commit | 4e68894a1c54d763ac2697ea1ac993793da4107c | [log] [tgz] |
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author | Xun Liu <liuxun@apache.org> | Wed Mar 27 08:06:12 2024 +0800 |
committer | GitHub <noreply@github.com> | Wed Mar 27 08:06:12 2024 +0800 |
tree | aaa7971847081e682cb31eebeb8026afe934c7d1 | |
parent | 246ecee0706cad8cfa6a66006a7005c67ae235da [diff] |
fix security risk of SQL injection (#1121)
Apache Submarine (Submarine for short) is an End-to-End Machine Learning Platform to allow data scientists to create end-to-end machine learning workflows. On Submarine, data scientists can finish each stage in the ML model lifecycle, including data exploration, data pipeline creation, model training, serving, and monitoring.
Some open-source and commercial projects are trying to build an end-to-end ML platform. What's the vision of Submarine?
Theodore Levitt once said:
“People don’t want to buy a quarter-inch drill. They want a quarter-inch hole.”
experiment
on prem or cloud via easy-to-use UI/API/SDK.experiment
and dependencies of environment
.As mentioned above, Submarine attempts to provide Data-Scientist-friendly UI to make data scientists have a good user experience. Here're some examples.
# New a submarine client of the submarine server submarine_client = submarine.ExperimentClient(host='http://localhost:8080') # The experiment's environment, could be Docker image or Conda environment based environment = EnvironmentSpec(image='apache/submarine:tf-dist-mnist-test-1.0') # Specify the experiment's name, framework it's using, namespace it will run in, # the entry point. It can also accept environment variables. etc. # For PyTorch job, the framework should be 'Pytorch'. experiment_meta = ExperimentMeta(name='mnist-dist', namespace='default', framework='Tensorflow', cmd='python /var/tf_dist_mnist/dist_mnist.py --train_steps=100') # 1 PS task of 2 cpu, 1GB ps_spec = ExperimentTaskSpec(resources='cpu=2,memory=1024M', replicas=1) # 1 Worker task worker_spec = ExperimentTaskSpec(resources='cpu=2,memory=1024M', replicas=1) # Wrap up the meta, environment and task specs into an experiment. # For PyTorch job, the specs would be "Master" and "Worker". experiment_spec = ExperimentSpec(meta=experiment_meta, environment=environment, spec={'Ps':ps_spec, 'Worker': worker_spec}) # Submit the experiment to submarine server experiment = submarine_client.create_experiment(experiment_spec=experiment_spec) # Get the experiment ID id = experiment['experimentId']
submarine_client.get_experiment(id)
submarine_client.wait_for_finish(id)
submarine_client.get_log(id)
submarine_client.list_experiments(status='running')
For a quick-start, see Submarine On K8s
(Available on 0.5.0, see Roadmap)
If you want to know more about Submarine's architecture, components, requirements and design doc, they can be found on Architecture-and-requirement
Detailed design documentation, implementation notes can be found at: Implementation notes
Read the Apache Submarine Community Guide
How to contribute Contributing Guide
Login Submarine slack channel: https://join.slack.com/t/asf-submarine/shared_invite
Issue Tracking: https://issues.apache.org/jira/projects/SUBMARINE
What to know more about what's coming for Submarine? Please check the roadmap out: https://cwiki.apache.org/confluence/display/SUBMARINE/Roadmap
From here, you can know the changelog and the issue tracker of different version of Apache Submarine.
Apache submarine: a unified machine learning platform made simple at EuroMLSys '22
The Apache Submarine project is licensed under the Apache 2.0 License. See the LICENSE file for details.