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Bump the all-julia-packages group across 1 directory with 5 updates#59
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Updates the requirements on CUDA, Statistics, Optimization, OrdinaryDiffEq and DiffEqBase to permit the latest version.
Updates CUDA to 6.1.0

Release notes

Sourced from CUDA's releases.

v6.1.0

CUDA v6.1.0

Diff since v6.0.0

Merged pull requests:

Closed issues:

  • CUFFT: support for arbitrary dims (#119)
  • LLVM 20: Adapt to LDG removal (#2531)
  • Julia 1.11: Views can return CPU SubArray (#2551)
  • 1-element view not recognized as contiguous (#2653)
  • Can't compare Float32 with Rational on CUDA (#2681)
  • Memory leak with unified memory? (#3013)
  • Stack overflow on sparse(::Symmetric) (#3042)
  • Scalar indexing on +(::Symmetric, ::Symmetric) (#3043)
  • Performance improvement ideas for randn! Float32 (#3056)
  • Base.FastMath.pow_fast fails to compile with integer exponent (#3065)
  • normalize on CuArray fails due to scalar indexing (#3097)
  • [cuSPARSE] Incorrect indexing for COO-formatted sparse arrays (#3100)
Commits

Updates Statistics to 1.11.1

Release notes

Sourced from Statistics's releases.

v1.11.1

Statistics v1.11.1

Diff since v1.11.0

Merged pull requests:

Closed issues:

  • The quantile function can return incorrect results for integer arrays (Int8, Int16, Int32) (#119)
Commits

Updates Optimization to 5.6.1

Release notes

Sourced from Optimization's releases.

v5.6.1

Optimization v5.6.1

Diff since v5.5.1

Closed issues:

  • Improve return codes from OptimizationManopt (#1034)
  • OptimizationOptimJL + SciMLBase@3 are incompatible (#1187)
  • TagBot trigger issue (#1196)
  • maxiters is ignored when maxtime is set (#1206)
Changelog

Sourced from Optimization's changelog.

v4 Breaking changes

  1. The main change in this breaking release has been the way mini-batching is handled. The data argument in the solve call and the implicit iteration of that in the callback has been removed, the stochastic solvers (Optimisers.jl and Sophia) now handle it explicitly. You would now pass in a DataLoader to OptimizationProblem as the second argument to the objective etc (p) if you want to do minibatching, else for full batch just pass in the full data.

  2. The support for extra returns from objective function has been removed. Now the objective should only return a scalar loss value, hence callback doesn't take extra arguments other than the state and loss value.

Commits

Updates OrdinaryDiffEq to 7.0.0

Release notes

Sourced from OrdinaryDiffEq's releases.

v7.0.0

OrdinaryDiffEq v7.0.0

Diff since v6.211.0

Many breaking changes. The complete migration story is detailed in https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/NEWS.md

Merged pull requests:

... (truncated)

Changelog

Sourced from OrdinaryDiffEq's changelog.

OrdinaryDiffEq.jl v7 / DifferentialEquations.jl v8 Breaking Changes

This release bumps to SciMLBase v3, RecursiveArrayTools v4, and includes breaking changes across DiffEqBase, OrdinaryDiffEqCore, and all solver sublibraries. It also coincides with the DifferentialEquations.jl v8 umbrella release, which is itself a breaking change to the user-facing meta-package.

DifferentialEquations.jl v8: scope reduction

DifferentialEquations.jl v8 no longer re-exports the full SciML solver suite. Previously, using DifferentialEquations pulled in OrdinaryDiffEq, StochasticDiffEq, DelayDiffEq, BoundaryValueDiffEq, Sundials, JumpProcesses, SteadyStateDiffEq, LinearSolve, NonlinearSolve, Optimization, etc. — a large default surface that drove up using time and made it unclear which package any given solver actually came from.

In v8, using DifferentialEquations only loads OrdinaryDiffEq. All other solver families have been removed from the umbrella. If your code relied on DifferentialEquations for SDEs, DDEs, BVPs, jumps, steady states, or any non-ODE solver, you will need to add the topic-specific package to your project explicitly.

