Comments (3)
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Getting models implemented in PyTorch to run on iOS using CoreML is a painful developer experience and it would be easier to just use a Swift mlx
frontend for on-device training / inference.
from mlx.
Closing this. Follow along #15 for progress updates.
from mlx.
Related Issues (20)
- [Feature] Make APIs non-blocking HOT 3
- [Feature] Enable Metal argsort, sort for > 2M elements along an axis
- [Feature] searchsorted HOT 4
- [Feature Request] Support convolution backward for groups > 1. HOT 1
- [BUG] 'mlx.core.linalg.qr' doesn't work on gpu, there's an abort. HOT 1
- [BUG] HOT 8
- [BUG] type stubs are missing for python 3.9 and 3.10 distributions
- [Feature Request] Adding option to follow PyTorch API in GELU initalization HOT 1
- [BUG] Segmentation fault while running custom operations HOT 4
- Memory Leakage Issue in MLX 0.16 HOT 9
- [PERFORMANCE] grads for bitwise ops + indexing HOT 1
- [Performance] Linear Layer Benchmark HOT 2
- [BUG] expm1 handling of overflow / underflow causes wrong results
- [Feature Request] mx.pad supports the "edge" padding mode.
- [BUG] Cannot use mlx.metallib from xcode MacOS project (Swift, C++)
- [BUG] Cannot convert a list with `None` to `mx.array` HOT 1
- [BUG] Docs not building HOT 4
- How can we enable w4a8 GEMM in MLX? HOT 6
- Missing pyi file for mlx.core HOT 1
- Performance Comparison Issue: Matrix Multiplication on MLX vs. PyTorch on Mac HOT 3
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