AI Train2015
Builds and trains deep learning models from one Python API across JAX, TensorFlow, PyTorch, and OpenVINO inference. Its real value is portability: model code, custom layers, and data pipelines can move across backends instead of locking into one stack.
AI Train2023
Patches Hugging Face Transformers and TRL with hand-written Triton kernels to fine-tune LLMs on a single consumer GPU up to 30x faster with about 90% less memory. Does LoRA, QLoRA, and full fine-tuning across 500+ models, exporting to GGUF and Safetensors.
AI Train2023
Trains and fine-tunes diffusion models on consumer GPUs: LoRA and LoKr for image families like FLUX.1/2, SDXL and Qwen-Image, plus video models such as Wan 2.x and LTX. Layer-specific targeting, configurable VRAM, and a browser dashboard for runs.
AI Train2014
Tianqi Chen, Carlos Guestrin +1University of Washington, Distributed Machine Learning Community (DMLC)
Trains gradient-boosted tree models across local and distributed environments, with bindings for Python, R, JVM, Julia, and C++. Its sparsity-aware split finding and quantile sketch made it a default baseline for tabular ML competitions.
AI Train2015
End-to-end quantitative trading framework that covers feature engineering, model training, signal generation and live execution; includes vnpy.alpha for ML-driven strategy research and workflow management, aimed at production trading and research pipelines.
AI Infra2015
Builds and deploys machine learning models across research, production, web, mobile, and edge environments. Its ecosystem spans Keras, TFX, LiteRT, TensorFlow.js, datasets, model hubs, and visualization tools.
AI Train2016
Trains gradient-boosted decision trees for classification, ranking, and large-scale tabular ML with lower memory use and faster training. GOSS and EFB help it handle high-dimensional sparse data on CPU, GPU, and distributed setups.
AI Infra2016
Meta (Facebook AI Research), PyTorch Foundation (Linux Foundation)PyTorch Foundation (Linux Foundation)
Lets researchers and engineers build neural networks as regular Python programs, with GPU-backed tensors, autograd, distributed training, and production paths through TorchScript and related tooling.
MLOps2017
Tracks every ML run — hyperparameters, metrics, checkpoints, dataset versions — into one dashboard you share as a live report, with Sweeps for tuning and a model registry. Weave extends it to LLM apps: tracing, evals, and production monitoring.
AI Train2017
Trains gradient-boosted decision trees with native categorical-feature handling, GPU acceleration, and production-ready prediction APIs. A strong fit for tabular ML when preprocessing categories into numeric features would add noise or leakage.
AI Train2017
Sequence modeling toolkit for training custom models for translation, summarization, and language modeling. Reference implementation behind RoBERTa, BART, mBART, XLM-R, and wav2vec 2.0, with multi-GPU and mixed-precision training.