| Name | executorch JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | On-device AI across mobile, embedded and edge for PyTorch |
| upload_time | 2024-04-29 23:46:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | BSD License For "ExecuTorch" software Copyright (c) Meta Platforms, Inc. and affiliates. Copyright 2023 Arm Limited and/or its affiliates. Copyright (c) Qualcomm Innovation Center, Inc. Copyright (c) 2023 Apple Inc. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name Meta nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
pytorch
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
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No coveralls.
|
**ExecuTorch** is a [PyTorch](https://pytorch.org/) platform that provides
infrastructure to run PyTorch programs everywhere from AR/VR wearables to
standard on-device iOS and Android mobile deployments. One of the main goals for
ExecuTorch is to enable wider customization and deployment capabilities of the
PyTorch programs.
The `executorch` pip package is in alpha.
* Required python version: `==3.10`
* Compatible systems: Linux x86_64, macOS aarch64
The prebuilt `executorch.extension.pybindings.portable_lib` module included in
this package provides a way to run ExecuTorch `.pte` files, with some
restrictions:
* Only [core ATen
operators](https://pytorch.org/executorch/stable/ir-ops-set-definition.html)
are linked into the prebuilt module
* Only the [XNNPACK backend
delegate](https://pytorch.org/executorch/main/native-delegates-executorch-xnnpack-delegate.html)
is linked into the prebuilt module
Please visit the [ExecuTorch website](https://pytorch.org/executorch/) for
tutorials and documentation. Here are some starting points:
* [Getting
Started](https://pytorch.org/executorch/stable/getting-started-setup.html)
* Set up the ExecuTorch environment and run PyTorch models locally.
* [Working with
local LLMs](https://pytorch.org/executorch/stable/llm/getting-started.html)
* Learn how to use ExecuTorch to export and accelerate a large-language model
from scratch.
* [Exporting to
ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial.html)
* Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and
optimizing its performance using quantization and hardware delegation.
* Running LLaMA on
[iOS](https://pytorch.org/executorch/stable/llm/llama-demo-ios.html) and
[Android](https://pytorch.org/executorch/stable/llm/llama-demo-android.html)
devices.
* Build and run LLaMA in a demo mobile app, and learn how to integrate models
with your own apps.
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