CairnML
CairnML records what your training scripts do (parameters, metrics, logs, artifacts), runs them on remote machines through job queues, and versions datasets with git and DVC so every run records the exact data it used.
This is the Python SDK. It needs a CairnML server (see cairnml-server and the deployment files).
CairnML is an unofficial, modified version of ClearML. It is not created, sponsored, endorsed or supported by ClearML, Inc. See DISCLAIMER.md and NOTICE.
Install
pip install cairnml
pip install "cairnml[dataset]" # adds the dataset tools (DVC, Parquet)
Use
Sign this machine in to your server once:
cairnml-init --server https://your-server:8008
Track a script:
from cairnml import Task
task = Task.init(project_name="demo", task_name="first run")
task.connect({"lr": 0.001})
task.get_logger().report_scalar("loss", "train", value=0.5, iteration=1)
Run a recorded task on an agent (cairnml-agent):
Task.enqueue(task, queue_name="default")
Version a dataset (a dataset is a git repository, its commit is its version):
cairnml-dataset init
cairnml-dataset add
cairnml-dataset commit -m "March measurements"
cairnml-dataset push
See docs/datasets.md for datasets, adapters and the cache.
License
Apache License 2.0, see LICENSE. Based on ClearML, Copyright 2025 ClearML Inc.
1 maintainer
Metadata
1 maintainer
Release files for cairnml 0.0.1
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