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

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.

Check out our key concepts and architecture intro.

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Development guide.

Deployment guide.

• Azure
• YCloud