zhangyiss/mlx-examples
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx-examples.git synced 2025-06-24 17:31:18 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
df1406735b
mlx-examples/cifar
History
dmdaksh 7d7e236061
- Removed unused Python imports (#683) 
- bert/model.py:10: tree_unflatten
  - bert/model.py:2: dataclass
  - bert/model.py:8: numpy
  - cifar/resnet.py:6: Any
  - clip/model.py:15: tree_flatten
  - clip/model.py:9: Union
  - gcn/main.py:8: download_cora
  - gcn/main.py:9: cross_entropy
  - llms/gguf_llm/models.py:12: tree_flatten, tree_unflatten
  - llms/gguf_llm/models.py:9: numpy
  - llms/mixtral/mixtral.py:12: tree_map
  - llms/mlx_lm/models/dbrx.py:2: Dict, Union
  - llms/mlx_lm/tuner/trainer.py:5: partial
  - llms/speculative_decoding/decoder.py:1: dataclass, field
  - llms/speculative_decoding/decoder.py:2: Optional
  - llms/speculative_decoding/decoder.py:5: mlx.nn
  - llms/speculative_decoding/decoder.py:6: numpy
  - llms/speculative_decoding/main.py:2: glob
  - llms/speculative_decoding/main.py:3: json
  - llms/speculative_decoding/main.py:5: Path
  - llms/speculative_decoding/main.py:8: mlx.nn
  - llms/speculative_decoding/model.py:6: tree_unflatten
  - llms/speculative_decoding/model.py:7: AutoTokenizer
  - llms/tests/test_lora.py:13: yaml_loader
  - lora/lora.py:14: tree_unflatten
  - lora/models.py:11: numpy
  - lora/models.py:3: glob
  - speechcommands/kwt.py:1: Any
  - speechcommands/main.py:7: mlx.data
  - stable_diffusion/stable_diffusion/model_io.py:4: partial
  - whisper/benchmark.py:5: sys
  - whisper/test.py:5: subprocess
  - whisper/whisper/audio.py:6: Optional
  - whisper/whisper/decoding.py:8: mlx.nn
2024-04-16 07:50:32 -07:00
..
dataset.py Update a few examples to use compile (#420) 2024-02-08 13:00:41 -08:00
main.py Update a few examples to use compile (#420) 2024-02-08 13:00:41 -08:00
README.md Updated CIFAR-10 ResNet example to use BatchNorm instead of LayerNorm (#257) 2024-01-12 05:43:11 -08:00
requirements.txt Update a few examples to use compile (#420) 2024-02-08 13:00:41 -08:00
resnet.py - Removed unused Python imports (#683) 2024-04-16 07:50:32 -07:00

README.md

CIFAR and ResNets

An example of training a ResNet on CIFAR-10 with MLX. Several ResNet configurations in accordance with the original paper are available. The example also illustrates how to use MLX Data to load the dataset.

Pre-requisites

Install the dependencies:

pip install -r requirements.txt

Running the example

Run the example with:

python main.py

By default the example runs on the GPU. To run on the CPU, use:

python main.py --cpu

For all available options, run:

python main.py --help

Results

After training with the default resnet20 architecture for 30 epochs, you should see the following results:

Epoch: 29 | avg. Train loss 0.294 | avg. Train acc 0.897 | Throughput: 270.81 images/sec
Epoch: 29 | Test acc 0.841

Note this was run on an M1 Macbook Pro with 16GB RAM.

At the time of writing, mlx doesn't have built-in learning rate schedules. We intend to update this example once these features are added.