feat(phase-00/03): add mps check script

phases/00-setup-and-tooling/03-gpu-setup-and-cloud/code/mps_check.py

@@ -0,0 +1,50 @@
+import torch
+import time
+import psutil
+import platform
+
+def check_mps():
+    print("====MPS Check====\n")
+    print(f"pytorch_version: {torch.__version__}")
+    print(f"mps_available: {torch.backends.mps.is_available()}")
+
+    if not torch.backends.mps.is_available():
+        print("\nNo MPS detected. That's fine for most lessons.")
+        print("For GPU-heavy lessons, use Google Colab (free).")
+        return
+
+    print(f"MPS device: Apple {platform.processor()} (MPS backend)")
+
+
+    print("\n=== CPU vs MPS Benchmark ===\n")
+    size = 4000
+    a = torch.randn(size, size)
+    b = torch.randn(size, size)
+
+
+    start = time.time()
+    _ = a @ b
+    cpu_time = time.time() - start
+    print(f"CPU matrix multiply ({size}x{size}): {cpu_time:.3f}s")
+
+    if torch.backends.mps.is_available():
+        a_mps = a.to("mps")
+        b_mps = b.to("mps")
+        torch.mps.synchronize()
+
+        start = time.time()
+        _ = a_mps @ b_mps
+        torch.mps.synchronize()
+        mps_time = time.time() - start
+        print(f"MPS matrix multiply ({size}x{size}): {mps_time:.3f}s")
+        print(f"Speedup: {cpu_time / mps_time:.0f}x")
+
+        total_ram = psutil.virtual_memory().total
+        vram_gb = total_ram / 1e9
+        params_fp16 = vram_gb * 1e9 / 2
+        params_billions = params_fp16 / 1e9
+        print(f"\nEstimated max model size (fp16): ~{params_billions:.0f}B parameters")
+
+
+if __name__ == "__main__":
+    check_mps()