← Docs

Roadmap

· 2 min read

TensorBloom’s planned development path. Features are grouped by milestone, not ordered by priority within each milestone.

v0.2 — Data Pipeline

  • Data store — persistent dataset management: download once, reuse across projects, track dataset versions
  • Visual augmentation graph — drag-and-drop data preprocessing: resize, normalize, random crop, color jitter as connectable nodes with live preview
  • Data preview — sample visualizations in the property panel: thumbnail grid for images, text snippets for NLP, waveform plots for audio
  • Full HuggingFace support — complex/nested columns, streaming datasets, preprocessing UI with code editor, tested dataset catalog

v0.3 — Advanced Architectures

  • Multi-computation graphs — separate graphs for encoder, decoder, discriminator that compose into a training pipeline (enables GANs, VAEs, teacher-student)
  • Multi-loss training — multiple loss nodes with configurable weights (e.g., reconstruction loss + KL divergence for VAE)
  • Attention mask support — pass masks through Transformer nodes for proper padding handling
  • Subgraph nodes — collapse a group of nodes into a reusable, parameterized block that can be instantiated multiple times

v0.4 — Training at Scale

  • Distributed training — multi-GPU training with DDP, automatic device placement
  • Gradient accumulation — effective batch size scaling without increasing VRAM
  • Learning rate warmup — configurable warmup schedules (linear, cosine)
  • Experiment tracking — Weights & Biases and TensorBoard integration
  • Checkpoint management — visual checkpoint browser, resume from any epoch, compare checkpoints

v0.5 — Deployment

  • ONNX optimization — one-click quantization, operator fusion, graph optimization
  • Model serving — local REST endpoint for inference testing
  • Edge export — TensorFlow Lite, CoreML, and ONNX.js conversion
  • Model card generation — automatic documentation with architecture diagram, training metrics, dataset info

Future Ideas

  • Collaborative editing — multiplayer graph editing with presence indicators
  • Plugin system — custom node types, data loaders, and training hooks
  • Model zoo — community-shared architectures and pretrained weights
  • Automatic architecture search (NAS) — define search space visually, let the system find optimal architectures
  • Python notebook integration — import/export .ipynb files
  • Graph version control — diff and merge model architectures like code
  • Hyperparameter sweep UI — grid/random/Bayesian search with parallel runs

Contributing

Want to work on any of these? Check the issues on GitHub or open a new one to discuss your approach.