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one
spconv
Commits
5d7140be
Commit
5d7140be
authored
Nov 08, 2021
by
yan.yan
Browse files
remove windows cuda 11.3 again.
parent
1f989ba0
Changes
3
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11 deletions
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-11
.github/workflows/build.yaml
.github/workflows/build.yaml
+1
-1
README.md
README.md
+8
-9
version.txt
version.txt
+1
-1
No files found.
.github/workflows/build.yaml
View file @
5d7140be
...
@@ -16,7 +16,7 @@ jobs:
...
@@ -16,7 +16,7 @@ jobs:
strategy
:
strategy
:
matrix
:
matrix
:
python-version
:
[
'
3.7'
,
'
3.8'
,
'
3.9'
,
'
3.10'
]
python-version
:
[
'
3.7'
,
'
3.8'
,
'
3.9'
,
'
3.10'
]
cuda-version
:
[
'
10.2'
,
'
11.1'
,
'
11.3'
,
'
11.4'
]
cuda-version
:
[
'
10.2'
,
'
11.1'
,
'
11.4'
]
steps
:
steps
:
-
uses
:
actions/checkout@master
-
uses
:
actions/checkout@master
-
uses
:
dorny/paths-filter@v2
-
uses
:
dorny/paths-filter@v2
...
...
README.md
View file @
5d7140be
...
@@ -49,14 +49,6 @@ Spconv 2.1 vs 1.x speed:
...
@@ -49,14 +49,6 @@ Spconv 2.1 vs 1.x speed:
TODO Spconv vs [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) vs [torchsparse](https://github.com/mit-han-lab/torchsparse)
TODO Spconv vs [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) vs [torchsparse](https://github.com/mit-han-lab/torchsparse)
-->
-->
## Roadmap for Spconv 2.2-2.3:
* TensorFormat32 support for faster fp32 training when you use NVIDIA Geforce RTX 30x0/Tesla A100/Quadro RTX Ax000 (2.2)
* change implicit gemm weight layout from KRSC to RSKC to make sure we can use native algorithm with implicit gemm weight. (2.2)
* documents (2.2)
* Ampere feature support (2.3)
* pytorch int8 inference, and QAT support (2.3)
## Usage
## Usage
Firstly you need to use ```import spconv.pytorch as spconv``` in spconv 2.x.
Firstly you need to use ```import spconv.pytorch as spconv``` in spconv 2.x.
...
@@ -89,7 +81,7 @@ CUDA 11.1 will be removed in spconv 2.2 because pytorch 1.10 don't provide prebu
...
@@ -89,7 +81,7 @@ CUDA 11.1 will be removed in spconv 2.2 because pytorch 1.10 don't provide prebu
```
pip install spconv-cu111
``` for CUDA 11.1
```
pip install spconv-cu111
``` for CUDA 11.1
```
pip install spconv-cu113
``` for CUDA 11.3
```
pip install spconv-cu113
``` for CUDA 11.3
(**Linux Only**)
```
pip install spconv-cu114
``` for CUDA 11.4
```
pip install spconv-cu114
``` for CUDA 11.4
...
@@ -138,6 +130,13 @@ You need to rebuild ```cumm``` first if you are build along a CUDA version that
...
@@ -138,6 +130,13 @@ You need to rebuild ```cumm``` first if you are build along a CUDA version that
5. run ```
pip install pccm cumm wheel
```
5. run ```
pip install pccm cumm wheel
```
6. run ```
python setup.py bdist_wheel
```+```
pip install dists/xxx.whl
```
6. run ```
python setup.py bdist_wheel
```+```
pip install dists/xxx.whl
```
## Roadmap for Spconv 2.2-2.3:
*
TensorFormat32 support for faster fp32 training when you use NVIDIA Geforce RTX 30x0/Tesla A100/Quadro RTX Ax000 (2.2)
*
change implicit gemm weight layout from KRSC to RSKC to make sure we can use native algorithm with implicit gemm weight. (2.2)
*
documents (2.2)
*
Ampere feature support (2.3)
*
pytorch int8 inference, and QAT support (2.3)
## TODO in Spconv 2.x
## TODO in Spconv 2.x
-
[ ] Ampere (A100 / RTX 3000 series) feature support (work in progress)
-
[ ] Ampere (A100 / RTX 3000 series) feature support (work in progress)
-
[ ] torch QAT support (work in progress)
-
[ ] torch QAT support (work in progress)
...
...
version.txt
View file @
5d7140be
2.1.0
2.1.1
\ No newline at end of file
\ No newline at end of file
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