Migration

Find the topic you need a solver for and add the corresponding sublib(s) directly. The DiffEqDocs tutorials and solver pages now specify, per algorithm, which package it ships from. Common cases:

Topic Old (DiffEq v7 umbrella) New (DiffEq v8)
ODEs using DifferentialEquations using OrdinaryDiffEq (or using OrdinaryDiffEqTsit5, OrdinaryDiffEqRosenbrock, … for individual solver families)
Stochastic ODEs using DifferentialEquations using StochasticDiffEq
Delay ODEs using DifferentialEquations using DelayDiffEq
Boundary value problems using DifferentialEquations using BoundaryValueDiffEq (or one of BoundaryValueDiffEqMIRK, BoundaryValueDiffEqFIRK, BoundaryValueDiffEqShooting, …)
Jump processes using DifferentialEquations using JumpProcesses
Steady state using DifferentialEquations using SteadyStateDiffEq
DAEs (mass matrix or implicit) using DifferentialEquations using OrdinaryDiffEq (mass matrix), using Sundials (IDA), or topic sublib
Sundials wrappers (CVODE, IDA, ARKODE) using DifferentialEquations using Sundials
Linear / nonlinear / optimization using DifferentialEquations using LinearSolve / using NonlinearSolve / using Optimization

For ODE work specifically, prefer importing only the sublib you need (e.g. using OrdinaryDiffEqTsit5: Tsit5) rather than the umbrella using OrdinaryDiffEq — the v7 ecosystem split lets you trim using time substantially. The DiffEqDocs tutorials and solver index annotate every algorithm with its host sublib.

Why

Removing the meta-package's broad re-exports lets each topic's package version cycle independently, eliminates the long using DifferentialEquations precompile chain for users who only need ODEs, and makes the dependency graph for any given script honest about what's actually being loaded.

This change is independent of the OrdinaryDiffEq v7 changes below — OrdinaryDiffEq v7 ships with DifferentialEquations v8, but you can also use OrdinaryDiffEq v7 directly without the umbrella package at all.

OrdinaryDiffEq.jl v7 Breaking Changes

This release bumps to SciMLBase v3, RecursiveArrayTools v4, and includes breaking changes across DiffEqBase, OrdinaryDiffEqCore, and all solver sublibraries.

Themes of the v7 release

Most of the breaking changes fall into a small set of recurring themes. Keep these in mind while reading the migration table — they explain why an individual change exists and often suggest the right migration direction:

  • Time to first solve (TTFS) reduction. Direct deps on Static.jl, StaticArrayInterface.jl, Polyester.jl, and StaticArrays.jl were dropped; using OrdinaryDiffEq now loads only the default solver set; ODEFunction switched to AutoSpecialize. All of this means less code loaded and more precompilation caching on first solve.

  • Type stability everywhere. All Bool solver/solve keyword arguments (autodiff, verbose, alias, lazy, …) were replaced by typed objects. Passing a Bool no longer changes dispatch in ways the compiler cannot specialize on, and the reverse is no longer allowed to silently fall back through slow generic paths. For example:

    # v6 — a Bool
Rosenbrock23(autodiff = true)
solve(prob, alg; verbose = false, alias = true)

... (truncated)

Commits
  • f3ce496 Merge pull request #3567 from SciML/drop-unused-rosenbrock-tableaus-dep
  • 02763e7 Drop unused OrdinaryDiffEqRosenbrockTableaus from top-level deps
  • a0a61e0 Update version to 7.0.0 in Project.toml
  • 68c304d Merge pull request #3566 from SciML/fix-delaydiffeq-extras-uuids
  • 7c87e5a Fix three wrong UUIDs in DelayDiffEq's [extras]
  • b3359fb Merge pull request #3565 from SciML/revert-core-ddt-bumps
  • cfb94cd Revert OrdinaryDiffEqCore and DiffEqDevTools major bumps from #3562
  • 5b867b8 Merge pull request #3563 from ChrisRackauckas-Claude/drop-old-majors-v7-stack
  • a8419de Revert out-of-scope deps additions in Rosenbrock/Tsit5 QA test envs
  • 260775b Drop old majors from v7-coupled stack compat
  • Additional commits viewable in compare view

Updates DiffEqBase to 7.5.5

Changelog

Sourced from DiffEqBase's changelog.

OrdinaryDiffEq.jl v7 / DifferentialEquations.jl v8 Breaking Changes

This release bumps to SciMLBase v3, RecursiveArrayTools v4, and includes breaking changes across DiffEqBase, OrdinaryDiffEqCore, and all solver sublibraries. It also coincides with the DifferentialEquations.jl v8 umbrella release, which is itself a breaking change to the user-facing meta-package.

DifferentialEquations.jl v8: scope reduction

DifferentialEquations.jl v8 no longer re-exports the full SciML solver suite. Previously, using DifferentialEquations pulled in OrdinaryDiffEq, StochasticDiffEq, DelayDiffEq, BoundaryValueDiffEq, Sundials, JumpProcesses, SteadyStateDiffEq, LinearSolve, NonlinearSolve, Optimization, etc. — a large default surface that drove up using time and made it unclear which package any given solver actually came from.

