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authorYong He <yonghe@outlook.com>2023-03-28 16:55:34 -0700
committerGitHub <noreply@github.com>2023-03-28 16:55:34 -0700
commit0ea68f28e7b7aa4086278dd181ddf247c00faade (patch)
treedccf6b08de1ca03dfbb0c6436107e557048c1c90
parent1f9f6045d2b83d8b6bddf30315e00231364a1bb3 (diff)
Update a1-02-slangpy.md
-rw-r--r--docs/user-guide/a1-02-slangpy.md7
1 files changed, 4 insertions, 3 deletions
diff --git a/docs/user-guide/a1-02-slangpy.md b/docs/user-guide/a1-02-slangpy.md
index ca0613b1b..6faf6ad47 100644
--- a/docs/user-guide/a1-02-slangpy.md
+++ b/docs/user-guide/a1-02-slangpy.md
@@ -77,7 +77,7 @@ To do so, we use a python package called `slangpy`. You can obtain it with
pip install slangpy
```
-With that, you can use the following code code call Slang function from Python:
+With that, you can use the following code to call `square_fwd` from Python:
```python
import torch
@@ -100,11 +100,12 @@ Y = tensor([[0.0198, 0.4349],
```
And that's it! `slangpy.loadModule` uses JIT compilation to compile your Slang source into CUDA binary.
-It may take a little longer the first time you execute the script, but the result will be cached and as
+It may take a little longer the first time you execute the script, but the compiled binaries will be cached and as
long as the kernel code is not changed, future runs will not rebuild the CUDA kernel.
Because the PyTorch JIT system requires `ninja`, you need to make sure `ninja` is installed on your system
-and is discoverable from the current environment.
+and is discoverable from the current environment, you also need to have a C++ compiler available on the system.
+On Windows, this means that Visual Studio need to be installed.
### Exposing an automatically differentiated kernel to PyTorch