torsh-core 0.2.0

Core types and traits for ToRSh deep learning framework
Documentation

torsh-core

There is currently very little information to present on this page because Docs.rs has only limited support for extracting structured feature metadata from Cargo crates. This issue is tracked in Rust RFC #3416. Check this library's main docs, readme, and Cargo.toml in case its authors have documentation for features available there instead.

This version has 33 feature flags, 5 of them enabled by default.

default

half (default)

This feature flag does not enable additional features.

parallel (default)

std (default)

This feature flag does not enable additional features.

crossbeam (default)

rayon (default)

avx512

This feature flag does not enable additional features.

bench

This feature flag does not enable additional features.

c-api

This feature flag does not enable additional features.

compression

cpu-optimized

cuda

This feature flag does not enable additional features.

cudnn

debug

This feature flag does not enable additional features.

distributed

This feature flag does not enable additional features.

experimental

This feature flag does not enable additional features.

fast-math

This feature flag does not enable additional features.

fp16

This feature flag does not enable additional features.

gpu

This feature flag does not enable additional features.

memory-debug

memory-mapping

This feature flag does not enable additional features.

no_std

This feature flag does not enable additional features.

numa

This feature flag does not enable additional features.

oxiarc-zstd

oxicode

python

This feature flag does not enable additional features.

serde

serde_json

serialize

simd

This feature flag does not enable additional features.

storage_benchmarks

This feature flag does not enable additional features.

trace

This feature flag does not enable additional features.

unsafe-optimizations

This feature flag does not enable additional features.

wgpu

This feature flag does not enable additional features.