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valohai.yaml
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valohai.yaml
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---
- step:
name: tensorflow-check
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- pwd
- lsb_release -a
- python --version
- python -c "import keras; print(keras.__version__);"
- python -c "import tensorflow; print(tensorflow.__version__);"
- python -c "from tensorflow.python.client import device_lib; device_lib.list_local_devices();"
- nvidia-smi
- nvcc --version | grep release
- cat /usr/include/x86_64-linux-gnu/cudnn_v*.h | grep CUDNN_MAJOR -A 2
- step:
name: keras-dvc-cnn-simple
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- tar -xf /valohai/inputs/dataset/*.tar -C /valohai/inputs/dataset/
- python valohai/keras-dvc-cnn-simple.py {parameters}
inputs:
- name: dataset
parameters:
- name: epochs
pass-as: --epochs={v}
type: integer
default: 20
- name: inference
pass-as: --inference={v}
type: integer
default: 0
- step:
name: keras-20ng-cnn
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- tar xf /valohai/inputs/dataset/glove6b100dtxt.tar.gz -C /valohai/inputs/dataset/
- tar xf /valohai/inputs/dataset/news20.tar.gz -C /valohai/inputs/dataset/
- python valohai/keras-20ng-cnn.py {parameters}
inputs:
- name: dataset
parameters:
- name: epochs
pass-as: --epochs={v}
type: integer
default: 20
- name: inference
pass-as: --inference={v}
type: integer
default: 1
- step:
name: keras-20ng-rnn
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- tar xf /valohai/inputs/dataset/glove6b100dtxt.tar.gz -C /valohai/inputs/dataset/
- tar xf /valohai/inputs/dataset/news20.tar.gz -C /valohai/inputs/dataset/
- python valohai/keras-20ng-rnn.py {parameters}
inputs:
- name: dataset
parameters:
- name: epochs
pass-as: --epochs={v}
type: integer
default: 20
- name: inference
pass-as: --inference={v}
type: integer
default: 1
- step:
name: keras-dvc-cnn-pretrained
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- tar xf /valohai/inputs/dataset/dogs-vs-cats.tar -C /valohai/inputs/dataset/
- python valohai/keras-dvc-cnn-pretrained.py {parameters}
inputs:
- name: dataset
default: https://object.pouta.csc.fi/swift/v1/AUTH_dac/mldata/dogs-vs-cats.tar
parameters:
- name: epochs
pass-as: --epochs={v}
type: integer
default: 10
- step:
name: keras-sfnet-cnn
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- tar xf /valohai/inputs/dataset/*.tar.gz -C /valohai/inputs/dataset/
- pip install tqdm
- python valohai/keras-sfnet-cnn.py {parameters}
inputs:
- name: dataset
default: https://object.pouta.csc.fi/swift/v1/AUTH_dac/mldata/sfnet2007-2008.tar.gz
- name: embedding
default: https://object.pouta.csc.fi/swift/v1/AUTH_dac/mldata/cc.fi.300.vec.gz
- step:
name: keras-sfnet-lstm
image: valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
command:
- tar xf /valohai/inputs/dataset/*.tar.gz -C /valohai/inputs/dataset/
- pip install tqdm
- python valohai/keras-sfnet-lstm.py {parameters}
inputs:
- name: dataset
default: https://object.pouta.csc.fi/swift/v1/AUTH_dac/mldata/sfnet2007-2008.tar.gz
- name: embedding
default: https://object.pouta.csc.fi/swift/v1/AUTH_dac/mldata/cc.fi.300.vec.gz
- step:
name: pytorch_dvc_cnn_simple
image: nvcr.io/nvidia/pytorch:19.01-py3
command:
- nvidia-smi
- nvcc --version | grep release
- cat /usr/include/x86_64-linux-gnu/cudnn_v*.h | grep CUDNN_MAJOR -A 2
- pip install torchvision
- tar xf /valohai/inputs/dataset/dogs-vs-cats.tar -C /valohai/inputs/dataset/
- python valohai/pytorch_dvc_cnn_simple.py {parameters}
inputs:
- name: dataset
default: https://object.pouta.csc.fi/swift/v1/AUTH_dac/mldata/dogs-vs-cats.tar
parameters:
- name: test
pass-as: --test
optional: True
- endpoint:
name: predict-animal
image: ufoym/deepo:all-py36-cpu
wsgi: valohai.prediction_server:predict_wsgi
files:
- name: model
path: model.h5
- endpoint:
name: predict-nyyssi
image: ufoym/deepo:all-py36-cpu
wsgi: valohai.prediction_server_text:predict_wsgi
files:
- name: model
path: model.h5
- name: tokenizer
path: tokenizer_sfnet.pkl
- name: valohai-metadata.json
path: valohai-metadata.json