FluxML/NNlib.jl tiene un índice de salud de 71 sobre 100, lo que lo sitúa en la banda Bueno. Su puntuación más alta es Vitality (80/100) y la más baja, Security (52/100). Se actualizó por última vez hoy. 4 personas concentran la mayor parte del trabajo reciente.
71
global / 100
Bueno
Índice de salud del software
Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.
71
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
Perfil de puntuación
Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.
¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.
Código verificable por tipos — Julia sin configuración de verificación de tipos
55/55
Tamaños de archivo manejables — 0/1 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_language
Julia
largest_source_bytes
1275
source_files_sampled
1
oversized_source_files
0
Datos clave
255estrellas de GitHub
94contribuidores
74commits en los últimos 12 meses
0días desde el último push
100versiones publicadas
4factor bus
36issues abiertas
—ecosistemas de paquetes
Advertencias de recopilación de datos
No resolved dependencies carried a version and a supported ecosystem
Más detalle
Historial de estrellas y forks 255 ★ / 134 ⇿
255Estrellas
134Forks
100Versiones
Cuándo se añadió cada estrella y fork, recopilado de GitHub y agrupado por día. El crecimiento acumulado se sitúa justo encima de las adiciones diarias que lo componen, de modo que ambos se leen en conjunto: la acumulación orgánica sostenida no se parece en nada a un pico abrupto y efímero. Cuando esa diferencia es medible, se informa como autenticidad del crecimiento.
Mayor 0Menor 3Parche 97
Cada punto abarca 9 días.
OpenSSF Scorecard 5.2 / 10
5.2agregado
Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).Scorecard v5.5.0 · 2026-07-22 03:21 UTC
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 0 paquetes directos y 2 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
Registro
Paquete
Versión
Relación
PyPI
numpy
—
indirecta
PyPI
torch
—
indirecta
Avisos de dependencias sin evaluar
El cotejo de avisos no pudo ejecutarse para este informe: No resolved dependencies carried a version and a supported ecosystem
Informe JSON sin procesar legible por máquina
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"body": "…(#733)\n\n* Support multiple AD backends in `test_gradients`, default to Zygote + Enzyme\n\n`test_gradients` now accepts a list of AD types for `compare` and defaults to\n`[AutoZygote(), AutoEnzyme()]`, so gradients are checked against both backends.\n\n- Add `NNLIB_TEST_ENZYME` env flag; when false, `Tes\n[…]\nthored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n* exclude enzyme on gpu and on windows\n\n* or\n\n* ifelse\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Compare gradients against both Zygote and Enzyme in `test_gradients` …",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-07-01T04:44:15Z",
"body_truncated": true,
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"oid": "b340fdd2d8a33a249c105757c8b72c009e9298e4",
"body": "* rename Test_Enzyme\n\n* Migrate GPU `gputest` calls to the shared `test_gradients`\n\nReplace per-backend `gputest` calls with the unified `test_gradients`\nhelper (test_module.jl) wherever the test checks a differentiable\nforward+gradient, continuing the migration started in #730.\n\nCUDA (verified loca\n[…]\ns, and the ForwardDiff `Dual` forward-mode tests.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Migrate GPU `gputest` calls to the shared `test_gradients` (#732)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-29T05:26:36Z",
"body_truncated": true,
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"oid": "bba9c2209f770355a917eaed97830a204a740192",
"body": "* Enable scatter common_testsuite on the Metal backend\n\nActivate the shared `scatter` suite on Metal and make it Metal-compatible.\nOther shared suites stay skipped on Metal pending their own fixes (they hit\nFloat64 / scalar-indexing / complex-activation / ImageTransformations limits).\n\nscatter.jl Me\n[…]\nle/gather/fold are 0-error on both Metal and CPU.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Enable scatter, gather, fold, upsample common_testsuites on Metal (#731)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-28T14:38:23Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "e0831c03e4bb5a39205988db0f6b7613e2de13f9",
