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https://github.com/determined-ai/determined
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
https://github.com/determined-ai/determined
data-science deep-learning distributed-training hyperparameter-optimization hyperparameter-search hyperparameter-tuning keras kubernetes machine-learning ml-infrastructure ml-platform mlops pytorch tensorflow
Last synced: 16 days ago
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Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
- Host: GitHub
- URL: https://github.com/determined-ai/determined
- Owner: determined-ai
- License: apache-2.0
- Created: 2020-04-07T16:12:29.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-05-21T18:44:33.000Z (6 months ago)
- Last Synced: 2024-05-21T18:51:43.988Z (6 months ago)
- Topics: data-science, deep-learning, distributed-training, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, keras, kubernetes, machine-learning, ml-infrastructure, ml-platform, mlops, pytorch, tensorflow
- Language: Go
- Homepage: https://determined.ai
- Size: 198 MB
- Stars: 2,883
- Watchers: 81
- Forks: 345
- Open Issues: 120
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
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- StarryDivineSky - determined-ai/determined
README
Determined is an all-in-one deep learning platform, compatible with PyTorch and TensorFlow.
It takes care of:
- Distributed training for faster results.
- Hyperparameter tuning for obtaining the best models.
- Resource management for cutting cloud GPU costs.
- Experiment tracking for analysis and reproducibility.
# How Determined Works
The main components of Determined are the Python library, the command line interface (CLI), and the Web UI.
## Python Library
Use the Python library to make your existing PyTorch or Tensorflow code compatible with Determined.
You can do this by organizing your code into one of the class-based APIs:
```python
from determined.pytorch import PyTorchTrial
class YourExperiment(PyTorchTrial):
def __init__(self, context):
...
```
Or by using just the functions you want, via the Core API:
```python
import determined as det
with det.core.init() as core_context:
...
```
## Command Line Interface (CLI)
You can use the CLI to:
- Start a Determined cluster locally:
```
det deploy local cluster-up
```
- Launch Determined on cloud services, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP):
```
det deploy aws up
```
- Train your models:
```bash
det experiment create gpt.yaml .
```
Configure everything from distributed training to hyperparameter tuning using YAML files:
```yaml
resources:
slots_per_trial: 8
priority: 1
hyperparameters:
learning_rate:
type: double
minval: .0001
maxval: 1.0
searcher:
name: adaptive_asha
metric: validation_loss
smaller_is_better: true
```
## Web UI
Use the Web UI to view loss curves, hyperparameter plots, code and configuration snapshots, model registries, cluster utilization, debugging logs, performance profiling reports, and more.
# Installation
To install the CLI:
```bash
pip install determined
```
Then use `det deploy` to start the Determined cluster locally, or on cloud services like AWS and GCP.
For installation details, visit the the cluster deployment guide for your environment:
- [Local (on-prem)](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/on-prem/overview.html)
- [AWS](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/aws/overview.html)
- [GCP](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/gcp/overview.html)
- [Kubernetes](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/k8s/overview.html)
- [Slurm/PBS](https://docs.determined.ai/latest/setup-cluster/deploy-cluster/slurm/overview.html)
# Examples
Get 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).
# Documentation
- [Documentation](https://docs.determined.ai)
- [Quick Start Guide](https://docs.determined.ai/latest/getting-started.html)
- Tutorials:
- [PyTorch MNIST Tutorial](https://docs.determined.ai/latest/tutorials/pytorch-mnist-tutorial.html)
- [TensorFlow Keras MNIST Tutorial](https://docs.determined.ai/latest/tutorials/tf-mnist-tutorial.html)
- User Guides:
- [Core API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/api-core-ug.html)
- [PyTorch API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/api-pytorch-ug.html)
- [Keras API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/api-keras-ug.html)
- [DeepSpeed API](https://docs.determined.ai/latest/model-dev-guide/apis-howto/deepspeed/overview.html)
# Community
If you need help, want to file a bug report, or just want to keep up-to-date
with the latest news about Determined, please join the Determined community!
- [Slack](https://determined-community.slack.com) is the best place to
ask questions about Determined and get support. [Click here to join our Slack](https://determined-community.slack.com).
- You can also follow us on [YouTube](https://www.youtube.com/@DeterminedAI) and [Twitter](https://www.twitter.com/DeterminedAI).
- You can also join the [community mailing list](https://groups.google.com/a/determined.ai/forum/#!forum/community)
to ask questions about the project and receive announcements.
- To report a bug, [open an issue](https://github.com/determined-ai/determined/issues) on GitHub.
- To report a security issue, email [`[email protected]`](mailto:[email protected]).
# Contributing
[Contributor's Guide](CONTRIBUTING.md)
# License
[Apache V2](LICENSE)