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Unet-3D Model Training for Intel® Extention for TensorFlow

Best known method of Unet-3D training for Intel® Extention for TensorFlow.

Model Information

Use Case Framework Model Repo Branch/Commit/Tag Optional Patch
Training TensorFlow DeepLearningExamples/UNet_3D master 3dunet_itex.patch
3dunet_itex_with_horovod.patch

Note: Refer to CONTAINER.md for UNet-3D training instructions using docker containers.

Pre-Requisite

  • Host has Intel® Data Center GPU Max Series

  • Host has installed latest Intel® Data Center GPU Max Series Drivers https://dgpu-docs.intel.com/driver/installation.html

  • The following Intel® oneAPI Base Toolkit components are required:

    • Intel® oneAPI DPC++ Compiler (Placeholder DPCPPROOT as its installation path)
    • Intel® oneAPI Math Kernel Library (oneMKL) (Placeholder MKLROOT as its installation path)
    • Intel® oneAPI MPI Library
    • Intel® oneAPI TBB Library
    • Intel® oneAPI CCL Library

    Follow instructions at Intel® oneAPI Base Toolkit Download page to setup the package manager repository.

Dataset

The 3D-UNet model was trained in the Brain Tumor Segmentation 2019 dataset. Test images provided by the organization were used to produce the resulting masks for submission. Upon registration, the challenge's data is made available through the https//ipp.cbica.upenn.edu service.

Run Model

  1. git clone https://github.com/IntelAI/models.git

  2. cd models/models_v2/tensorflow/3d_unet/training/gpu

  3. Create virtual environment venv and activate it:

    python3 -m venv venv
. ./venv/bin/activate

  4. Run setup.sh

    ./setup.sh

  5. Install tensorflow and ITEX

  6. If you run multi-tile (export MULTI_TILE=True), please install "intel-optimization-for-horovod" with pip install intel-optimization-for-horovod

  7. Set environment variables for Intel® oneAPI Base Toolkit: Default installation location {ONEAPI_ROOT} is /opt/intel/oneapi for root account, ${HOME}/intel/oneapi for other accounts

    source {ONEAPI_ROOT}/compiler/latest/env/vars.sh
source {ONEAPI_ROOT}/mkl/latest/env/vars.sh
source {ONEAPI_ROOT}/tbb/latest/env/vars.sh
source {ONEAPI_ROOT}/mpi/latest/env/vars.sh
source {ONEAPI_ROOT}/ccl/latest/env/vars.sh

  8. Setup required environment paramaters

    Parameter export command
    DATASET_DIR export DATASET_DIR=/the/path/to/dataset
    OUTPUT_DIR export OUTPUT_DIR=/the/path/to/output_dir
    MULTI_TILE export MULTI_TILE=False (False or True)
    BATCH_SIZE (optional) export BATCH_SIZE=1
    PRECISION export PRECISION=bfloat16 (bfloat16 or fp32)

  9. Run run_model.sh

Output

Output will typically looks like:

Current step: 997, time in ms: 73.95
Current step: 998, time in ms: 74.81
Current step: 999, time in ms: 74.31
Current step: 1000, time in ms: 74.44
self._step: 1000
Total time spent (after warmup): 79390.14 ms
Time spent per iteration (after warmup): 79.55 ms
Latency is 79.549 ms
Throughput is xxx samples/sec

Final results of the training run can be found in results.yaml file.

results:
 - key: throughput
   value: xxx
   unit: images/sec