Side by side
Ray Train vs tinygrad
Both stand on deep learning software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Free plan LinuxMacSelf-hosted+1
Ray Train brings distributed training, data processing, and experiment tracking into the Ray cluster workflow. Its security guidance recommends separate clusters when workloads need isolation.
Visit Ray Train Free plan LinuxMacSelf-hosted+2
tinygrad combines training tools with a broad set of listed accelerators and multi-GPU tensor sharding. Its pre-1.0 status and stated performance caveat are important considerations for prospective users.
Visit tinygradThe facts
| Ray Train | tinygrad | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | Linux, Mac, Self-hosted, Windows | Linux, Mac, Self-hosted, Web, Windows |
| Lowest paid plan | Free or on request | Free or on request |
| Plans published | 1 | 1 |
| Maker | Not published | Not published |
| Website | ray.io | tinygrad.org |
| On deep learning software | #22 of 35 | #26 of 35 |
Ray Train: what it does well
- Integrates with PyTorch, XGBoost, JAX, and others.
- Supports Ray Data and framework-native datasets.
- Includes guides for metrics and experiment tracking.
- Supports distributed training on CPUs or GPUs.
- Open source and listed as free.
What to know first
- No job isolation within a cluster.
- No developer access controls within a cluster.
tinygrad: what it does well
- Supports forward and backward automatic differentiation.
- Includes neural network classes and optimizers.
- Tensor sharding across multiple GPUs is documented.
- Listed accelerators include CUDA, Metal, and WebGPU.
- Available under the MIT license.
What to know first
- The project is not yet version 1.0.
- FAQ says it is not faster than PyTorch for most uses.
- Source installation is recommended.
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