| commit | 79ceb4c45c5cd9223dd4bed8701cdf6a0da9cc9f | [log] [tgz] |
|---|---|---|
| author | Lucas Chollet <lucas.chollet@free.fr> | Mon Aug 12 11:58:35 2024 -0400 |
| committer | GitHub <noreply@github.com> | Mon Aug 12 15:58:35 2024 +0000 |
| tree | 3aacf58afba61dc408e54551b1f633516e587b73 | |
| parent | d3475aa77621545b7b0ef585f38fb8634c7ee766 [diff] |
build: update python_rules to support Python 3.12 (#2654) Before, when trying to use to build the project using Bazel, this would result in multiple occurrence of this error: `AttributeError: module 'pkgutil' has no attribute 'ImpImporter'. Did you mean: 'zipimporter'?` This error is due to `rules_python` being too old. While building with Python3.12 still result in an error, the new one clearly indicate is mismatch of the TensorFlow version. Very similar/related: https://github.com/googleapis/gapic-generator-python/pull/1825 BUG=https://github.com/tensorflow/tensorflow/issues/73174
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on DSPs, microcontrollers and other devices with limited memory.
Additional Links:
| Build Type | Status |
|---|---|
| CI (Linux) |  |
| Code Sync |  |
This table captures platforms that TFLM has been ported to. Please see New Platform Support for additional documentation.
| Platform | Status |
|---|---|
| Arduino |   |
| Coral Dev Board Micro | TFLM + EdgeTPU Examples for Coral Dev Board Micro |
| Espressif Systems Dev Boards |  |
| Renesas Boards | TFLM Examples for Renesas Boards |
| Silicon Labs Dev Kits | TFLM Examples for Silicon Labs Dev Kits |
| Sparkfun Edge |  |
| Texas Instruments Dev Boards |  |
This is a list of targets that have optimized kernel implementations and/or run the TFLM unit tests using software emulation or instruction set simulators.
| Build Type | Status |
|---|---|
| Cortex-M |  |
| Hexagon |  |
| RISC-V |  |
| Xtensa |  |
| Generate Integration Test |  |
See our contribution documentation.
A Github issue should be the primary method of getting in touch with the TensorFlow Lite Micro (TFLM) team.
The following resources may also be useful:
SIG Micro email group and monthly meetings.
SIG Micro gitter chat room.
For questions that are not specific to TFLM, please consult the broader TensorFlow project, e.g.: