Update checkpoint.py

checkpoint.py

@@ -42,7 +42,6 @@ sys.modules['__main__'].QuantizedWeight8bit = QuantizedWeight8bit
 @contextlib.contextmanager
 def copy_to_shm(file: str):
     if file.startswith("/dev/shm/"):
-        # Nothing to do, the file is already in shared memory.
         yield file
         return
 
@@ -81,7 +80,6 @@ def fast_pickle(obj: Any, path: str) -> None:
 
 
 def load_tensors(shaped_arrays, directory, mesh_config, tensor_indices=None):
-    """Loads a set of arrays."""
     pool = ThreadPoolExecutor(max_workers=32)
     fs = list()
     num_tensors = 0
@@ -124,13 +122,11 @@ def get_load_path_str(
     load_rename_rules: Optional[list[tuple[str, str]]] = None,
     load_exclude_rules: Optional[list[str]] = None,
 ) -> Optional[str]:
-    # Exclusion
     if load_exclude_rules is not None:
         for search_pattern in load_exclude_rules:
             if re.search(search_pattern, init_path_str):
                 return None
 
-    # Renaming
     load_path_str = init_path_str
     if load_rename_rules is not None:
         for search_pattern, replacement_pattern in load_rename_rules:
@@ -197,7 +193,6 @@ def restore(
 
     state = jax.tree_util.tree_unflatten(structure, loaded_tensors)
 
-    # Sanity check to give a better error message.
     ckpt_keys = set(state.params.keys())
     code_keys = set(state_sharding.params.keys())
 
@@ -219,3 +214,71 @@ def restore(
     if params_only:
         state = state.params
     return state
+
+# Database and machine learning integration
+import sqlite3
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import pandas as pd
+
+def create_database():
+    conn = sqlite3.connect('data_analysis.db')
+    c = conn.cursor()
+    c.execute('''CREATE TABLE IF NOT EXISTS data (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
+                    latency REAL,
+                    packet_loss REAL)''')
+    conn.commit()
+    conn.close()
+
+def record_data(latency, packet_loss):
+    conn = sqlite3.connect('data_analysis.db')
+    c = conn.cursor()
+    c.execute('INSERT INTO data (latency, packet_loss) VALUES (?, ?)', (latency, packet_loss))
+    conn.commit()
+    conn.close()
+
+def train_model():
+    conn = sqlite3.connect('data_analysis.db')
+    data = pd.read_sql_query("SELECT * FROM data", conn)
+    conn.close()
+
+    X = data[['latency']]
+    y = data['packet_loss']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    return model
+
+def analyze_task_startup(latency, model):
+    predicted_packet_loss = model.predict([[latency]])[0]
+    if predicted_packet_loss > 10:
+        print("High packet loss predicted: ", predicted_packet_loss)
+    else:
+        print("Packet loss within acceptable range: ", predicted_packet_loss)
+
+def join_data_with_external_source():
+    external_data = pd.DataFrame({
+        'external_id': [1, 2, 3],
+        'external_info': ['info1', 'info2', 'info3']
+    })
+
+    conn = sqlite3.connect('data_analysis.db')
+    data = pd.read_sql_query("SELECT * FROM data", conn)
+    conn.close()
+
+    joined_data = data.merge(external_data, left_on='id', right_on='external_id')
+    return joined_data
+
+if __name__ == "__main__":
+    create_database()
+    record_data(50, 5)  # Example data
+    record_data(100, 20)  # Example data
+
+    model = train_model()
+    analyze_task_startup(70, model)
+    joined_data = join_data_with_external_source()
+    print(joined_data)
+