Lightning ⚡ Colossal-AI
Efficient Large-Scale Distributed Training with Colossal-AI and Lightning AI
Installation
pip install -U lightning-colossalai
Usage
Simply set the strategy argument in the Trainer:
import lightning as L
trainer = L.Trainer(strategy="colossalai", precision="16-mixed", devices=...)
For more fine-grained tuning of Colossal-AI's parameters, pass the strategy object to the Trainer:
import lightning as L
from lightning_colossalai import ColossalAIStrategy
strategy = ColossalAIStrategy(...)
trainer = L.Trainer(strategy=strategy, precision="16-mixed", devices=...)
Find all configuration options in the docs!
Release files for lightning-colossalai 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| lightning-colossalai-0.1.0.tar.gz | 15.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lightning_colossalai-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 30.7 kB
Release files / lightning-colossalai-0.1.0.tar.gz
| Download URL | lightning-colossalai-0.1.0.tar.gz |
|---|---|
| Size | 15.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.11.2
|
Release files / lightning_colossalai-0.1.0-py3-none-any.whl
| Download URL | lightning_colossalai-0.1.0-py3-none-any.whl |
|---|---|
| Size | 14.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.11.2
|
Security for lightning-colossalai 0.1.0
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