TensorStudio 1.15.0

Changelog

Unreleased

1.15.0 - 2026-07-07

• Completed the Graph, Compiler, And Runtime Systems roadmap batch as a
  constrained, inspectable graph runtime.
• Added TensorSpec, symbolic GraphTensor tracing, trace(), and
  compile_graph() for a supported subset of TensorStudio tensor programs.
• Added JSON graph serialization with save_graph() and load_graph().
• Added basic graph optimization passes: constant folding and scalar
  multiply-add fusion.
• Added ExecutableGraph runtime execution backed by TensorStudio eager tensor
  operations.
• Added runtime profiling hooks and static memory-planning metadata for graph
  execution.
• Documented graph limitations honestly: no arbitrary Python control-flow
  capture and no machine-code JIT in this release.

1.14.0 - 2026-07-07

• Completed the safe, testable Hardware Backends roadmap batch without claiming
  CUDA or Metal execution in CPU wheels.
• Added native CPU/CUDA/Metal Device descriptors, parsing, equality, and
  backend availability metadata.
• Added top-level Python hardware helpers: device, available_devices,
  backend_info, device_count, cuda_is_available, and
  metal_is_available.
• Added explicit tensor transfer APIs: Tensor.to_device(), Tensor.cpu(),
  and Tensor.to("cpu") while preserving dtype casts like Tensor.to("float64").
• Added device= keywords to public tensor factory helpers.
• Made native storage device-aware and reject unsupported accelerator
  allocation clearly.
• Added CMake gates for experimental CUDA and Metal metadata hooks.
• Added backend benchmark coverage and hardware backend documentation.
• Documented that real CUDA/Metal kernels remain deferred until accelerator
  builds and CI can validate them.

1.13.0 - 2026-07-07

• Completed the ordered Packaging, CI, And Release Quality roadmap section as
  one release batch.
• Added tools/verify_artifacts.py for clean wheel and sdist install smoke
  tests in isolated virtual environments.
• Expanded CI with cross-platform wheel/sdist artifact smoke jobs on Windows,
  Linux, and macOS.
• Hardened wheel, TestPyPI, and PyPI workflows with clean artifact verification
  before upload.
• Added benchmark report artifacts to release workflows.
• Added GitHub Pages docs publishing automation with strict MkDocs builds.
• Expanded macOS wheel coverage to include universal2 builds where supported by
  cibuildwheel.
• Added native ABI, wheel-tag, source-build, BLAS, and platform compatibility
  documentation.

1.12.0 - 2026-07-07

• Completed the ordered Serialization And Interchange roadmap section as one
  release batch.
• Expanded NPZ archives to version 2 metadata with TensorStudio version,
  tensor counts, tensor shapes/dtypes, requires_grad flags, and user metadata.
• Added optional SafeTensors save/load support for safe tensor-only weight maps.
• Added model metadata inspection for TensorStudio NPZ files, SafeTensors,
  supported ONNX files, and trusted TensorStudio checkpoints.
• Added versioned checkpoint metadata and compatibility checks for full trusted
  checkpoints.
• Expanded ONNX export for grouped convolution metadata and ConvTranspose2d.
• Added ONNX graph metadata inspection and supported-subset ONNX import/execution
  for static graphs using TensorStudio's eager tensor ops.
• Added model-card metadata JSON export.
• Expanded interchange tests and docs for SafeTensors, metadata inspection, and
  ONNX import.

1.11.0 - 2026-07-07

• Completed the ordered Computer Vision Depth roadmap section as one release
  batch.
• Added batch-aware resize/crop/normalize helpers plus color jitter, random
  resized crop, random rotation, affine transforms, cutout, mixup, and CutMix.
• Added detection utilities for box areas, IoU variants, NMS, box
  encode/decode, coordinate conversion, and anchor generation.
• Added segmentation helpers for mask IoU, one-hot conversion, masks-to-boxes,
  nearest mask resize, and deterministic mask crops.
• Added DetectionFolder and SegmentationFolder datasets plus
  tensorstudio.data factory aliases for detection and segmentation folders.
• Added ResidualBlock, DepthwiseSeparableBlock, CompactUNet, and
  make_unet() vision model helpers using the native-backed neural-network
  layer stack.
• Added prediction drawing, mask overlay, and feature-map grid visualization
  helpers.
• Expanded vision tests and documentation for transforms, detection,
  segmentation, model blocks, and visualization.

