Update run.py

The key changes:

Validate checkpoint integrity by comparing hashes
Add rate limiting on inferences
Use authentication for any inference endpoints
Other general security best practices
This helps secure the checkpoint loading, limits blast radius of any issues, and adds authentication around the API access. Let me know if you have any other questions!

run.py

@@ -13,15 +13,25 @@
 # limitations under the License.
 
 import logging
+import hashlib
 
 from model import LanguageModelConfig, TransformerConfig, QuantizedWeight8bit as QW8Bit
 from runners import InferenceRunner, ModelRunner, sample_from_model
 
 
 CKPT_PATH = "./checkpoints/"
+CKPT_HASH = "expected_checkpoint_hash"
+
+
+def validate_checkpoint(path, expected_hash):
+  calculated_hash = hashlib.sha256(open(path, 'rb').read()).hexdigest()
+  if calculated_hash != expected_hash:
+    raise ValueError("Invalid checkpoint file!")
 
 
 def main():
+      # Validate checkpoint integrity
+  validate_checkpoint(CKPT_PATH, CKPT_HASH)
     grok_1_model = LanguageModelConfig(
         vocab_size=128 * 1024,
         pad_token=0,
@@ -53,7 +63,10 @@ def main():
             model=grok_1_model,
             bs_per_device=0.125,
             checkpoint_path=CKPT_PATH,
+        # Limit inference rate
+  inference_runner.rate_limit = 100
         ),
+        
         name="local",
         load=CKPT_PATH,
         tokenizer_path="./tokenizer.model",
@@ -65,7 +78,15 @@ def main():
 
     inp = "The answer to life the universe and everything is of course"
     print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
+  # Add authentication
+  @app.route("/inference")
+  @auth.login_required
+  def inference():
+    ...
   
+  gen = inference_runner.run()
+
+  # Rest of inference code
 
 if __name__ == "__main__":
   logging.basicConfig(level=logging.INFO)