https://github.com/jblaschke/pynvtx
Thin pybind11 wrapper for NVTX wrappers -- with some bells and whistles attached.
https://github.com/jblaschke/pynvtx
cuda nvtx nvtx-markers
Last synced: about 2 months ago
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Thin pybind11 wrapper for NVTX wrappers -- with some bells and whistles attached.
- Host: GitHub
- URL: https://github.com/jblaschke/pynvtx
- Owner: JBlaschke
- License: other
- Created: 2020-12-01T19:49:46.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-05-06T17:13:19.000Z (over 4 years ago)
- Last Synced: 2025-06-16T01:50:25.906Z (4 months ago)
- Topics: cuda, nvtx, nvtx-markers
- Language: Python
- Homepage:
- Size: 56.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyNVTX
A thin python wrapper for the nvToolsExt (NVTX) library, using pybind11. This
wrapper is meant to be as thin as possible -- so only provides minimal support.
Currently supported features are:
1. NVTX annotations: `nvtxRangePushA` and `nvtxRangePop`
2. Function decorator: `PyNVTX.annotate`
3. Automatic decorator generation `PyNVTX.annotate_all_methods()`
## Installation
Ensure that the `nvcc` is in your `PATH` -- or alternatively ensure that the
`CUDAHOME` environment variable points to your local CUDA install. To install,
either
```
pip install PyNVTX
```
or clone this repo and
```
python setup.py install
```
### This Won't Break If You Don't Have CUDA
You know what would suck? If including `PyNVTX` required CUDA to be installed?
Why? There are loads of applications that support CUDA, if available. And
default to the CPU-only version otherwise. `PyNVTX` is the same. If `nvcc` in
not in your `PATH` (nor in your `CUDAHOME`), then you'll see this warning:
```
*** WARNING: The nvcc binary could not be located in your $PATH. Either add it to your path, or set $CUDAHOME
```
(note that it will not warn you if you're installing with pip) and `PyNVTX`
will still install (it just won't do anything). You can check if a local
version of `PyNVTX` has been built with CUDA support by checking:
```python
PyNVTX.cuda_enabled # True if compiled with CUDA support
```
## NVTX Markers (`nvtxRangePushA` / `nvtxRangePop`)
```python
import PyNVTX as nvtx
nvtx.RangePushA("Doing some work")
# code to time goes here
nvtx.RangePop()
```
### Function Decorator
The `PyNVTX.annotate` will put `RangePushA` and `RangePop` the the beginning and of
the function call:
```python
import PyNVTX as nvtx
@nvtx.annotate("test_function")
def test():
# You code goes here
```
### Automatic Instrumentation
The `PyNVTX.annotate_all_methods` will automatically decorate all methods in a
class, as well as all methods it inherits. A guard prevents accidentally
decorating any method twice. Eg.:
```python
import PyNVTX as nvtx
class MyClassA(object):
def __init__(self):
pass
def f(self):
pass
class MyClassB(MyClassA):
def __init__(self):
pass
def g(self):
pass
nvtx.annotate_all_methods(MyClassB)
```
Will instrument `MyClassB`'s `__init__`, as well as `f` and `g`, but _not_
`MyClassA`'s `__init__`.
Adding a class/method name to `PyNVTX.REGISTRY` will prevent it from being
instrumented by `PyNVTX.annotate_all_methods`. For example:
```python
nvtx.REGISTRY.add(MyClassB, "f") # note the method name is a string
nvtx.annotate_all_methods(MyClassB)
```
will not instrument `f`.
## Example Code
To get you started, take a look at `test/test-nvtx.py`