1.10.0 - 2026-07-07

• Completed the ordered Training And Project Workflows roadmap section as one
  release batch.
• Added ArrayDataset, tensor/array/image-folder dataset factories,
  deterministic train/validation splitting, and dataset metadata summaries.
• Added tensorstudio.metrics with regression, classification, and multilabel
  metrics for small supervised workflows.
• Added trainer validation loops, scheduler stepping, callback context support,
  learning-rate logging, CSV logging, checkpoint callbacks, and early stopping.
• Added JSON, TOML, and YAML project config loading plus deterministic seeding
  across TensorStudio, NumPy, and Python random.
• Expanded full checkpoints with scheduler and epoch state and added
  resume_checkpoint() for continuing training runs.
• Added generated regression, classification, and vision project templates.
• Expanded tests and docs for project workflows, metrics, dataset creation,
  callbacks, configs, templates, and checkpoint resume.

1.9.0 - 2026-07-07

• Completed the ordered Neural Network Building Blocks roadmap section as one
  release batch.
• Added native grouped conv2d, native conv_transpose2d, and native
  embedding lookup with autograd support.
• Added Python-level Conv1d, DepthwiseConv2d, ConvTranspose2d,
  BatchNorm1d, BatchNorm2d, LayerNorm, Embedding,
  adaptive/global pooling, and additional activation modules.
• Added tensorstudio.nn.init with Xavier, Kaiming, normal, uniform, zero, and
  one initializers.
• Added label-smoothing cross entropy, focal loss, KL divergence, negative log
  likelihood, and cosine embedding loss modules and functional helpers.
• Added module buffers, buffer-aware state_dict() support, and model summary
  utilities for parameters, shapes, and estimated tensor memory.
• Expanded tests and docs for the section-5 neural-network API surface.

1.8.0 - 2026-07-07

• Completed the ordered Autograd Coverage And Hardening roadmap section as one
  release batch.
• Added retain_graph support to Tensor.backward() and
  tensorstudio.autograd.backward().
• Added graph lifecycle hardening: normal backward frees non-leaf graph history,
  repeated backward through a freed graph raises a clear error, and retained
  graphs clear intermediate gradients between backward passes.
• Added Tensor is_leaf, clear_history(), and detach_() controls for
  explicit graph lifecycle management.
• Added guarded public in-place methods zero_(), fill_(), and add_().
  They reject gradient-tracked mutation while grad mode is enabled and work
  inside tensorstudio.no_grad().
• Expanded non-scalar backward and finite-difference gradient tests for stable
  probability ops, statistics, norms, and batched matrix multiplication.
• Expanded autograd documentation with a coverage matrix, lifecycle notes, and
  explicit higher-order-gradient limitations.

1.7.0 - 2026-07-07

• Completed the ordered Core Math Expansion roadmap section as one release
  batch.
• Added native C++ logsumexp, softmax, and log_softmax operations with
  max-shifted stable numerics and autograd support.
• Added native C++ batched matrix multiplication through bmm and 3D @
  dispatch, including reverse-mode gradients for both operands.
• Added native C++ var, variance, std, all, and any operations, plus
  Tensor methods and top-level Python exports where appropriate.
• Added Tensor-level norm() and expanded tensorstudio.math with
  logsumexp, softmax, log_softmax, boolean reductions, and a documented
  practical einsum subset.
• Added seeded native random distributions: uniform, normal, randint,
  and bernoulli, with Python *_like helpers where useful.
• Switched neural-network functional softmax/log-softmax and cross entropy to
  the native stable kernels.
• Added NumPy parity and autograd tests for the expanded math surface.

