{"id":13475478,"url":"https://github.com/determined-ai/determined","last_synced_at":"2025-05-14T22:05:39.312Z","repository":{"id":36986432,"uuid":"253846879","full_name":"determined-ai/determined","owner":"determined-ai","description":"Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. 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align=\"center\"\u003e\u003cimg width=\"400\" src=\"determined-logo.svg\" alt=\"Determined AI Logo\"\u003e\u003c/p\u003e\n\nDetermined is an all-in-one deep learning platform, compatible with PyTorch and TensorFlow.\n\nIt takes care of:\n\n- Distributed training for faster results.\n- Hyperparameter tuning for obtaining the best models.\n- Resource management for cutting cloud GPU costs.\n- Experiment tracking for analysis and reproducibility.\n\n\u003cbr/\u003e\n\n\u003cp align=\"center\"\u003e\n\u003cimg alt=\"Features gif\" src=\"./docs/assets/readme_images/features.gif\"\u003e\n\u003c/p\u003e\n\n# How Determined Works\n\nThe main components of Determined are the Python library, the command line interface (CLI), and the Web UI.\n\n## Python Library\n\nUse the Python library to make your existing PyTorch or Tensorflow code compatible with Determined.\n\nYou can do this by organizing your code into one of the class-based APIs:\n\n```python\nfrom determined.pytorch import PyTorchTrial\n\nclass YourExperiment(PyTorchTrial):\n  def __init__(self, context):\n    ...\n```\n\nOr by using just the functions you want, via the Core API:\n\n```python\nimport determined as det\n\nwith det.core.init() as core_context:\n    ...\n```\n\n## Command Line Interface (CLI)\n\nYou can use the CLI to:\n\n- Start a Determined cluster locally:\n\n```\ndet deploy local cluster-up\n```\n\n- Launch Determined on cloud services, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP):\n\n```\ndet deploy aws up\n```\n\n- Train your models:\n\n```bash\ndet experiment create gpt.yaml .\n```\n\nConfigure everything from distributed training to hyperparameter tuning using YAML files:\n\n```yaml\nresources:\n  slots_per_trial: 8\n  priority: 1\nhyperparameters:\n  learning_rate:\n    type: double\n    minval: .0001\n    maxval: 1.0\nsearcher:\n  name: adaptive_asha\n  metric: validation_loss\n  smaller_is_better: true\n```\n\n## Web UI\n\nUse the Web UI to view loss curves, hyperparameter plots, code and configuration snapshots, model registries, cluster utilization, debugging logs, performance profiling reports, and more.\n\n![Web UI](docs/assets/readme_images/webui.png)\n\n# Installation\n\nTo install the CLI:\n\n```bash\npip install determined\n```\n\nThen use `det deploy` to start the Determined cluster locally, or on cloud services like AWS and GCP.\n\nFor installation details, visit the the cluster deployment guide for your environment:\n\n- [Local (on-prem)](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/on-prem/overview.html)\n- [AWS](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/aws/overview.html)\n- [GCP](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/gcp/overview.html)\n- [Kubernetes](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/k8s/overview.html)\n- [Slurm/PBS](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/slurm/overview.html)\n\n# Examples\nGet familiar with Determined by exploring the 30+ examples in the [examples folder](https://github.com/determined-ai/determined/tree/main/examples) and the [determined-examples repo](https://github.com/determined-ai/determined-examples).\n\n# Documentation\n\n- [Documentation](https://docs.determined.ai)\n- [Quick Start Guide](https://docs.determined.ai/latest/getting-started.html)\n- Tutorials:\n  - [PyTorch MNIST Tutorial](https://docs.determined.ai/latest/tutorials/pytorch-mnist-tutorial.html)\n  - [TensorFlow Keras MNIST Tutorial](https://docs.determined.ai/latest/tutorials/tf-mnist-tutorial.html)\n- User Guides:\n  - [Core API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/api-core-ug.html)\n  - [PyTorch API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/api-pytorch-ug.html)\n  - [Keras API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/api-keras-ug.html)\n  - [DeepSpeed API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/deepspeed/overview.html)\n\n# Community\n\nIf you need help, want to file a bug report, or just want to keep up-to-date\nwith the latest news about Determined, please join the Determined community!\n\n- [Slack](https://determined-community.slack.com) is the best place to\n  ask questions about Determined and get support. [Click here to join our Slack](https://determined-community.slack.com).\n- You can also follow us on [YouTube](https://www.youtube.com/@DeterminedAI) and [Twitter](https://www.twitter.com/DeterminedAI).\n- You can also join the [community mailing list](https://groups.google.com/a/determined.ai/forum/#!forum/community)\n  to ask questions about the project and receive announcements.\n- To report a bug, [open an issue](https://github.com/determined-ai/determined/issues) on GitHub.\n- To report a security issue, email [`security@determined.ai`](mailto:security@determined.ai).\n\n# Contributing\n\n[Contributor's Guide](CONTRIBUTING.md)\n\n# License\n\n[Apache V2](LICENSE)\n","funding_links":[],"categories":["🛠️ General ML Testing Frameworks","Go","AutoML","Model Training Orchestration","Open Source Projects at HPE","Profiling","Model Training and Orchestration","Scheduling","其他_机器学习与深度学习","Python","Tools","tensorflow","Researchers","Uncategorized","Tools and projects"],"sub_categories":["Profiling","General-Purpose Machine Learning","Misc","Tools","Uncategorized","LLM"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdetermined-ai%2Fdetermined","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdetermined-ai%2Fdetermined","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdetermined-ai%2Fdetermined/lists"}