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AIMET Installation in Google Colab

This page provides instructions to install AIMET package in Google colab environment. Please follow the instructions in the order provided, unless specified otherwise.

NOTE: These instructions are out of date and may NOT work with the latest releases.

Google colab set up

  • Please go to Google Colab website: https://colab.research.google.com/
  • Open a new notebook from main menu option: File -> New notebook
  • Select Hardware Accelerator as GPU in below Google Colab Menu option: Runtime -> Change runtime -> Hardware Accelerator(GPU)

Install Dependency packages

import os
os.environ['SRC_URL'] = 'https://raw.githubusercontent.com/quic/aimet/develop/packaging/'
!curl ${SRC_URL}packages_common.txt | xargs apt-get --assume-yes install
!curl ${SRC_URL}packages_gpu.txt | xargs apt-get --assume-yes --allow-change-held-packages install 
!wget ${SRC_URL}requirements.txt
!pip3 install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

Install AIMET packages

Go to https://github.com/quic/aimet/releases and identify the release tag of the packages you want to install. Replace <RELEASE_TAG> in the steps below with the appropriate tag (ex. "1.14.0"). Then run the below commands to install the AIMET packages:

import os
os.environ['release_tag']=<RELEASE_TAG>
!pip3 install https://github.com/quic/aimet/releases/download/${release_tag}/AimetCommon-${release_tag}-py3-none-any.whl 
!pip3 install https://github.com/quic/aimet/releases/download/${release_tag}/AimetTorch-${release_tag}-py3-none-any.whl
!pip3 install https://github.com/quic/aimet/releases/download/${release_tag}/AimetTensorflow-${release_tag}-py3-none-any.whl

Please restart Google runtime environment when prompted or from below menu option:

Runtime -> Restart runtime

Configure

import sys
sys.path.append('/usr/local/lib/python3.8/dist-packages/aimet_common')

import os
os.environ['LD_LIBRARY_PATH'] +=':/usr/local/lib/python3.8/dist-packages/aimet_common'

Usage

You should be able to import the required packages from aimet_common, aimet_torch and aimet_tensorflow to incorporate aimet packages, for additional usage suggestion please refer to the examples from the documentation.

Validation

The install could be validated by executing a snippet of code that instantiates a AIMET quantization simulator

import torch
from torchvision import models
from aimet_torch.quantsim import QuantizationSimModel
m = models.resnet18()
sim = QuantizationSimModel(m, (1, 3, 224, 224))

Sample output

print(sim)
-------------------------
Quantized Model Report
-------------------------
Layer: conv1
    Input: bw=8, encoding-present=False
    Params:
        weight: bw=8, encoding-present=False
    Output: bw=8, encoding-present=False
Layer: bn1
    Input: Unquantized
    Params:
        weight: bw=8, encoding-present=False
        bias: Unquantized
    Output: bw=8, encoding-present=False
Layer: relu
    Input: Unquantized
    Params:
    Output: bw=8, encoding-present=False
Layer: maxpool
    Input: Unquantized
    Params:
    Output: bw=8, encoding-present=False
Layer: layer1.0.conv1
    Input: Unquantized
    Params:
        weight: bw=8, encoding-present=False
    Output: bw=8, encoding-present=False
Layer: layer1.0.bn1
...