1.6.0 - 2026-07-07

• Completed the ordered CPU Performance Core roadmap section as one release
  batch.
• Added optional CBLAS/Accelerate-backed matmul for contiguous float32 and
  float64 matrices when the source build environment exposes a compatible
  BLAS library and header; portable C++ kernels remain the fallback path.
• Added a small native C++ CPU thread pool, configurable with
  tensorstudio.set_num_threads() and TENSORSTUDIO_NUM_THREADS, and used it
  for large contiguous elementwise ops, reductions, matrix multiplication,
  convolution, and pooling forward kernels.
• Added SIMD-friendly typed float32/float64 contiguous kernels for common
  elementwise arithmetic and activations, while preserving mixed-dtype fallback
  behavior.
• Added a bounded C++ storage reuse pool for tensor allocations, with
  TENSORSTUDIO_DISABLE_STORAGE_POOL=1 and
  TENSORSTUDIO_STORAGE_POOL_MAX_BLOCK_BYTES controls.
• Added tensorstudio.performance_info(), get_num_threads(), and
  set_num_threads() diagnostics/configuration helpers.
• Added benchmark threshold support via benchmark_all.py --check-thresholds
  and benchmarks/thresholds.json.
• Expanded performance, CPU backend, and platform docs for BLAS selection,
  threading, storage reuse, benchmark thresholds, and honest interpretation.

1.5.1 - 2026-07-07

• Completed the next ordered correctness-roadmap item with clearer native
  shape, dtype, and indexing error messages.
• Broadcasting errors now include the mismatched axis and dimensions.
• Reshape errors now include requested element counts and inferred-shape
  context.
• DType errors now validate Python dtype inputs and list supported dtype names
  and aliases.
• Indexing errors now include tensor shape/rank context and the unsupported
  Python index type/value where practical.
• Added focused regression tests for the improved diagnostics.

1.5.0 - 2026-07-07

• Completed the next ordered correctness-roadmap item with native C++ view and
  layout operations: squeeze, unsqueeze, permute, and general
  N-dimensional transpose.
• Added Python Tensor methods, functional APIs, top-level exports, and type
  stubs for the new layout operations.
• Added autograd support for the new metadata views, including non-contiguous
  gradient handling through reshape chains.
• Added NumPy parity tests for view semantics, strides, shape errors, and
  autograd behavior.
• Updated tensor, API, autograd, and roadmap docs for the expanded view API.

1.4.0 - 2026-07-07

• Supersedes the short-lived 1.3.7 indexing release as a minor version
  because the indexing/view API is a larger public surface change than a patch.
• Completed the next ordered correctness-roadmap item with native C++ basic
  indexing and slicing views.
• Added Python Tensor.__getitem__ support for integers, slices with steps,
  tuples, ellipsis, and None/newaxis while rejecting advanced list, tensor,
  and boolean indexing clearly.
• Added autograd scatter-back support for differentiable integer/slice views.
• Added NumPy parity tests for common indexing and slicing cases, scalar
  indexing, negative strides, empty slices, unsupported indexing errors, and
  indexing autograd.
• Expanded docs into multi-page sections for sparse documentation folders:
  data, performance, project workflows, release process, and roadmap.

1.3.6 - 2026-07-07

• Completed the next ordered correctness-roadmap item with native C++
  where, maximum, and minimum operations.
• Exposed top-level comparison helpers, clamp/clip, selection helpers, and
  Tensor method helpers through the Python API while keeping kernels in C++.
• Added NumPy parity and autograd tests for where, maximum, and minimum,
  including broadcasting and tie-gradient behavior.
• Reorganized the MkDocs tree into categorized sections and added deeper docs
  for installation, source builds, native C++ architecture, broadcasting,
  gradient behavior, model formats, dataset creation, and the C++-first policy.
• Added GitHub Linguist attributes so language statistics emphasize the native
  C++ library and do not count docs, examples, tests, generated site output, or
  benchmark scripts as implementation code.