"body": "* Migrate gradient tests from `gradtest` to `test_gradients`\n\nAdd a multi-backend `test_gradients` utility (adapted from Flux) to\n`test/test_module.jl` and migrate the bulk of `gradtest` call sites to it.\n\n`test_gradients`:\n- compares a Zygote gradient against a FiniteDifferences reference (inputs\n \n[…]\nfloat coverage. Verified on CPU and CUDA locally.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Migrate gradient tests from `gradtest` to `test_gradients` (#730)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-28T04:39:05Z",
"body_truncated": true,
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"oid": "cda262f97fa8e8afdbabf614784bb1024b9a8bb8",
"body": null,
"is_bot": false,
"headline": "add AGENTS.md",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-27T05:43:20Z",
"body_truncated": false,
"is_coding_agent": false
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{
"oid": "ad332c694e934804bccdbc2bfa94f1c5c47f0e34",
"body": "* Migrate test suite to ParallelTestRunner (#533)\n\nRun each test file in its own ParallelTestRunner worker so the suite runs\nin parallel. Shared imports/helpers move into test/test_module.jl (loaded in\nevery worker via init_code); the backend-parametrized testsuite/ suites are\ndriven with one entry \n[…]\nember (Metal 1.9.3 lands in the shared manifest).\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Migrate test suite to ParallelTestRunner (#533) (#729)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-27T05:39:16Z",
"body_truncated": true,
"is_coding_agent": true
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"oid": "4726413e4956b47f4169d87daf4347e4d85d906d",
"body": "* bachnorm rules\n\n* rm cuda, cudnn deps\n\n* fix batchnorm rrule!! backward with nothing running stats\n\n* cache fwd mean/ivar to make backward deterministic\n\n* fix dims\n\n* note about caching mean & var",
"is_bot": false,
"headline": "Mooncake rule for `NNlib.batchnorm`. (#727)",
"author_name": "Astitva Aggarwal",
"author_login": "AstitvaAggarwal",
"committed_at": "2026-06-26T15:04:55Z",
"body_truncated": false,
"is_coding_agent": false
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{
"oid": "fdc06d1d0717939ac57d067fba9df00067092e0c",
"body": "The `dropout` rrule built `@. ((keep > p) * val) * A` with `p` passed\nthrough unchanged. When `p` is a `Float64` (e.g. the `Dropout(0.0)`\ndefault inside `MultiHeadAttention`) and `A` is a `Float32` GPU array,\nthe `keep > p` comparison promoted the broadcast to `Float64`, which\nfails to compile on Me\n[…]\nays\nin the element type. Adds a Metal regression test covering `p = 0.0`\n(the MHA default) and `p = 0.3`, with and without `dims`.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix dropout rrule on Metal: keep `p` in the array's float type (#726)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-25T06:16:31Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "ba135536be31fa2b00ad6712305f36a6708ef26e",
"body": "Bumps [actions/checkout](https://github.com/actions/checkout) from 6 to 7.\n- [Release notes](https://github.com/actions/checkout/releases)\n- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/actions/checkout/compare/v6...v7)\n\n---\nupdated-dependenc\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
"is_bot": true,
"headline": "Bump actions/checkout from 6 to 7 (#724)",
"author_name": "dependabot[bot]",
"author_login": "dependabot[bot]",
"committed_at": "2026-06-22T12:43:51Z",
"body_truncated": true,
"is_coding_agent": false
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{
"oid": "9ef1fd04fbefb68f9688e55f3a3e834a6bc81944",
"body": "* Update Buildkite pipeline for the new JuliaGPU cluster.\n\nSwitch to the per-backend queues of the new JuliaGPU cluster.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>\n\n* Move the Metal job(s) to the new metal queue.\n\nThe JuliaGPU cluster now has a macOS agent with an Apple Silicon GPU,\nso Metal jobs no longer need a separate pipeline on the ecosystem\ncluster.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Fable 5 <noreply@anthropic.com>",
"is_bot": false,
"headline": "Update Buildkite pipeline for the new JuliaGPU cluster (#723)",
"author_name": "Tim Besard",
"author_login": "maleadt",
"committed_at": "2026-06-14T19:11:52Z",
"body_truncated": false,
"is_coding_agent": true
},
{
"oid": "f71b71c7f497b849c6553e098ba1966d36418225",
"body": "* Refactor softmax gradients to not capture x\n\nThe softmax/logsoftmax backward passes never use the input `x` for its\nvalues: the generic path computes from `dy` and `y` alone, and the cuDNN\npath uses `x` only for shape/eltype metadata, which `y` carries equally.\nCapturing `x` in the rrule pullback \n[…]\nnoreply@anthropic.com>\n\n* Remove deprecation test\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Refactor softmax and hook cudnn backward (#718)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T16:07:28Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "fd8bf8364ec2cc9f2d22cd03b1daa6d542d47f16",
