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AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com | ||
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ECR_REPOSITORY_NAME = simple-app | ||
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# How to run and train model in google colab for shorter run-time? | ||
### STEPS: | ||
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Clone the repository | ||
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```bash | ||
https://github.com/entbappy/End-to-end-Text-Summarization | ||
``` | ||
### STEP 01- Create a conda environment after opening the repository | ||
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```bash | ||
! git clone https://github.com/krishnaik06/Text-Summarization-NLP-Project.git | ||
``` | ||
### STEP 02- install the requirements | ||
```bash | ||
import os | ||
os.chdir('/content/Text-Summarization-NLP-Project/') | ||
!pip freeze > requirements.txt | ||
!pip install . | ||
!pip install fastapi nest-asyncio pyngrok uvicorn | ||
``` | ||
P.S. Here its a bit tedious, since I couldn't figure it out yet, it is required for all libraries in requirements.txt to be installed separately for each runtime. Hopefully I will try and fix it soon | ||
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# Finally run the following command | ||
```bash | ||
! python3 app.py | ||
``` | ||
Now, | ||
```bash | ||
open up your displayed public url | ||
``` |