| Name | alphai JSON |
| Version |
0.0.7
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2024-04-16 22:18:43 |
| maintainer | None |
| docs_url | None |
| author | Andrew Chang |
| requires_python | <3.12,>=3.9 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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No coveralls.
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# AlphAI
AlphAI is a high-level open-source Python toolkit designed for efficient AI development and in-depth GPU profiling. Supporting popular tensor libraries like [PyTorch](https://pytorch.org/get-started/locally/) and [Jax](https://github.com/google/jax), it optimizes developer operations on GPU servers and integrates seamlessly with [American Data Science Labs](https://dashboard.amdatascience.com), offering robust control over remote Jupyter Lab servers and environment runtimes.
## Features
- **GPU Profiling and Analytics**: Advanced GPU profiling capabilities to maximize resource efficiency and performance.
- **Benchmarking Tools**: Pythonic, easy-to-use tools for evaluating and comparing model performance.
- **Remote Jupyter Lab Integration**: Programmatic management of remote Jupyter Lab servers for enhanced productivity.
- **Local Tensor Model Support**: Streamlines the integration and management of tensor models from providers like Hugging Face.
- **Tensor Engine Compatibility**: Fully compatible with PyTorch, with upcoming support for Jax and TensorFlow.
## Quick Start
### Installation
Install AlphAI easily using pip:
```bash
pip install alphai
# If you'd like to install torch in a Linux machine with CUDA-drivers
pip install alphai[torch]
```
### Authentication Pre-requisites
Although not strictly required to use the computational functions of the alphai package, it is recommended to create an account at [American Data Science](https://dashboard.amdatascience.com) and generate an API key to make use of your two free remote Jupyter Lab servers.
You don't need an API key to use the GPU profiling, benchmarking, and generate modules.
### Basic Usage
Here's a quick example to get started with AlphAI:
```python
from alphai import AlphAI
# Initialize AlphAI
aai = AlphAI(
api_key=os.environ.get("ALPHAI_API_KEY"),
)
# Start remote Jupyter Lab servers
aai.start_server()
# Upload to your server's file system
aai.upload("./main.py")
# Start python kernel and run code remotely
code = "print('Hello world!')"
aai.run_code(code)
```
## Documentation and Detailed Usage
For more documentation and detailed instructions on how to use AlphAI's various features, please refer to our [Documentation](https://alphai.amdatascience.com).
### [Working with Tensor Models](https://alphai.amdatascience.com/americandatascience/alphai/models/hugging-face/)
Guidance on integrating and leveraging tensor models.
### [GPU Profiling and Analytics](https://alphai.amdatascience.com/americandatascience/alphai/gpu-profiling/)
Comprehensive features for GPU profiling and analytics.
### [Integration with American Data Science Labs](https://alphai.amdatascience.com/americandatascience/alphai/servers/)
Discover the benefits of integrating AlphAI with American Data Science Labs.
## System Requirements
- Python 3.9+
- PyTorch (recommnended) or Jax (limited support)
- Linux OS i.e. Ubuntu 18.04+
## Contributing
We welcome contributions! Please see our [Contribution Guidelines](https://github.com/americandatascience/alphai/README.md) for more information.
## License
AlphAI is released under the [Apache 2.0](https://github.com/americandatascience/alphai/LICENSE.txt) license.
## Support and Contact
For support or inquiries about enterprise solutions, contact us at [info@amdatascience.com](mailto:info@amdatascience.com).
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"description": "# AlphAI\n\nAlphAI is a high-level open-source Python toolkit designed for efficient AI development and in-depth GPU profiling. Supporting popular tensor libraries like [PyTorch](https://pytorch.org/get-started/locally/) and [Jax](https://github.com/google/jax), it optimizes developer operations on GPU servers and integrates seamlessly with [American Data Science Labs](https://dashboard.amdatascience.com), offering robust control over remote Jupyter Lab servers and environment runtimes.\n\n## Features\n\n- **GPU Profiling and Analytics**: Advanced GPU profiling capabilities to maximize resource efficiency and performance.\n- **Benchmarking Tools**: Pythonic, easy-to-use tools for evaluating and comparing model performance.\n- **Remote Jupyter Lab Integration**: Programmatic management of remote Jupyter Lab servers for enhanced productivity.\n- **Local Tensor Model Support**: Streamlines the integration and management of tensor models from providers like Hugging Face.\n- **Tensor Engine Compatibility**: Fully compatible with PyTorch, with upcoming support for Jax and TensorFlow.\n\n## Quick Start\n\n### Installation\n\nInstall AlphAI easily using pip:\n\n```bash\npip install alphai\n\n# If you'd like to install torch in a Linux machine with CUDA-drivers\npip install alphai[torch]\n```\n\n### Authentication Pre-requisites\n\nAlthough not strictly required to use the computational functions of the alphai package, it is recommended to create an account at [American Data Science](https://dashboard.amdatascience.com) and generate an API key to make use of your two free remote Jupyter Lab servers.\n\nYou don't need an API key to use the GPU profiling, benchmarking, and generate modules.\n\n\n### Basic Usage\n\nHere's a quick example to get started with AlphAI:\n\n```python\nfrom alphai import AlphAI\n\n# Initialize AlphAI\n\naai = AlphAI(\n api_key=os.environ.get(\"ALPHAI_API_KEY\"),\n)\n\n# Start remote Jupyter Lab servers\naai.start_server()\n\n# Upload to your server's file system \naai.upload(\"./main.py\")\n\n# Start python kernel and run code remotely\ncode = \"print('Hello world!')\"\naai.run_code(code)\n\n```\n\n## Documentation and Detailed Usage\n\nFor more documentation and detailed instructions on how to use AlphAI's various features, please refer to our [Documentation](https://alphai.amdatascience.com).\n\n### [Working with Tensor Models](https://alphai.amdatascience.com/americandatascience/alphai/models/hugging-face/)\n\nGuidance on integrating and leveraging tensor models.\n\n### [GPU Profiling and Analytics](https://alphai.amdatascience.com/americandatascience/alphai/gpu-profiling/)\n\nComprehensive features for GPU profiling and analytics.\n\n### [Integration with American Data Science Labs](https://alphai.amdatascience.com/americandatascience/alphai/servers/)\n\nDiscover the benefits of integrating AlphAI with American Data Science Labs.\n\n## System Requirements\n\n- Python 3.9+\n- PyTorch (recommnended) or Jax (limited support)\n- Linux OS i.e. Ubuntu 18.04+\n\n## Contributing\n\nWe welcome contributions! Please see our [Contribution Guidelines](https://github.com/americandatascience/alphai/README.md) for more information.\n\n## License\n\nAlphAI is released under the [Apache 2.0](https://github.com/americandatascience/alphai/LICENSE.txt) license.\n\n## Support and Contact\n\nFor support or inquiries about enterprise solutions, contact us at [info@amdatascience.com](mailto:info@amdatascience.com).\n",
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