"body": "…mark (#513) (#717)\n\n* Add GPU softmax benchmark (cuDNN vs custom kernels, issue #513)\n\nBenchmarks softmax/logsoftmax forward and gradient on the GPU, comparing\nthe cuDNN routines against NNlib's generic broadcast kernels (and\nLogExpFunctions.softmax as a cross-check), for dims=1 and dims=2.\n\nReprod\n[…]\nustom policy, and a\n\"why not permute\" comparison.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix slow cuDNN softmax backward via INSTANCE mode + GPU softmax bench…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T09:55:49Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "5a36dcfc4cafc6b5b02653107ca9cbbcd94cb999",
"body": "* skip enzyme gather test on amdgpu\n\n* deactivate enzyme tests on amdgpu",
"is_bot": false,
"headline": "skip enzyme tests on amdgpu (#709)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T09:16:44Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "f91c34dbc4086e47aee9c6e9b326b9d7c645ec38",
"body": "Adds `NNlib._batched_gemm!(::Type{<:MtlArray}, ...)` to the Metal extension,\nthe dispatch hole that made `batched_mul` (and thus `dot_product_attention`)\nraise a MethodError on `MtlArray`s.\n\nReal `Float16`/`Float32` operands with matching batch sizes go through Metal\nPerformance Shaders' batched `ma\n[…]\nperands,\nmatrix×batch broadcasting, batched_vec, tiny arrays, Float16, gradients, the\nin-place 5-arg form, and display/conversion.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support batched_mul on Metal via MPS (fixes #581) (#714)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T06:13:20Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "2a3c123146c852a01ac52bd4cab60b1cc6e724a6",
"body": "The cuDNN softmax algorithm was selected from CUDA.math_mode: under\nCUDA.FAST_MATH it used CUDNN_SOFTMAX_FAST, which skips the row-max\nsubtraction and overflows to NaN on masked / large-magnitude inputs\n(e.g. attention masks of -1000 or -Inf), and is unsafe in Float16.\n\nsoftmaxalgo() now always retu\n[…]\nse overflow-safe.\n\nAdds a regression test exercising both DEFAULT_MATH and FAST_MATH with\nlarge-negative and -Inf attention masks.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Always use accurate cuDNN softmax algorithm (fixes #506) (#715)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T06:07:43Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "4ed25976bb6c67a4f2d04630e26fd1f37fbde6eb",
"body": null,
"is_bot": false,
"headline": "Bump version from 0.9.35 to 0.9.36",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T03:50:57Z",
"body_truncated": false,
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{
"oid": "8d831105dc79e56005d97523595593be16b505e5",
"body": "* Support complex inputs in meanpool on GPU (fixes #610)\n\nMean pooling is linear, so the cuDNN extension supports complex inputs by\npooling the real and imaginary parts independently and recombining, mirroring\nthe existing complex-conv pattern. This already worked on the CPU path via the\ngeneric imp\n[…]\nCUDA/CPU complex tests to\nalso cover the AD path.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support complex inputs in meanpool across backends (fixes #610) (#711)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T03:49:56Z",
"body_truncated": true,
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{
"oid": "ba68bbbaaf039f1fd1ef791ce895a18a850f8398",
"body": "The `@inferred stft(x; n_fft=16)` test in test/testsuite/spectral.jl was\ndisabled with a \"TODO fix type stability for pad_reflect\" comment. There\nwere two distinct sources of instability:\n\n1. The single-dimension padding kernels (`pad_reflect`, `pad_symmetric`,\n `pad_circular`, `pad_repeat`) forwa\n[…]\nng docstring examples match\nexactly, gradients agree with finite differences, and the padding.jl and\nspectral CPU testsuites pass.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Make padding functions and stft type-stable (#702) (#713)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T03:45:10Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "6af48f575444f9ee3e9d8c40101c36849b2121af",
"body": "* Enable Enzyme tests on Julia 1.12\n\nEnzyme is functional on Julia 1.12 (verified against Enzyme 0.13.154):\nconv!/∇conv_data!/∇conv_filter!/depthwiseconv! (spatial ranks 1-3),\nmaxpool!/meanpool!, gather!, scatter! and dropout all pass.\n\n- runtests.jl: extend the `Test_Enzyme` guard to `v\"1.13-\"` so \n[…]\nhttps://github.com/EnzymeAD/Enzyme.jl/issues/3150\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Enable Enzyme tests on Julia 1.12 (#708)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T01:58:17Z",