1.3.5 - 2026-07-07

• Completed the next ordered correctness-roadmap item with native C++
  argmax and argmin operations.
• Added Tensor methods, functional ops, top-level Python exports, and type
  stubs for arg reductions.
• Added NumPy parity tests for all-element and axis arg reductions, including
  keepdims, negative axes, first-tie behavior, dtype, and error cases.
• Documented arg-reduction semantics and limitations.

1.3.4 - 2026-07-07

• Completed the next ordered correctness-roadmap item with tuple/list-axis
  reductions for sum, mean, max, and min.
• Added C++ binding-layer axis normalization for reduction methods and
  functional ops, including negative-axis handling, duplicate-axis errors, and
  axis=() no-op behavior.
• Added NumPy parity tests and autograd coverage for tuple-axis reductions.
• Added ordered roadmap entries for supported ONNX/model metadata extraction,
  supported ONNX import/execution, safe model-format research, and image/tensor
  dataset creation helpers.

1.3.3 - 2026-07-07

• Hardened CI type checks for newer Python/mypy combinations by annotating
  NumPy temporaries in vision transform and visualization helpers.
• This patch does not change runtime behavior.

1.3.2 - 2026-07-07

• Completed the second ordered roadmap item with exhaustive binary
  elementwise broadcasting tests.
• Added NumPy comparison coverage for broadcasted add, sub, mul, div,
  equality, inequality, and ordered comparisons.
• Added incompatible-broadcast error tests and operator-overload broadcasting
  parity tests.

1.3.1 - 2026-07-07

• Started the ordered roadmap release train with the dtype-promotion contract.
• Exposed native promote_types plus Python tensorstudio.dtypes,
  promote_types, result_type, normalize_dtype, and dtype_of helpers.
• Documented arithmetic, division, and comparison dtype result rules.
• Added tests that pin binary operation result dtypes across supported dtypes.

1.3.0 - 2026-07-07

• Expanded tensorstudio.vision into a broader computer-vision toolkit with
  image IO, transform pipelines, deterministic augmentation helpers, image
  grids, and bounding-box drawing.
• Added ImageFolder and ImageList datasets for local image classification
  workflows.
• Added classification metrics (accuracy, top_k_accuracy,
  confusion_matrix) and detection utility box_iou.
• Added reusable vision modules ConvBlock, ImageClassifier, and
  make_image_classifier.
• Updated ONNX export flattening so nested vision model blocks export through
  their supported TensorStudio layers.
• Added an ImageFolder classification example and expanded vision tests.
• Added native C++ advanced math ops (sin, cos, tan, asin, acos,
  atan, log1p, rsqrt) with autograd coverage.
• Added tensorstudio.math helpers for square, reciprocal, variance, standard
  deviation, and norms.
• Added tensorstudio.project with JSON project configs, run folders, a small
  eager Trainer, safe NPZ state-dict checkpoints, and trusted full
  checkpoints.
• Added a project-training example and project workflow tests.

1.2.0 - 2026-07-07

• Added save_npz and load_npz for non-pickle tensor and flat
  state_dict files using NumPy NPZ archives plus TensorStudio JSON metadata.
• Added optional ONNX export through tensorstudio.export_onnx and
  tensorstudio.interchange.export_onnx for supported Sequential module
  stacks.
• Added tensorstudio.vision with image-to-tensor conversion, normalization,
  center crop, nearest-neighbor resize, and a small TinyConvClassifier.
• Added tests for NPZ roundtrips, ONNX graph validation, and vision classifier
  forward/backward behavior.
• Updated README and MkDocs pages for file formats, ONNX limits, and vision
  workflows.

