ae7160790d
* add nano installation panel * try fix * try fix again * fix typo * add versions * try fix second * try fix second again * fix col width * rollback * rollback again * fix syntax error * fix syntax error again * fix syntax error last * fix syntax * fix syntax * fix syntax again * add some comment * try fix * try fix * try fix again * try fix * fix typo * fix some error * fix typo * some optimization * change width * change width again * change width again * change width again * last width change * fix description error * change inference default to yes * switch inferenceyes and inferenceno
83 lines
3.5 KiB
Markdown
83 lines
3.5 KiB
Markdown
# Nano Installation
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Note: For windows users, we recommend using Windows Subsystem for Linux 2 (WSL2) to run BigDL-Nano. Please refer to [Nano Windows install guide](../Howto/windows_guide.md) for instructions.
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BigDL-Nano can be installed using pip and we recommend installing BigDL-Nano in a conda environment.
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You can select bigdl-nano along with some dependencies specific to PyTorch or Tensorflow using the following panel.
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```eval_rst
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.. raw:: html
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<link rel="stylesheet" type="text/css" href="../../../_static/css/nano_installation_guide.css" />
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<div class="displayed">
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<table id="table-1">
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<tbody>
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<tr>
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<td colspan="1">FrameWork</td>
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<td colspan="2"><button id="pytorch">Pytorch</button></td>
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<td colspan="2"><button id="tensorflow">Tensorflow</button></td>
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</tr>
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<tr id="version" class="taller_tr">
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<td colspan="1">Version</td>
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<td colspan="1"><button id="pytorch_113">1.13</button></td>
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<td colspan="1"><button id="pytorch_112">1.12</button></td>
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<td colspan="1"><button id="pytorch_111">1.11</button></td>
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<td colspan="1"><button id="pytorch_110">1.10</button></td>
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</tr>
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<tr>
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<td colspan="1">Inference Optimization</td>
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<td colspan="2"><button id="inferenceyes">Yes</button></td>
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<td colspan="2"><button id="inferenceno">No</button></td>
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</td>
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</tr>
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<tr>
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<td colspan="1">Release</td>
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<td colspan="2"><button id="nightly">Nightly</button></td>
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<td colspan="2"><button id="stable">Stable</button></td>
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</tr>
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<tr class="tallet_tr">
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<td colspan="1">Install CMD</td>
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<td colspan="4" id="cmd">NA</td>
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</tr>
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</tbody>
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</table>
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</div>
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<script src="../../../_static/js/nano_installation_guide.js"></script>
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```
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We also partially support M-series chip users with no guarantee of acceleration with same API. Currently only tensorflow is experimentally supported.
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```bash
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conda create -n env python=3.8
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conda activate env
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conda install -c apple tensorflow-deps
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pip install --pre --upgrade bigdl-nano[tensorflow]
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```
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```eval_rst
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.. note::
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Since bigdl-nano is still in the process of rapid iteration, we highly recommend that you install nightly build version through the above command to facilitate your use of the latest features.
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For stable version, please refer to the document and installation guide `here <https://bigdl.readthedocs.io/en/v2.1.0/doc/Nano/Overview/nano.html>`_ .
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```
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```bash
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conda create -n env
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conda activate env
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# select your preference in above panel to find the proper command to replace the below command, e.g.
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pip install --pre --upgrade bigdl-chronos[pytorch]
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# after installing bigdl-nano, you can run the following command to setup a few environment variables.
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source bigdl-nano-init
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```
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The `bigdl-nano-init` scripts will export a few environment variable according to your hardware to maximize performance.
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In a conda environment, when you run `source bigdl-nano-init` manually, this command will also be added to `$CONDA_PREFIX/etc/conda/activate.d/`, which will automaticly run when you activate your current environment.
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In a pure pip environment, you need to run `source bigdl-nano-init` every time you open a new shell to get optimal performance and run `source bigdl-nano-unset-env` if you want to unset these environment variables.
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---
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