No project description provided
Project description
ʎzy
ʎzy is a platform for a hybrid execution of ML workflows that transparently integrates local and remote runtimes with the following properties:
- Python-native SDK
- Automatic env (pip/conda) sync
- K8s-native runtime
- Resources allocation on-demand
- Env-independent results storage
Quick start
ʎzy allows running any python functions on a cluster by annotating them with @op
decorator:
@op(gpu=Gpu.any())
def train(data_set: Bunch) -> CatBoostClassifier:
cb_model = CatBoostClassifier(iterations=1000, task_type="GPU", devices='0:1', train_dir='/tmp/catboost')
cb_model.fit(data_set.data, data_set.target, verbose=True)
return cb_model
# local python function call
model = train(data_set)
# remote call on a cluster
env = LzyRemoteEnv()
with env.workflow("training"):
model = train(data_set)
Please read the tutorial for details. We provide a free sandbox installation.
Runtime
Check out our key concepts and architecture intro.
Community
Join our chat on telegram!
Development
Development guide.
Deployment
Deployment guide.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pylzy-1.0.0rc2.tar.gz
(76.2 kB
view hashes)
Built Distribution
pylzy-1.0.0rc2-py2.py3-none-any.whl
(110.6 kB
view hashes)
Close
Hashes for pylzy-1.0.0rc2-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d453dedc12aa678f2500f8f371e21c5cd306e95674b9c1e4a39fe2af8c139ddb |
|
MD5 | b7ed02a9dc08bc9f36bd218981ce9ff5 |
|
BLAKE2b-256 | cb30c191ec150ee2ba99bb4d2aedfae1b48f118f9b94b2fc280f1c27f59a6611 |