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 Apolo flow, and start developing on the platform.

Prerequisites

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

2. Install the apolo-flow package:

$ pip install -U apolo-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 [e-mail]:songyouwei/ABSA-PyTorch.git
$ cd ABSA-PyTorch

Now, we need to create two more files in this folder:

• 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:1.4-cuda10.1-cudnn7-runtime
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
    name: absa-pytorch-train
    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:

$ apolo-flow mkvolumes
$ apolo-flow upload ALL

• Then, build an image:

$ apolo-flow build pytorch

• Finally, run training:

$ apolo-flow run train

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