In v8, using DifferentialEquations only loads OrdinaryDiffEq. All other solver families have been removed from the umbrella. If your code relied on DifferentialEquations for SDEs, DDEs, BVPs, jumps, steady states, or any non-ODE solver, you will need to add the topic-specific package to your project explicitly.

Migration

Find the topic you need a solver for and add the corresponding sublib(s) directly. The DiffEqDocs tutorials and solver pages now specify, per algorithm, which package it ships from. Common cases:

Topic Old (DiffEq v7 umbrella) New (DiffEq v8)
ODEs using DifferentialEquations using OrdinaryDiffEq (or using OrdinaryDiffEqTsit5, OrdinaryDiffEqRosenbrock, … for individual solver families)
Stochastic ODEs using DifferentialEquations using StochasticDiffEq
Delay ODEs using DifferentialEquations using DelayDiffEq
Boundary value problems using DifferentialEquations using BoundaryValueDiffEq (or one of BoundaryValueDiffEqMIRK, BoundaryValueDiffEqFIRK, BoundaryValueDiffEqShooting, …)
Jump processes using DifferentialEquations using JumpProcesses
Steady state using DifferentialEquations using SteadyStateDiffEq
DAEs (mass matrix or implicit) using DifferentialEquations using OrdinaryDiffEq (mass matrix), using Sundials (IDA), or topic sublib
Sundials wrappers (CVODE, IDA, ARKODE) using DifferentialEquations using Sundials
Linear / nonlinear / optimization using DifferentialEquations using LinearSolve / using NonlinearSolve / using Optimization

For ODE work specifically, prefer importing only the sublib you need (e.g. using OrdinaryDiffEqTsit5: Tsit5) rather than the umbrella using OrdinaryDiffEq — the v7 ecosystem split lets you trim using time substantially. The DiffEqDocs tutorials and solver index annotate every algorithm with its host sublib.

Why

Removing the meta-package's broad re-exports lets each topic's package version cycle independently, eliminates the long using DifferentialEquations precompile chain for users who only need ODEs, and makes the dependency graph for any given script honest about what's actually being loaded.

This change is independent of the OrdinaryDiffEq v7 changes below — OrdinaryDiffEq v7 ships with DifferentialEquations v8, but you can also use OrdinaryDiffEq v7 directly without the umbrella package at all.

OrdinaryDiffEq.jl v7 Breaking Changes

This release bumps to SciMLBase v3, RecursiveArrayTools v4, and includes breaking changes across DiffEqBase, OrdinaryDiffEqCore, and all solver sublibraries.

Themes of the v7 release

Most of the breaking changes fall into a small set of recurring themes. Keep these in mind while reading the migration table — they explain why an individual change exists and often suggest the right migration direction:

  • Time to first solve (TTFS) reduction. Direct deps on Static.jl, StaticArrayInterface.jl, Polyester.jl, and StaticArrays.jl were dropped; using OrdinaryDiffEq now loads only the default solver set; ODEFunction switched to AutoSpecialize. All of this means less code loaded and more precompilation caching on first solve.

  • Type stability everywhere. All Bool solver/solve keyword arguments (autodiff, verbose, alias, lazy, …) were replaced by typed objects. Passing a Bool no longer changes dispatch in ways the compiler cannot specialize on, and the reverse is no longer allowed to silently fall back through slow generic paths. For example:

    # v6 — a Bool
Rosenbrock23(autodiff = true)
solve(prob, alg; verbose = false, alias = true)

... (truncated)

Commits

@dependabot dependabot Bot added dependencies Pull requests that update a dependency file julia Pull requests that update julia code labels Jun 2, 2026
@dependabot @github

dependabot Bot commented on behalf of github Jun 4, 2026

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Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot recreate.

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Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot recreate.

@ChrisRackauckas

ChrisRackauckas commented Jun 4, 2026

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@dependabot dependabot Bot force-pushed the dependabot/julia/test/all-julia-packages-6385b17d19 branch from a938bc2 to 367393d Compare June 4, 2026 15:40
@dependabot
Bump the all-julia-packages group across 1 directory with 5 updates
0583903 
Updates the requirements on [CUDA](https://github.com/JuliaGPU/CUDA.jl), [Statistics](https://github.com/JuliaStats/Statistics.jl), [Optimization](https://github.com/SciML/Optimization.jl), [OrdinaryDiffEq](https://github.com/SciML/OrdinaryDiffEq.jl) and [DiffEqBase](https://github.com/SciML/OrdinaryDiffEq.jl) to permit the latest version.

