openpilot/tinygrad_repo/examples/mlperf
2025-11-01 12:00:00 -07:00
..
scripts FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
training_submission_v4.0/tinycorp FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
training_submission_v4.1/tinycorp FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
training_submission_v5.0/tinycorp FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
training_submission_v5.1/tinycorp FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
dataloader.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
helpers.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
initializers.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
losses.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
lr_schedulers.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
metrics.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
model_eval.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
model_spec.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
model_train.py FrogPilot 0.9.7 2025-11-01 12:00:00 -07:00
README openpilot v0.9.7 release 2025-11-01 12:00:00 -07:00

Each model should be a clean single file.
They are imported from the top level `models` directory

It should be capable of loading weights from the reference imp.

We will focus on these 5 models:

# Resnet50-v1.5 (classic) -- 8.2 GOPS/input
# Retinanet
# 3D UNET (upconvs)
# RNNT
# BERT-large (transformer)

They are used in both the training and inference benchmark:
https://mlcommons.org/en/training-normal-21/
https://mlcommons.org/en/inference-edge-30/
And we will submit to both.

NOTE: we are Edge since we don't have ECC RAM