Alpa is a system for training and serving large-scale neural networks.
Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code.
The key features of Alpa include:
💻 Automatic Parallelization. Alpa automatically parallelizes users' single-device code on distributed clusters with data, operator, and pipeline parallelism.
🚀 Excellent Performance. Alpa achieves linear scaling on training models with billions of parameters on distributed clusters.
✨ Tight Integration with Machine Learning Ecosystem. Alpa is backed by open-source, high-performance, and production-ready libraries such as Jax, XLA, and Ray.
Alpa provides a free, unlimited OPT-175B text generation service. Try our service at https://opt.alpa.ai/ and share your prompting results!
Join Alpa slack and let us know any new features you want!
Use Alpa's decorator @parallelize to scale your single-device training code to distributed clusters.
import alpa
# Parallelize the training step in Jax by simply using a decorator
@alpa.parallelize
def train_step(model_state, batch):
def loss_func(params):
out = model_state.forward(params, batch["x"])
return jnp.mean((out - batch["y"]) ** 2)
grads = grad(loss_func)(model_state.params)
new_model_state = model_state.apply_gradient(grads)
return new_model_state
# The training loop now automatically runs on your designated cluster
model_state = create_train_state()
for batch in data_loader:
model_state = train_step(model_state, batch)Check out the Alpa Documentation site for installation instructions, tutorials, examples, and more.
- Alpa OSDI 2022 paper
- Google AI blog
- Alpa talk slides
- ICML 2022 Big Model Tutorial slides
- ICML 2022 Big Model Tutorial video recording
- Prof. Ion Stoica introduces the Alpa system
- Please read the contributor guide if you are interested in contributing to Alpa.
- Connect to Alpa contributors via the Alpa slack.
Alpa is licensed under the Apache-2.0 license.