Skip to content

Latest commit

 

History

History

pytorch

README.md

CNNs (PyTorch)

Deep CNN residual learning models for image recognition and classification, optimised for Graphcore's IPU.

Framework Domain Model Datasets Tasks Training Inference
PyTorch Vision CNNs ImageNet LSVRC 2012 Image recognition, Image classification

✅
Min. 16 IPUs (POD16) required

✅
Min. 4 IPUs (POD4) required

Instructions summary

  1. Install and enable the Poplar SDK (see Poplar SDK setup)

  2. Install the system and Python requirements (see Environment setup)

  3. Download the ImageNet LSVRC 2012 dataset (See Dataset setup)

Poplar SDK setup

To check if your Poplar SDK has already been enabled, run:

 echo $POPLAR_SDK_ENABLED

If no path is provided, then follow these steps:

  1. Navigate to your Poplar SDK root directory

  2. Enable the Poplar SDK with:

cd poplar-<OS version>-<SDK version>-<hash>
. enable.sh
  1. Additionally, enable PopART with:
cd popart-<OS version>-<SDK version>-<hash>
. enable.sh

More detailed instructions on setting up your Poplar environment are available in the Poplar quick start guide.

Environment setup

NOTE: You must install PopTorch to your environment (as in 2. below) before installing requirements (as in 5. below), else you may encounter compatibility issues with Horovod

To prepare your environment, follow these steps:

  1. Create and activate a fresh Python3 virtual environment:
python3 -m venv <venv name> --clear
source ./<venv name>/bin/activate

  1. Navigate to the Poplar SDK root directory

  2. Install the PopTorch (PyTorch) wheel:

cd <poplar sdk root dir>
pip3 install poptorch...x86_64.whl

  1. Navigate to this example's root directory

  2. Install the Python requirements and pillow-SIMD:

make install
  1. Install turbojpeg:
make install-turbojpeg

More detailed instructions on setting up your PyTorch environment are available in the PyTorch quick start guide.

Dataset setup

ImageNet LSVRC 2012

Download the ImageNet LSVRC 2012 dataset from the source or via kaggle

Disk space required: 144GB

.
├── bounding_boxes
├── imagenet_2012_bounding_boxes.csv
├── train
└── validation

3 directories, 1 file

Running and benchmarking

To run a tested and optimised configuration and to reproduce the performance shown on our performance results page, use the examples_utils module (installed automatically as part of the environment setup) to run one or more benchmarks. The benchmarks are provided in the benchmarks.yml file in this example's root directory.

For example:

python3 -m examples_utils benchmark --spec <path to benchmarks.yml file>

Or to run a specific benchmark in the benchmarks.yml file provided:

python3 -m examples_utils benchmark --spec <path to benchmarks.yml file> --benchmark <name of benchmark>

For more information on using the examples-utils benchmarking module, please refer to the README.

Custom Inference

Inference from pre-trained weights

Pretrained models are used for inference. The weights are downloaded from the following places:

Other features

Troubleshooting

If Triton server tests fails with such an error:

[model_runtime:cpp] [error] Error in model_runtime/source/Executable.cpp:38:Failed to deserialize XXX : Error reading executable - package hash (YYY) differs from poplar hash (ZZZ)

This mean that models were generated and saved with different version of SDK and needs to be recreated. Please remove tests/tritonserver/test_environment_ready.lock and rerun tests.

Failed: Failed to download and/or compile Triton Server!

Most probably some system packages are missing, ensure that all packages listed in required_apt_packages.txt are installed. Also refer to Triton Server build log file. After fixing error remove ../../../utils/triton_server/triton_environment_is_prepared.lock and rerun tests.

License

ModelZoo.pytorch is licensed under MIT License: https://github.com/PistonY/ModelZoo.pytorch/blob/master/LICENSE

PyTorch Image Models is licensed under the Apache License 2.0: https://github.com/rwightman/pytorch-image-models/blob/v0.4.12/LICENSE

Pillow SIMD is licensed under the Python Imaging License: https://github.com/uploadcare/pillow-simd/blob/v7.0.0.post3/LICENSE

Libjpeg-turbo is licensed under BSD-style open-source licenses: https://github.com/libjpeg-turbo/libjpeg-turbo/blob/2.1.0/LICENSE.md

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 HOLDERS 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.