README.md

Inference

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Setup for Linux

    $ conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
    $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
    $ pip install tensorflow==2.11.0

To automate pytorch and tensorflow sees cuda libraries installed by conda, follow these steps,

    $ mkdir -p $CONDA_PREFIX/etc/conda/activate.d
    $ echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

To verify both library installations ..

    $ import torch
    $ torch.cuda.is_available()

    $ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Setup for Apple Silicon

    # Create a conda environment
    $ conda create -n inference python 3.10
    $ conda activate inference

    # For TensorFlow
    $ conda install -c apple tensorflow-deps
    $ pip install tensorflow-macos
    $ pip install tensorflow-metal

    # For PyTorch
    $ conda install pytorch torchvision torchaudio -c pytorch-nightly

TensorFlow reference --> link
PyTorch reference --> link

Set TensorFlow GPU Consumption

    $ import tensorflow as tf
    $ gpus = tf.config.experimental.list_physical_devices('GPU')
    $ for gpu in gpus:
    $   tf.config.experimental.set_memory_growth(gpu, True)

In my case, without this code block, TensorFlow was using almost all the available resources in the device memory. After this code, it decreases the total memory consumption by 1 GB. The next step to limit the memory consumption would be write a data loader to load the data optimized to the device.

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TO-DO List

1. Learn more about grad-cam visualization and it's implementations.
   • https://github.com/jacobgil/pytorch-grad-cam
   • https://pyimagesearch.com/2020/03/09/grad-cam-visualize-class-activation-maps-with-keras-tensorflow-and-deep-learning/
2. Learn more about feature maps and filters visualizations -->
   • https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/
3. Learn more about model pruning
   • https://pytorch.org/tutorials/intermediate/pruning_tutorial.html
4. Learn more about model post-training quantization
   • https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html
   • https://pytorch.org/TensorRT/tutorials/ptq.html
   • https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/index.html
5. Learn more about NVIDIA TensorRT tool for model quantization
   • https://docs.nvidia.com/deeplearning/tensorrt/sample-support-guide/index.html#onnx_mnist_sample
   • https://developer.nvidia.com/tensorrt
   • https://github.com/NVIDIA/TensorRT