"body_truncated": true,
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{
"oid": "49948aeea8174d6105236a40d2f912d6651da83c",
"body": "…nbreak tests (#706)\n\nAudit follow-up to #702.\n\nsrc/activations.jl:\n- hardσ: the broadcasted/scalar derivative returned `oftf(Ω, 1)` (i.e. 1)\n in its saturated branch instead of 0, so the gradient was wrong for |x|>3\n on every backend. The existing gradtests only probe ±2±rand, always inside\n har\n[…]\nactivations.jl: add a regression test checking saturated-region gradients\n of hardσ/hardtanh/relu6/hardswish against ForwardDiff.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix clamping-activation gradients (incl. hardσ correctness bug) and u…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T16:47:35Z",
"body_truncated": true,
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},
{
"oid": "8918579a075e3e77d01a7c22d90712c94ba5e766",
"body": "…ing (#705)\n\nWhen `gather`/`scatter` are called with the source/destination on the GPU\nbut the index array left on the CPU, dispatch previously fell through to the\ngeneric method, which scalar-indexes the GPU array — slow, or an error under\n`allowscalar(false)`. Add methods that move such an index o\n[…]\nr GPU) against the\nindexed dimensions before launching the kernel, for both the generic-KA and\nCUDA-specific scatter paths (#416).\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support CPU index with GPU source in gather/scatter, add bounds check…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T16:47:08Z",
"body_truncated": true,
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"oid": "1992a728e9f986d13dcf7640f202adefe171126a",
"body": "…03) (#704)\n\n* Support all scatter reduction ops on Metal (#534)\n\nThe shared GPU scatter kernel performed its atomic update via\n`Atomix.modify!`, whose Metal backend only implements a fixed op list.\nOn Metal this meant:\n\n- `max`/`min` on floats failed to compile (no Float32 atomic_fetch_max/min,\n s\n[…]\noverride. The Metal grad\ntests now cover all ops.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix scatter on Metal: all reduction ops and gradients (fixes #534, #7…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T15:47:10Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "33528494bed3403a26a609cbc930f96a41d8b3ab",
"body": "cuDNN's TRUE_HALF_CONFIG (accumulate in Float16) is unsupported for 3D\nconvolutions and for many backward-filter shapes, surfacing as\nCUDNN_STATUS_NOT_SUPPORTED / BAD_PARAM. Set the convolution descriptor's\ncompute type to Float32 for Float16 inputs (PSEUDO_HALF_CONFIG), which is\nbroadly supported a\n[…]\nhin Float16\nrounding), plus explicit regression guards for the #505 (5D forward) and\n#515 (3=>64, 1x1-output ∇conv_filter) shapes.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix Float16 GPU conv via PSEUDO_HALF_CONFIG (fixes #505, #515) (#701)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T15:09:03Z",
"body_truncated": true,
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{
"oid": "8ee3540eccd991597e6c89d2fe33f608035e29fe",
"body": "…pace (#520) (#700)\n\nGrouped convolutions (and, since #698, depthwise convolutions) allocated a\nfresh im2col `col` workspace per group, so a depthwise conv with N channels\nallocated O(N) buffers. They also nested two levels of threading (parallel\nover groups x per-image batch threading inside each g\n[…]\n to share).\n\nThe #520 MWE drops from ~12.6k to ~1.2k allocations and the redundant per-group\ncol buffers (~13 MiB) are eliminated.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Reduce grouped/depthwise conv allocations by sharing the im2col works…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T13:17:03Z",
"body_truncated": true,
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{
"oid": "7c006e7bcc74eb75438e2c420cadaee6f1c73bf2",
"body": "…698)\n\nA depthwise convolution is exactly a grouped convolution with\n`groups == channels_in`. The specialized depthwise im2col/direct kernels\nbenchmarked consistently slower than the generic grouped-convolution path\n(forward, ∇data, and especially ∇filter which was ~4.5x slower), so route\nevery dept\n[…]\ng test is repurposed to check `depthwiseconv` against an\nindependently (explicit-loop) reordered grouped convolution.\n\nCloses #402\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Implement depthwiseconv via grouped conv, drop specialized kernels (#…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T09:56:33Z",
