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---
- step:
name: build-cpu-gpu-uberjar
image: neomatrix369/dl4j-nlp-cuda:v0.5
inputs:
- name: src-main-resources
default: https://github.com/neomatrix369/dl4j-nlp-cuda-example/releases/download/dl4j-nlp-src-main-resources-v0.1/dl4j-nlp-src-main-resources.tgz
description: NLP data for training, prediction, evaluation
command:
- ./buildUberJar.sh
environment: aws-eu-west-1-g2-2xlarge
- step:
name: train-cpu-linux
image: neomatrix369/dl4j-nlp-cuda:v0.5
inputs:
- name: cpu-linux-uberjar
default: datum://
description: dl4j nlp cpu linux uberjar
- name: imdb-reviews
default: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
description: IMDB Review dataset for sentiment analysis
- name: google-word2vec
default: https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz
description: word2vec pre-trained Google News corpus
command:
- cd ${VH_REPOSITORY_DIR}
- export BACKEND=cpu
- export ACTION=train
- time ./runUberJar.sh --action ${ACTION} --output-model-dir .
environment: aws-eu-west-1-g3-4xlarge
- step:
name: train-gpu-linux
image: neomatrix369/dl4j-nlp-cuda:v0.5
inputs:
- name: gpu-linux-uberjar
default: datum://
description: dl4j nlp gpu linux uberjar
- name: imdb-reviews
default: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
description: IMDB Review dataset for sentiment analysis
- name: google-word2vec
default: https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz
description: word2vec pre-trained Google News corpus
command:
- cd ${VH_REPOSITORY_DIR}
- export BACKEND=gpu
- export ACTION=train
- time ./runUberJar.sh --action ${ACTION} --output-model-dir .
environment: aws-eu-west-1-g3-4xlarge
- step:
name: evaluate-model-linux
image: neomatrix369/dl4j-nlp-cuda:v0.5
inputs:
- name: linux-uberjar
default: datum://
description: dl4j nlp linux uberjar
- name: model
default: datum://
description: nlp model trained on Google news corpus
- name: imdb-reviews
default: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
description: IMDB Review dataset for sentiment analysis
- name: google-word2vec
default: https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz
description: word2vec pre-trained Google News corpus
command:
- cd ${VH_REPOSITORY_DIR}
- export ACTION=evaluate
- echo "~~~ Copying jar and model into ${VH_REPOSITORY_DIR}"
- cp ${VH_INPUTS_DIR}/linux-uberjar/*.jar ${VH_REPOSITORY_DIR}
- cp ${VH_INPUTS_DIR}/model/*.pb .
- time ./runUberJar.sh --action ${ACTION} --input-model-file $(ls *.pb)
environment: aws-eu-west-1-g3-4xlarge
###
### aws-eu-west-1-g2-2xlarge
### GPU Specification | 1x GPU - NVIDIA GRID K520 (4GB)
### Description | 8 cores, 15GB memory, 500GB storage
### Per-Hour USD$ | 0.90700
### aws-eu-west-1-g3-4xlarge
### GPU Specification | 1x GPU - NVIDIA Tesla M60 (8GB)
### Description | 16 cores, 122GB memory, 500GB storage
### Per-Hour USD$ | 1.21000
### aws-eu-west-1-g2-8xlarge
### GPU Specification | 4x GPU - NVIDIA GRID K520 (16GB)
### Description | 32 cores, 60GB memory, 500GB storage
### Per-Hour USD$ | 2.80800
###
- step:
name: know-your-gpus
image: neomatrix369/dl4j-nlp-cuda:v0.5
command:
- cd ${VH_REPOSITORY_DIR}
- ./know-your-gpus.sh &> "${VH_OUTPUTS_DIR}/know-your-gpus.logs"
- cat "${VH_OUTPUTS_DIR}/know-your-gpus.logs"
environment: aws-eu-west-1-g3-4xlarge
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