #finetune_training_status .progress-block .progress-level-inner {
     margin: var(--size-2) auto;

llama_lora/ui/finetune/training.py

@@ -398,10 +398,16 @@ def render_training_status():
     if time_remaining >= 0:
         meta_info.append(
             f"{format_time(time_elapsed)}<{format_time(time_remaining)}")
-        meta_info.append(f"ETA: {format_timestamp(Global.training_eta)}")
     else:
         meta_info.append(format_time(time_elapsed))
 
+    current_speed = Global.training_eta_predictor.get_current_speed()
+    if current_speed is not None:
+        meta_info.append(f"{current_speed:.2f}it/s")
+
+    if time_remaining >= 0:
+        meta_info.append(f"ETA: {format_timestamp(Global.training_eta)}")
+
     params_info_html = ""
     if Global.training_params_info_text:
         params_info_html = f"""

llama_lora/utils/eta_predictor.py

@@ -52,3 +52,18 @@ class ETAPredictor:
             print("Error predicting ETA:", e)
             traceback.print_exc()
             return None
+
+    def get_current_speed(self):
+        if len(self.data) < 5:
+            return None
+
+        last = self.data[-1]
+        sample = self.data[-5]
+        if len(self.data) > 100:
+            sample = self.data[-2]
+
+        steps_completed = last[0] - sample[0]
+        time_elapsed = last[1] - sample[1]
+        steps_per_second = steps_completed / time_elapsed
+
+        return steps_per_second