Encode numpy arrays

tsinfer/inference.py

@@ -945,13 +945,27 @@ def common_params(self) -> dict:
         }
 
     def save(self, path):
+        def numpy_encoder(obj):
+            if isinstance(obj, np.ndarray):
+                return {
+                    "__numpy__": True,
+                    "data": obj.tolist(),
+                    "dtype": str(obj.dtype),
+                }
+            return obj
+
         with open(path, "w") as f:
-            json.dump(dataclasses.asdict(self), f, indent=2)
+            json.dump(dataclasses.asdict(self), f, indent=2, default=numpy_encoder)
 
     @classmethod
     def load(cls, path):
+        def numpy_decoder(dct):
+            if "__numpy__" in dct:
+                return np.array(dct["data"], dtype=dct["dtype"])
+            return dct
+
         with open(path) as f:
-            wd_dict = json.load(f)
+            wd_dict = json.load(f, object_hook=numpy_decoder)
         return cls(**wd_dict)
 
 
@@ -1048,7 +1062,7 @@ def match_samples_batch_init(
     sample_times = sample_times.tolist()
     wd.sample_indexes = sample_indexes
     wd.sample_times = sample_times
-    num_samples_per_partition = min_work_per_job // variant_data.num_sites
+    num_samples_per_partition = int(min_work_per_job // variant_data.num_sites)
     if num_samples_per_partition == 0:
         num_samples_per_partition = 1
     wd.num_samples_per_partition = num_samples_per_partition