"body_truncated": true,
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"oid": "9f5ada1bd0bdb038fede4248c0a2907bd7d01c57",
"body": "* Thread CPU conv within a single image at small batch (#234)\n\n`conv_im2col!` only parallelized over the batch dimension, so with\n`batch < nthreads` the extra threads sat idle — the main reason CPU conv\nlagged PyTorch at small batch in #234.\n\nWhen there are fewer images than tasks, also split each i\n[…]\nin a fair 4-thread config and report\nthe minimum.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Thread CPU conv within a single image at small batch (#234) (#697)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T09:55:21Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "741381c577e0b389afaf5a2ea09a6840a8f5be08",
"body": "* Add conv perf analysis to benchmark README\n\nSummarizes why NNlib's CPU conv is slower than PyTorch's: NNlib threads\nonly over the batch dimension (idle cores when batch < nthreads), while\nPyTorch dispatches float32 convs to oneDNN's JIT direct convolution, which\nparallelizes within a single image.\n[…]\nright fix, not a from-scratch\ndirect convolution.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Add CPU conv performance analysis to benchmark README (#696)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T06:53:07Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "0a62811a0981645780fa165dffa7c999217ed95d",
"body": "cuDNN exposes two average-pooling modes: one that includes padding zeros\nin the averaging denominator and one that excludes them. NNlib's meanpool\nonly implemented the include-padding mode.\n\nAdd a `count_include_pad` keyword (default `true`, so the behavior is\nunchanged) to `meanpool`. When `false`,\n[…]\nyword.\n- Tests: CPU forward/gradient vs a brute-force reference across spatial\n ranks, and CPU-vs-cuDNN agreement for both modes.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Add count_include_pad option to meanpool (issue #218) (#694)",
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"headline": "CompatHelper: bump compat for cuDNN in [weakdeps] to 6, (keep existin…",
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"headline": "fix: `gather` and `scatter` on empty arrays (#668)",
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"headline": "Extending scatter! to work with CUDA sparse arrays (#648)",
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"headline": "Metal Extension with fix for tanh_fast (#666)",
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"body": "* Initial plan\n\n* Implement generalized batched_vec for N-D batches\n\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\n\n* Update src/batched/batchedmul.jl\n\n* Update test/batchedmul.jl\n\n---------\n\nCo-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\nCo-authored-by: Carlo Lucibello <carlo.lucibello@gmail.com>",
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"body": "* Initial plan\n\n* Implement gelu_sigmoid and update gelu_tanh to use tanh\n\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\n\n* Add gelu_sigmoid to documentation\n\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\n\n---------\n\nCo-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>",
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"headline": "update min julia version (#661)",
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"body": "Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6.\n- [Release notes](https://github.com/actions/checkout/releases)\n- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/actions/checkout/compare/v5...v6)\n\n---\nupdated-dependenc\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
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"body": "Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 5.\n- [Release notes](https://github.com/actions/checkout/releases)\n- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/actions/checkout/compare/v3...v5)\n\n---\nupdated-dependenc\n[…]\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>\nCo-authored-by: Carlo Lucibello <carlo.lucibello@gmail.com>",
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"headline": "Bump actions/checkout from 3 to 5 (#658)",
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"headline": "Bump codecov/codecov-action from 3 to 5 (#657)",
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Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.26.0 — metodología completa · wiki de métricas.
