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Version: v0.21.x

Release notes

Version 0.21.2 (July 29th, 2026)​

New Features​

  • [AutoML] AutoML logs now show live experiment progress — status transitions, periodic runtime/ETA/loss updates, and a summary when each experiment finishes or fails.

Fixes​

  • [AutoML] Fixed AutoML showing reinforcement learning (RL) experiments as regular causal LM experiments, including an irrelevant output column option for them.
  • [AutoML] Fixed AutoML runs getting stuck showing "running" when a failure occurred partway through.
  • [AutoML] Fixed LLM-as-a-Judge being rejected as the optimization metric when starting AutoML through the assistant.
  • [AutoML] RL experiments in AutoML now always use LLM-as-a-Judge as the optimization metric, since no other metric applies. The metric selector is hidden for RL.
  • [Training] RL rollout prompts and completions are no longer written to training logs by default, preventing training data from leaking into logs.
  • [Model Zoo] Fixed an issue that prevented deployment of RL models.
  • [Datasets] Fixed CSV row counts being overstated for datasets containing multi-line fields.
  • [Datasets] Removed an unused output column from the RL demo dataset that incorrectly implied labels are required for RL training.
  • [Assistant] Fixed the assistant failing when using model names from a gateway's model catalog.

Version 0.21.1 (July 27th, 2026)​

New Features​

  • [Platform] Support for running against a customer-managed external Postgres database (for example, AWS Aurora), including optional IAM authentication.

Fixes​

  • [Platform] Improved performance of the experiments, deployments, and data generation list pages, which no longer load full task logs on every automatic refresh.
  • [Data Generation] Fixed an issue where data generation jobs could get stuck showing "running" instead of completing or failing.
  • [Training] Fixed a crash when training on datasets containing missing values.
  • [Experiments] Fixed a server error that broke the experiment page and blocked AutoML from starting the next experiment for experiments trained on JSON datasets.
  • [Platform] Fixed the settings page so a failed OpenAI models fetch no longer blocks the rest of the page from loading.

Version 0.21.0 (July 20th, 2026)​

New Features​

  • [Model Zoo] Model deployments now run through a shared, GPU-aware deployment manager instead of a dedicated worker per deployment, improving scalability and cost efficiency.

Version 0.20.2 (July 9th, 2026)​

New Features​

  • [Model Zoo] New batch inference API — submit batch jobs asynchronously, poll or cancel them, and stream progress or receive a webhook callback on completion. Batch jobs are priority-queued so they can't starve interactive traffic, with native batched support for vLLM, chat, NER, PII masking, the Danube3 classifier, and BART.
  • [Platform] Storage bucket names are now configurable via environment variables.

Fixes​

  • [UI] Fixed a dropdown selection issue affecting components using multi-select.
  • [Training] Fixed a training failure that occurred when input CSV files used CRLF line endings.
  • [Platform] Resolved a dependency conflict that could block installation.

Security​

  • Fixed server-side request forgery (SSRF) findings in frontend server actions and API routes.

Version 0.20.1 (July 2nd, 2026)​

New Features​

  • [Data Generation] You can now choose the output dataset's problem type when generating data.
  • [Platform] Further upgraded the core ML stack: PyTorch 2.11, vLLM 0.22.1, and CUDA 13 / cuDNN 9 base images. This raises the minimum required NVIDIA driver version.
  • [Model Zoo] Added per-model request queueing with backpressure, along with cache and queue status visibility via the health and status endpoints.

Fixes​

  • [Model Zoo] Fixed a crash during FLUX model inference caused by a dtype mismatch.

Version 0.20.0 (May 22nd, 2026)​

This release adds reinforcement learning training, a model cache for faster repeat inference, and upgrades the core ML stack.

New Features​

  • [Experiments] Training with reinforcement learning (RL) and LLM reward.
  • [Experiments] You can now select which columns to include or exclude for tabular problems, with automatic column suggestions.
  • [Platform] LRU model cache with a configurable VRAM budget, speeding up repeat inference.
  • [Platform] Core ML stack upgraded to Transformers v5, vLLM 0.19.1, and PyTorch 2.10, with a CUDA 12.8 toolchain across all services.
  • [Platform] Tabh2o moved to a dedicated on-prem HTTP service, improving air-gapped support.
  • [Platform] Improved Content Security Policy (CSP) headers.

Fixes​

  • [Datasets] Numerical columns are no longer misclassified as categorical during tabular data loading.
  • [Datasets] Fixed an issue that prevented new dataset uploads.
  • [Projects] Fixed an issue where renaming a project didn't work correctly.
  • [UI] Fixed a theme flicker and a hydration mismatch that could occur on page load.
  • [Platform] Worker components can now access the HF_TOKEN secret for gated Hugging Face model downloads.
  • [Platform] Swagger UI assets are now self-hosted, with HTTPS-aware security headers behind TLS-terminating ingresses.
  • [Platform] Fixed Model Zoo pod startup and Docker build failures.
  • [Platform] Fresh-namespace installs no longer show a temporary login error while the UI finishes starting up.
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