Updates `CUDA` to 6.1.0
- [Release notes](https://github.com/JuliaGPU/CUDA.jl/releases)
- [Commits](https://github.com/JuliaGPU/CUDA.jl/commits/v6.1.0)

Updates `Statistics` to 1.11.1
- [Release notes](https://github.com/JuliaStats/Statistics.jl/releases)
- [Commits](JuliaStats/Statistics.jl@v1.4.0...v1.11.1)

Updates `Optimization` to 5.6.1
- [Release notes](https://github.com/SciML/Optimization.jl/releases)
- [Changelog](https://github.com/SciML/Optimization.jl/blob/master/NEWS.md)
- [Commits](https://github.com/SciML/Optimization.jl/commits/v5.6.1)

Updates `OrdinaryDiffEq` to 7.0.0
- [Release notes](https://github.com/SciML/OrdinaryDiffEq.jl/releases)
- [Changelog](https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/NEWS.md)
- [Commits](SciML/OrdinaryDiffEq.jl@v6.0.0...v7.0.0)

Updates `DiffEqBase` to 7.5.5
- [Release notes](https://github.com/SciML/OrdinaryDiffEq.jl/releases)
- [Changelog](https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/NEWS.md)
- [Commits](https://github.com/SciML/OrdinaryDiffEq.jl/commits)

---
updated-dependencies:
- dependency-name: CUDA
  dependency-version: 6.1.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DiffEqBase
  dependency-version: 7.5.5
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Optimization
  dependency-version: 5.6.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: OrdinaryDiffEq
  dependency-version: 7.0.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Statistics
  dependency-version: 1.11.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot force-pushed the dependabot/julia/test/all-julia-packages-6385b17d19 branch from 367393d to 0583903 Compare June 8, 2026 22:13
@ChrisRackauckas-Claude ChrisRackauckas-Claude mentioned this pull request Jun 10, 2026
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@ChrisRackauckas-Claude

ChrisRackauckas-Claude commented Jun 10, 2026

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The CI failures here are not directly fixable on this branch: ParametricMCPs (latest release included) caps ForwardDiff at 0.10 via a weak dependency, which conflicts with the forced-latest ForwardDiff 1.4 on dependabot runs and transitively blocks NNlib ≥ 0.9.32 / CUDA 6. #63 supersedes this PR — it includes these compat bumps plus the test-dependency change needed for the new stack to actually resolve.

ChrisRackauckas added a commit that referenced this pull request Jun 10, 2026
@ChrisRackauckas-Claude @dependabot
@ChrisRackauckas @claude
Breaking dependency updates: CUDA 6, DiffEqBase 7, OrdinaryDiffEq 7, …
7f04704 
…Optimization 5 (supersedes #59) (#63)

* Bump the all-julia-packages group across 1 directory with 5 updates

Updates the requirements on [CUDA](https://github.com/JuliaGPU/CUDA.jl), [Statistics](https://github.com/JuliaStats/Statistics.jl), [Optimization](https://github.com/SciML/Optimization.jl), [OrdinaryDiffEq](https://github.com/SciML/OrdinaryDiffEq.jl) and [DiffEqBase](https://github.com/SciML/OrdinaryDiffEq.jl) to permit the latest version.

Updates `CUDA` to 6.1.0
- [Release notes](https://github.com/JuliaGPU/CUDA.jl/releases)
- [Commits](https://github.com/JuliaGPU/CUDA.jl/commits/v6.1.0)

Updates `Statistics` to 1.11.1
- [Release notes](https://github.com/JuliaStats/Statistics.jl/releases)
- [Commits](JuliaStats/Statistics.jl@v1.4.0...v1.11.1)

Updates `Optimization` to 5.6.1
- [Release notes](https://github.com/SciML/Optimization.jl/releases)
- [Changelog](https://github.com/SciML/Optimization.jl/blob/master/NEWS.md)
- [Commits](https://github.com/SciML/Optimization.jl/commits/v5.6.1)

Updates `OrdinaryDiffEq` to 7.0.0
- [Release notes](https://github.com/SciML/OrdinaryDiffEq.jl/releases)
- [Changelog](https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/NEWS.md)
- [Commits](SciML/OrdinaryDiffEq.jl@v6.0.0...v7.0.0)

Updates `DiffEqBase` to 7.5.5
- [Release notes](https://github.com/SciML/OrdinaryDiffEq.jl/releases)
- [Changelog](https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/NEWS.md)
- [Commits](https://github.com/SciML/OrdinaryDiffEq.jl/commits)

---
updated-dependencies:
- dependency-name: CUDA
  dependency-version: 6.1.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: DiffEqBase
  dependency-version: 7.5.5
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Optimization
  dependency-version: 5.6.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: OrdinaryDiffEq
  dependency-version: 7.0.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: Statistics
  dependency-version: 1.11.1
  dependency-type: direct:production
  dependency-group: all-julia-packages
...

