Note: The purpose of the project to explore the libraries and learn how to use them. Not to build a SOTA model.
This project uses Python 3.8
Create a virtual env with the following command:
conda create --name project-setup python=3.8
conda activate project-setup
Install the requirements:
pip install -r requirements.txt
After installing the requirements, in order to train the model simply run:
python train.py
Once the training is completed in the end of the logs you will see something like:
wandb: Synced 5 W&B file(s), 4 media file(s), 3 artifact file(s) and 0 other file(s)
wandb:
wandb: Synced proud-mountain-77: https://wandb.ai/raviraja/MLOps%20Basics/runs/3vp1twdc
Follow the link to see the wandb dashboard which contains all the plots.
Refer to the blog: DVC Configuration
Once the model is trained, convert the model using the following command:
python convert_model_to_onnx.py
python inference.py
python inference_onnx.py
Install the docker using the instructions here
Build the image using the command
docker build -t inference:latest .Then run the container using the command
docker run -p 8000:8000 --name inference_container inference:latest(or)
Build and run the container using the command
docker-compose upI am using Jupyter lab to run the notebooks.
Since I am using a virtualenv, when I run the command jupyter lab it might or might not use the virtualenv.
To make sure to use the virutalenv, run the following commands before running jupyter lab
conda install ipykernel
python -m ipykernel install --user --name project-setup
pip install ipywidgets
https://docs.wandb.ai/quickstart
https://dvc.org/doc/command-reference/remote/add
https://docs.github.com/cn/actions/quickstart
- 函数计算
https://help.aliyun.com/product/50980.html
- 容器镜像服务 ACR