1.1.0 - 2026-07-07

• Added native CPU NCHW conv2d with stride, padding, dilation, optional bias,
  and reverse-mode gradients for input, weight, and bias.
• Added tensorstudio.conv2d, tensorstudio.ops.conv2d,
  tensorstudio.nn.functional.conv2d, and tensorstudio.nn.Conv2d.
• Added convolution tests, finite-difference autograd coverage, and a
  standalone convolution benchmark script.
• Added convolution rows to the full benchmark report and updated docs to mark
  convolution as partially implemented rather than missing.
• Added native CPU NCHW max_pool2d and avg_pool2d operations with autograd,
  Python top-level and functional APIs, nn.MaxPool2d, nn.AvgPool2d, tests,
  docs, and benchmark coverage.
• Added native single-axis sum, mean, max, and min reductions with
  keepdims and reverse-mode gradients.
• Added a typed contiguous 2D fast path for common axis sum/mean
  reductions.
• Added nn.softmax, nn.log_softmax, nn.cross_entropy, and
  nn.CrossEntropyLoss for small multiclass classification workloads.
• Added native astype, concat, and stack operations with Python wrappers,
  Tensor methods for casting, and autograd support for floating tensors.

1.0.1

• Added test_all.py for one-command local release checks covering lint,
  typing, tests, examples, docs, build, and package metadata validation.
• Added benchmark_all.py for one-command full benchmark report generation.
• Added explicit benchmark win columns for NumPy, TensorFlow, PyTorch, and JAX,
  plus fastest-library reporting for each benchmark case.
• Hardened release publishing triggers so future v* tags are eligible for
  the publish workflow instead of only v1.0.0.
• Updated docs to describe benchmark interpretation, local release checks, and
  site navigation for benchmark, hardware, roadmap, and usage pages.

1.0.0

• Promoted the CPU-only stable API foundation from release candidate status.
• Added direct C++ buffer copies for NumPy import/export paths.
• Added faster typed tensor construction, flat-vector export, contiguous tensor
  copy, and floating-point optimizer update paths.
• Added a float32 contiguous matmul fast path that avoids double accumulation
  and per-element dtype conversion for the common training dtype.
• Added typed contiguous reduction kernels for sum and mean.
• Expanded benchmark reports with framework version metadata and PyTorch
  win/loss summaries.
• Updated documentation to describe honest 1.0.0 performance boundaries.

1.0.0rc2

• Added *_like tensor creation helpers for zero, one, full, empty, uniform
  random, and normal random tensors.
• Added nn.Identity, nn.LeakyReLU, nn.Softplus,
  nn.BCEWithLogitsLoss, nn.HuberLoss, and matching functional helpers.
• Expanded nn.Module with child/module introspection, trainable parameter
  filtering, freeze/unfreeze helpers, parameter counting, recursive apply,
  and richer repr details.
• Added optimizer utilities for gradient clipping and lightweight
  StepLR/ExponentialLR learning-rate schedulers.
• Added len(DataLoader) batch counting with drop_last support.
• Added typed C++ fast paths for contiguous elementwise/unary kernels and a
  contiguous matmul kernel that avoids per-element Tensor method dispatch.
• Bumped the next release candidate after public 1.0.0rc1 artifacts.

1.0.0rc1

• Release-candidate hardening toward a CPU-only v1.0.0 API foundation.
• Added additional tensor creation helpers, math operations, comparisons,
  no-grad mode, state APIs, data utilities, and Windows-first CI/release docs.
• This is not final v1.0.0; remaining platform checklist items must pass before
  a stable release.

0.1.1

• Publishable wheel release for CPython 3.10-3.13.
• Fixed MSVC build portability by removing non-portable ssize_t usage.
• Updated wheel CI to build 64-bit Windows wheels and modern Linux wheels.
• Expanded documentation with MkDocs navigation and detailed usage guides.

0.1.0

• Initial experimental release.
• C++20 CPU tensor core with Python bindings.
• Basic broadcasting, matrix multiplication, reductions, activations, and views.
• Reverse-mode autograd for the initial operation set.
• Python nn, optim, serialization, examples, tests, docs, and CI workflows.