Merge pull request #20 from guitared/main

Optimize dashboard and prevent sudo hang when password needed

README.md

@@ -12,20 +12,20 @@ This is a **research project**, not a production framework.
 
 The goal was to demonstrate that **training on the Apple Neural Engine — and potentially other NPUs — is possible**, and that the barrier has always been software support, not hardware capability. The ANE is a remarkably capable piece of silicon that Apple restricts to inference-only use through CoreML. This project bypasses that restriction using reverse-engineered private APIs to show what's possible when you give the hardware a chance.
 
-### What this project is
+### What This Project Is
 
 - A proof of concept for ANE training via `_ANEClient` and `_ANECompiler` private APIs
 - A set of benchmarks documenting real ANE performance characteristics (throughput, power, SRAM behavior)
 - A reference for anyone exploring direct ANE access outside CoreML
 - Research code that I update when I find something interesting
 
-### What this project is not
+### What This Project Is Not
 
 - A maintained framework or library
 - A replacement for CoreML, MLX, llama.cpp, or any production inference stack
 - A path to training large models on consumer hardware (yet)
 
-### On the hype
+### On The Hype
 
 Some coverage of this project has overstated its implications. To be clear:
 
@@ -37,7 +37,7 @@ The honest results — including all limitations — are documented in the accom
 - [Part 1: Reverse Engineering](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine)
 - [Part 2: Benchmarks](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine-615)
 
-### On maintenance
+### On Maintenance
 
 I don't intend to grow this into a large community project. My focus is on original research (compiler infrastructure for edge AI optimization), and maintaining an open-source framework takes time away from that.
 

training/dashboard.py

@@ -147,7 +147,7 @@ def softmax(x):
     e = np.exp(x)
     return e / np.sum(e)
 
-def generate_text(W, tok, max_tokens=64, temperature=0.8):
+def generate_text(W, max_tokens=64, temperature=0.8):
     tokenizer = get_tokenizer()
     if tokenizer is None:
         return '[no tokenizer]'
@@ -249,7 +249,7 @@ def generation_thread():
                 with S.gen_lock:
                     S.gen_status = 'idle'
                 continue
-            text = generate_text(W, get_tokenizer(), max_tokens=64, temperature=0.8)
+            text = generate_text(W, max_tokens=64, temperature=0.8)
             with S.gen_lock:
                 S.gen_text = text
                 S.gen_step = S.step
@@ -790,6 +790,8 @@ def spawn_training(resume=False, steps=10000, dynamic=False, ane=False, scratch=
     return proc
 
 def spawn_powermetrics():
+    if not sys.stdin.isatty():
+        return None
     try:
         proc = subprocess.Popen(
             ['sudo', 'powermetrics', '--samplers', 'cpu_power,gpu_power,ane_power', '-i', '1000'],
@@ -982,7 +984,7 @@ def main():
                             try:
                                 W = load_weights_from_ckpt(CKPT_PATH)
                                 if W:
-                                    text = generate_text(W, get_tokenizer(), max_tokens=64, temperature=0.8)
+                                    text = generate_text(W, max_tokens=64, temperature=0.8)
                                     with S.gen_lock:
                                         S.gen_text = text
                                         S.gen_step = S.step