Signed-off-by: dependabot[bot] <support@github.com>

* Remove ParametricMCPs test dependency to unblock dependency updates

ParametricMCPs (including its latest release, 0.1.17) caps ForwardDiff at
0.10 through a weak dependency, which makes it impossible to resolve a test
environment containing NonlinearSolve >= 4.17 (requires ForwardDiff 1),
NNlib >= 0.9.32, or CUDA >= 6. This is what made the GPU CI jobs on the
dependabot update PR fail at version resolution, and it silently held the
CPU test jobs back on DiffEqBase 6 / OrdinaryDiffEq 6 / CUDA 5 /
ForwardDiff 0.10.

Dropping the ParametricMCPs comparison benchmark lets the test environment
resolve the full updated stack (CUDA 6.1, DiffEqBase 7.5, OrdinaryDiffEq
7.0, Optimization 5.6, ForwardDiff 1.4, NonlinearSolve 4.19,
SciMLSensitivity 7.112, SciMLBase 3.18). The ComplementaritySolve.jl side
of the benchmark testset is kept since it exercises forward solves and
Zygote adjoints for both in-place and out-of-place problems.

Core, Applications, and QA test groups all pass locally against the
updated stack with no source changes required.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Fix GPU ForwardDiff seeding and Julia 1.10 Statistics compat

ForwardDiff 1.x rewrote seed! to use scalar setindex! loops (via
structural_eachindex), which errors on GPU arrays with scalar indexing
disallowed; ForwardDiff 0.10 used broadcast and worked. This broke every
NonlinearReformulation/BokhovenIterativeAlgorithm CUDA test through
SimpleNonlinearSolve's ForwardDiff jacobians. Add broadcast-based seed!
overloads for AbstractGPUArray in the existing type-piracy section
(should eventually live upstream in ForwardDiff as a GPUArraysCore
extension), with a JLArrays-based regression test covering vector and
chunk mode.

Also relax the Statistics compat to "1.10, 1.11": the stdlib is version
1.10.0 on Julia 1.10, so "1.11.1" was unsatisfiable on the downgrade CI
jobs which run on the LTS.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Retrigger CI: CUDA Core job hit out-of-GPU-memory at context creation (runner infrastructure issue)

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Remove ForwardDiff seed! type piracy, document the GPU limitation instead

Per review feedback on #63: drop the pirated broadcast-based seed!
overloads for AbstractGPUArray (and the JLArrays regression test that
exercised them) and document the limitation instead, in the README CUDA
footnote and next to DEFAULT_NLSOLVER. With ForwardDiff >= 1,
ForwardDiff-based jacobians seed dual arrays with scalar setindex! loops,
so solvers that compute jacobians through ForwardDiff error on CUDA
arrays until that is fixed upstream in ForwardDiff. The CUDA CI groups
will fail with ForwardDiff 1.x as a consequence.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Document ForwardDiff GPU seeding breakage in an issue; mark CUDA tests broken

Per review feedback: move the ForwardDiff >= 1 GPU limitation documentation
out of the README/source and into
#65, and mark the
affected CUDA tests with @test_broken so the GPU CI reflects the known
upstream breakage instead of erroring. Only InteriorPointMethod (which does
not use ForwardDiff jacobians) keeps regular assertions on CUDA; the
NonlinearReformulation/BokhovenIterativeAlgorithm solve and adjoint paths
are @test_broken until ForwardDiff restores broadcast-compatible seeding.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Keep regular assertions for the size(q) = (2, 1) batched CUDA adjoint

CI reported Unexpected Pass for this combination: it takes the batched
code path that does not go through ForwardDiff jacobian seeding, so it
still works on CUDA with ForwardDiff >= 1 and must not be @test_broken.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Rackauckas (Claude) <accounts@chrisrackauckas.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
@dependabot @github

dependabot Bot commented on behalf of github Jun 10, 2026

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Looks like these dependencies are no longer updatable, so this is no longer needed.

@dependabot dependabot Bot closed this Jun 10, 2026
@dependabot dependabot Bot deleted the dependabot/julia/test/all-julia-packages-6385b17d19 branch June 10, 2026 14:23
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