dml-util 0.0.18

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dml-util

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Overview

dml-util provides utilities and adapters for DaggerML, enabling seamless function wrapping, artifact storage, and execution in local and cloud environments. It is designed to work with the daggerml ecosystem.

Front-End vs Back-End

• Front-End (User Interface):
  • The main entrypoints for most users are the funkify decorator, S3Store, and the included adapters/runners (e.g. conda, hatch, ssh, batch -- all via funkify).
  • These let you wrap Python functions as DAG nodes, store artifacts, and run code in a variety of environments with minimal setup.
  • Beginners should start by using these included adapters and runners, as shown in the examples.
• Back-End (Advanced/Extensible):
  • The back-end consists of the adapters and runners themselves, which handle execution, state, and integration with cloud/local resources.
  • Advanced users can write their own adapters or runners by following the patterns in src/dml_util/adapters/ and src/dml_util/runners/.
  • See the source and docstrings for guidance.

Installation

pip install dml-util

Usage

Wrapping Functions as DAG Nodes

from daggerml import Dml
from dml_util import funkify

@funkify
def add_numbers(dag):
    """Add numbers together.
    
    Parameters
    ----------
    dag : DmlDag
        The DAG context provided by DaggerML.
    
    Returns
    -------
    int
        The sum of the input numbers.
    """
    dag.result = sum(dag.argv[1:].value())
    return dag.result

dml = Dml()
with dml.new("simple_addition") as dag:
    dag.add_fn = add_numbers
    dag.sum = dag.add_fn(1, 2, 3)
    print(dag.sum.value())  # Output: 6

S3 Storage

from dml_util import S3Store
s3 = S3Store()
uri = s3.put(b"my data", name="foo.txt").uri
print(uri)  # s3://<bucket>/<prefix>/data/foo.txt

Advanced: Docker, Batch, and ECR

from dml_util import dkr_build, funkify, S3Store

@funkify
def fn(dag):
    *args, denom = dag.argv[1:].value()
    dag.result = sum(args) / denom

s3 = S3Store()
tar = s3.tar(dml, ".")
img = dkr_build(tar, ["--platform", "linux/amd64", "-f", "Dockerfile"])
batch_fn = funkify(fn, data={"image": img.value()}, adapter=dag.batch.value())
result = batch_fn(1, 2, 3, 4)

Documentation

• All public functions and classes use numpy style docstrings.
• See the python-lib GitHub repo for core DaggerML usage.
• See the daggerml-cli GitHub repo for CLI usage.

License

dml-util is distributed under the terms of the MIT license.