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program:sequence2sequence-lstm-tf (v3.0.0)
Copyright: See copyright in the source repository
License: See license in the source repository
Creation date: 2018-06-18
Source: GitHub
cID: b0ac08fe1d3c2615:84617aa6b95406a9

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This portable workflow is our attempt to provide a common CLI with Python JSON API and a JSON meta description to automatically detect or install required components (models, data sets, libraries, frameworks, tools), and then build, run, validate, benchmark and auto-tune the associated method (program) across diverse models, datasets, compilers, platforms and environments. Our on-going project is to make the onboarding process as simple as possible via this platform. Please check this CK white paper and don't hesitate to contact us if you have suggestions or feedback!
  • Automation framework: CK
  • Development repository: ck-ml
  • Source: GitHub
  • Available command lines:
    • ck run program:sequence2sequence-lstm-tf --cmd_key=train (META)
    • ck run program:sequence2sequence-lstm-tf --cmd_key=train-and-profile (META)
    • ck run program:sequence2sequence-lstm-tf --cmd_key=train-and-profile-fp32 (META)
  • Support for host OS: any
  • Support for target OS: any
  • Tags: machine-translation,tensorflow-machine-translation
  • Template: LSTM via TensorFlow
  • How to get the stable version via the client:
    pip install cbench
    cb download program:sequence2sequence-lstm-tf --version=3.0.0 --all
    ck run program:sequence2sequence-lstm-tf
  • How to get the development version:
    pip install ck
    ck pull repo:ck-ml
    ck run program:sequence2sequence-lstm-tf

  • CLI and Python API: module:program
  • Dependencies    



    Python 2


    # pip2 install enum34 mock pillow
    # pip2 install wheel absl-py


    # apt install liblapack-dev libatlas-dev
    # pip2 install scipy

    Install via CK


    $ ck install package:lib-tensorflow-1.4.0-src-{cpu,cuda}{,-xla}

    TensorFlow models

    $ ck install package:tensorflowmodel-alexnet-py
    $ ck install package:tensorflowmodel-squeezenet-py
    $ ck install package:tensorflowmodel-googlenet-py
    $ ck install package:tensorflowmodel-mobilenet-v1-1.0-224-py
    $ ck show env --tags=tensorflowmodel
    Env UID:         Target OS: Bits: Name:                                                   Version: Tags:
    ef7343498dbec627   linux-64    64 TensorFlow python model and weights (squeezenet)        ImageNet 64bits,host-os-linux-64,python,squeezenet,target-os-linux-64,tensorflow-model,tensorflowmodel,v0,weights
    dede2b537d476299   linux-64    64 TensorFlow python model and weights (mobilenet-1.0-224) ImageNet 64bits,host-os-linux-64,mobilenet,mobilenet-v1,mobilenet-v1-1.0-224,python,target-os-linux-64,tensorflow-model,tensorflowmodel,v0,weights
    73619b7df1e2488e   linux-64    64 TensorFlow python model and weights (googlenet)         ImageNet 64bits,googlenet,host-os-linux-64,python,target-os-linux-64,tensorflow-model,tensorflowmodel,v0,weights
    4dd098ad717db21d   linux-64    64 TensorFlow python model and weights (alexnet)           ImageNet 64bits,alexnet,host-os-linux-64,python,target-os-linux-64,tensorflow-model,tensorflowmodel,v0,weights

    ImageNet dataset

    $ ck install package:imagenet-2012-val-min
    $ ck install package:imagenet-2012-aux


    $ ck list local:experiment:*
    $ cd `ck find program:classification-tensorflow`
    $ python benchmark.nvidia-gtx1080.py

    Program parameters


    Preprocessing parameter, size of intermediate image. If this parameter is set to a value greater than targer image size defined by a model, loaded images will be scaled to this size and then cropped to target size.

    For example, when running against MobileNet you may specify --env.CK_TMP_IMAGE_SIZE=256, then images will be resized to 256x256 the cropped to 224x244 as required to MobileNet.

    Default: 0


    Preprocessing parameter, percentage of central image region for cropping. If this parameter is set to a value between 0 and 100, loaded images will be cropped to this percent and then scaled to targer image size defined by a model.

    Not used if CK_TMP_IMAGE_SIZE is set and valid.

    Default: 87.5


    Preprocessing parameter, defines whether program should subtract mean value from loaded image. If CK_USE_MODEL_MEAN is not set then mean value is calculated over all images' pixels.

    Default: YES


    Preprocessing parameter, defines whether program should ask a model for mean value that will be subtracted. Model should provide get_mean_value method for this.

    Used when CK_SUBTRACT_MEAN is set.

    Default: YES


    Do caching of preprocessed images. Images are cached into a directory whose name contained of preprocessing parameters. Next time when program runs with the same preprocessing parameters, preprocessed images will be loaded from cache. This significantly speeds up images loading process.

    Default: YES


    Is set to YES then existed cached images will be erased.

    Default: NO


    Root director for storing cached images. This directory will include additional subdirectories for images preprocessed with different preprocessing parameters CK_TMP_IMAGE_SIZE and CK_CROP_PERCENT.

    Default: ../preprocessed




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