Running Your Code

Oftentimes you don't start a project from scratch. Instead of that you use someone's or your own old code as a baseline and develop your solution on top of it. This guide demonstrates how to take an existing code base, convert it into a Neu.ro project, and start developing on the platform.

Prerequisites

  1. Make sure that you have the Neuro CLI installed and logged in.

  2. Install the neuro-flow package:

pip install -U neuro-flow

Configuration

As an example we'll use the GitHub repo that contains PyTorch implementations for Aspect-Based Sentiment Analysis models (see Attentional Encoder Network for Targeted Sentiment Classification for more details).

First, let's clone the repo and navigate to the created folder:

git clone git@github.com:songyouwei/ABSA-PyTorch.git
cd ABSA-PyTorch

Now, we need to add a couple of files:

  • Dockerfile contains a very basic Docker image configuration. We need this file to build a custom Docker image which is based on pytorch/pytorch public images and contains this repo requirements (which are gracefully listed by the repo maintainer in requirements.txt):

Dockerfile
FROM pytorch/pytorch
COPY . /cfg
RUN pip install --progress-bar=off -U --no-cache-dir -r /cfg/requirements.txt
  • .neuro/live.yml contains minimal configuration allowing us to run this repo's scripts right on the platform through handy short commands:

.neuro/live.yml
kind: live
title: Sentiment Analysis Training
id: absa
volumes:
project:
remote: storage:${{ flow.id }}
mount: /project
local: .
images:
pytorch:
ref: image:${{ flow.id }}:v1.0
dockerfile: ${{ flow.workspace }}/Dockerfile
context: ${{ flow.workspace }}
jobs:
train:
image: ${{ images.pytorch.ref }}
preset: gpu-small
volumes:
- ${{ volumes.project.ref_rw }}
bash: |
cd ${{ volumes.project.mount }}
python train.py --model_name bert_spc --dataset restaurant

Here is a brief explanation of this config:

  • volumes section contains declarations of connections between your computer file system and the platform storage; here we state that we want the entire project folder to be uploaded to storage at storage:absa folder and be mounted inside jobs /project;

  • images section contains declarations of Docker images created in this project; here we declare our image which is decribed in Dockerfile above;

  • jobs section is the one where action happens; here we declare a train job which runs our training script with a couple of parameters.

Running code

Now it's time to run several commands that set up the project environment and run training.

  • First, create volumes and upload project to platform storage:

neuro-flow mkvolumes
neuro-flow upload ALL
  • Then, build an image:

neuro-flow build pytorch
  • Finally, run training:

neuro-flow run train

Please run neuro-flow --help to get more information about available commands.