Mnist API server w/ FastAPI

Jinwoo Park (Curt)

Last update: Feb 8, 2022

Related tags

FastAPI Projects mnist-api-server

Overview

MNIST API Server

MNIST inference server w/ FastAPI

Prerequisites

$ make env      # create anaconda environment
$ make setup    # initial setup for the project

How to Play

$ make train        # train conv net (optional)
$ make backend      # start up the server
$ make frontend     # start up frontend (draw board)

For Developers

$ make format   # format python scripts
$ make lint     # lint python scripts
$ make utest    # run unit tests
$ make cov      # open coverage report (after `make utest`)

References

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95 Jan 2, 2023

FastAPI Boilerplate

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417 Jan 7, 2023

Comments

Add mnist training script & model trained

Usage

$ python train.py --help
usage: train.py [-h] [--batch-size N] [--test-batch-size N] [--epochs N] [--lr LR] [--gamma M] [--no-cuda] [--seed S] [--log-interval N] [--save-model]

PyTorch MNIST Example

optional arguments:
  -h, --help           show this help message and exit
  --batch-size N       input batch size for training (default: 64)
  --test-batch-size N  input batch size for testing (default: 1000)
  --epochs N           number of epochs to train (default: 14)
  --lr LR              learning rate (default: 1.0)
  --gamma M            Learning rate step gamma (default: 0.7)
  --no-cuda            disables CUDA training
  --seed S             random seed (default: 1)
  --log-interval N     how many batches to wait before logging training status
  --save-model         For Saving the current Model

The saved model can be used by:

model = torch.jit.load('model_scripted.pt')
model.eval()

opened by Curt-Park 0

Owner

Jinwoo Park (Curt)

A domain-independent problem-solver

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