This release is a pre-release and may not be stable for production use.
covex-neuron
Run your PyTorch and Hugging Face code on AWS Trainium, unchanged from NVIDIA except for two lines
covex-neuron is built on TorchNeuron, the native PyTorch backend for Trainium2 and Trainium3. It handles the parts that make Trainium different from a GPU (bf16, fixed input shapes, the Neuron compiler) and runs your script on AWS by the second, next to an NVIDIA GPU for comparison.
Installation
pip install covex-neuron
Quickstart
Add two lines to an existing GPU script:
import covex_neuron as cn
device = cn.device() # "neuron" on Trainium, "cuda" on GPU
pipe = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english", device=device)
pipe = cn.prepare(pipe) # no-op on GPU / CPU
cn.prepare takes a transformers model (with its tokenizer), a transformers pipeline, or a diffusers pipeline.
Run it on a GPU and on Trainium from your laptop, using only your AWS credentials:
gpu = cn.run(["sentiment.py"], on="gpu") # 1x NVIDIA A10G
trn = cn.run(["sentiment.py"], on="trainium") # 1x Trainium2 chip
cn.compare(gpu, trn)
You pay per second while the job runs, and nothing keeps running afterwards.
Notebooks
| Notebook | Workload |
|---|---|
| 01 Sentiment analysis | transformers pipeline, DistilBERT SST-2 |
| 02 Text classification | AutoModelForSequenceClassification, emotion (7 classes) |
| 03 Image generation | diffusers, SD-Turbo |
Each one runs the same script on a GPU and on Trainium and prints the comparison.
Examples
examples/nvidia has sentiment analysis, text classification and image generation scripts as written for a GPU. examples/trainium has the same scripts with the two lines added. To run both and compare:
python examples/run_demo.py
License
Metadata
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Total release size: 35.2 kB
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