# tensorplay.cuda.graphs.make_graphed_callables

Source: https://www.tensorplay.cn/docs/generated/tensorplay.cuda.graphs.make_graphed_callables.html

```python
tensorplay.cuda.graphs.make_graphed_callables(callables, sample_args, num_warmup_iters=3, allow_unused_input=False, pool=None, capture_error_mode='global')
```

Callables that run per-iteration with CUDA graph capture.

forward (and backward, via [tensorplay.autograd.grad()](/docs/generated/tensorplay.autograd.grad.html#tensorplay.autograd.grad)) into CUDA
graphs sharing one private memory pool, then wraps them in autograd
Functions whose forward/backward are graph replays.  Per-iteration host
overhead drops to two graph launches.

carried over verbatim: sample_args must contain only Tensors whose
requires_grad matches the live workload; modules may not carry hooks
or trainable buffers; arguments must keep their order and shapes.

Parameters:

- callables – function or tensorplay.nn.Module, or a tuple of them in live-workload order.

- sample_args – matching tuple of argument-tuples of CUDA Tensors.

- num_warmup_iters – warmup iterations run on the capture stream before capturing (flushes lazy cuDNN/cuBLAS state).

- allow_unused_input – passed through to [tensorplay.autograd.grad()](/docs/generated/tensorplay.autograd.grad.html#tensorplay.autograd.grad).

- pool – share an existing graph pool instead of allocating one.