Train your own AI modelsUse Tracto to train your own AI models

Start building

Prepare a training dataset

Leverage pre-built functions to pull and process public data from CommonCrawl, Github or S3 storage

Store all datasets (raw, in-progress, final) in Tracto Cypress prior to model training

Leverage serverless GPUs and CPUs to run data preparation tasks at scale

Train your model

Use your favorite fine-tuning frameworks, like PyTorch, Jax, Hugging Face

Store checkpoints in Tracto Cypress

Scale training jobs to hunderds of GPUs

Monitor results on W&B

Post training

Improve pretrained model to handle specific tasks.

Evaluate trained model via offline inference

Deploy your model

Infer your model at scale.

Download model weights.

Infrastructure

Access GPU infra from Python code

Choose from pre-built Docker images or bring your own image

Run multi GPU training jobs with a few lines of code

Try it on TractoAI

Train an image classifier with PyTorch

Train an image classifier with Pytorch using distributed multi-node GPU acceleration

Teams choosing to build with TractoAI