| [project] | |
| name = "deit-tiny-int8-xnnpack-executorch-runtime" | |
| version = "0.1.0" | |
| description = "Runtime dependencies for example.py (DeiT-Tiny INT8, ExecuTorch + XNNPACK)" | |
| requires-python = ">=3.13,<3.14" | |
| dependencies = [ | |
| "executorch==1.1.0", | |
| "torch==2.10.0", | |
| "torchvision==0.25.0", | |
| "pillow==12.3.0", | |
| ] | |
| [tool.uv] | |
| package = false | |
| # The example only runs on Arm-based Edge Linux; it was measured on a Raspberry | |
| # Pi 5 (aarch64, Debian 13 trixie). Restricting the resolution environment keeps | |
| # the lock to the wheels that device actually installs. | |
| environments = ["sys_platform == 'linux' and platform_machine == 'aarch64'"] | |
| # coremltools targets Apple Core ML, which this XNNPACK-on-Arm-Linux example | |
| # never uses. Nothing in example.py imports it. | |
| exclude-dependencies = [ | |
| { package = { name = "executorch", version = "1.1.0" }, dependencies = ["coremltools"] }, | |
| ] | |
| [tool.ai-portal.deployment] | |
| schema-version = "1" | |
| runtime = "executorch" | |
| required-capabilities = ["XnnpackBackend"] | |
| [tool.ai-portal.deployment.ubuntu] | |
| packages = [] | |
| [tool.ai-portal.deployment.raspbian] | |
| packages = [] | |
| [tool.ai-portal.deployment.files] | |
| expected = ["predictions.json"] | |