diff --git a/tinygrad/device.py b/tinygrad/device.py
index 5acca1216b..a09fbdf724 100644
--- a/tinygrad/device.py
+++ b/tinygrad/device.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+import multiprocessing
 from collections import defaultdict
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, List, Optional, Dict, Tuple, ClassVar, cast
@@ -24,6 +25,7 @@ class _Device:
   @functools.lru_cache(maxsize=None)  # this class is a singleton, pylint: disable=method-cache-max-size-none
   def __get_canonicalized_item(self, ix:str) -> Compiled:
     if DEBUG >= 1: print(f"opening device {ix} from pid:{os.getpid()}")
+    assert multiprocessing.current_process().name == "MainProcess", "can only open device from parent process"
     x = ix.split(":")[0].upper()
     return [cls for cname, cls in inspect.getmembers(importlib.import_module(f'tinygrad.runtime.ops_{x.lower()}')) if (cname.lower() == x.lower() + "device") and x in self._devices][0](ix)  # noqa: E501
   @functools.cached_property
@@ -32,7 +34,9 @@ class _Device:
     if device_from_env: return device_from_env
     for device in ["METAL", "HSA", "CUDA", "GPU", "CLANG", "LLVM"]:
       try:
-        if self[device]: return device
+        if self[device]:
+          os.environ[device] = "1"   # we set this in environment for spawned children
+          return device
       except Exception: pass
     raise RuntimeError("no usable devices")
 Device = _Device()
diff --git a/tinygrad/engine/jit.py b/tinygrad/engine/jit.py
index a1cfae9da6..fbaae63aa8 100644
--- a/tinygrad/engine/jit.py
+++ b/tinygrad/engine/jit.py
@@ -150,7 +150,7 @@ class TinyJit(Generic[ReturnType]):
       # jit capture
       self.expected_names: List[Union[int, str]] = expected_names
       self.expected_lbs: List[Tuple[ShapeTracker, Tuple[Variable, ...], DType, str]] = expected_lbs
-      with Context(GRAPH=getenv("JITGRAPH", GRAPH.value)):
+      with Context(GRAPH=getenv("JITGRAPH", GRAPH.value), BEAM=getenv("JITBEAM", BEAM.value)):
         capturing.append(self)
         self.ret = self.fxn(*args, **kwargs)
         if len(params:=get_parameters(self.ret)): Tensor.realize(params[0], *params[1:])