| # Environment |
|
|
| ## Validated Jittor setup |
|
|
| | Component | Version | |
| |---|---| |
| | Python | 3.10 | |
| | Jittor | 1.3.8.5 | |
| | NumPy | 1.26.4 | |
| | CUDA toolchain | Jittor CUDA 11.2 + cuDNN 8 | |
| | C++ compiler | g++-10 | |
|
|
| Install the Python dependencies and expose the repository package: |
|
|
| ```bash |
| pip install -r requirements.txt |
| export cc_path=/usr/bin/g++-10 |
| export PYTHONPATH="$PWD:$PWD/python" |
| ``` |
|
|
| Jittor compiles operators on first use, so the first model construction can |
| take several minutes. |
|
|
| ## Compatibility notes |
|
|
| ### NumPy |
|
|
| Keep NumPy at 1.26.4. Jittor 1.3.8.5 combined with NumPy 2.x can produce |
| incorrect values for operations consuming NumPy-backed arrays without raising |
| an exception. Verify the installation with: |
|
|
| ```bash |
| python - <<'PY' |
| import jittor as jt |
| x = jt.float32([1, 2, 3]) |
| assert (x + x).numpy().tolist() == [2.0, 4.0, 6.0] |
| print('Jittor array check passed') |
| PY |
| ``` |
|
|
| ### Compiler |
|
|
| Jittor's CUDA 11.2 frontend is incompatible with newer system compiler headers |
| on some Linux distributions. The validated toolchain uses g++-10 selected by |
| the lowercase `cc_path` environment variable. |
|
|
| ### Reference environment |
|
|
| Regenerating PyTorch goldens or using the reference DOTA merge metric requires |
| the original Point2RBox-v3 environment: PyTorch 2.2, torchvision 0.17, |
| mmengine 0.10.7, mmcv 2.2.0, mmdet 3.3.0 and the reference mmrotate package. |
| Regular Jittor training and inference do not require this second environment. |
|
|
