Quickstart

There are several ways to use PyTorch-BSF.

Run as an MLflow project

If you have data and labels for training a Bezier simplex in common file formats such as CSV, JSON, etc., then the easiest way is to invoke PyTorch-BSF via MLflow. In this way, some CUI commands for training and prediction are provided without installing PyTorch-BSF. On each training and prediction, separation of runtime environment and installation of PyTorch-BSF are automatically handled by MLflow!

Installation

First, install Miniconda. Then, install mlflow conda package from conda-forge channel:

conda install -c conda-forge mlflow

Training

Let’s prepare data and labels for training:

cat << EOS > params.csv
1.00, 0.00
0.75, 0.25
0.50, 0.50
0.25, 0.75
0.00, 1.00
EOS
cat <<EOS > values.csv
0.00, 1.00
3.00, 2.00
4.00, 5.00
7.00, 6.00
8.00, 9.00
EOS

Warning

The data file and label file must have the same number of lines.

Now, you can fit a Bezier simplex to those data and labels with the latest version of PyTorch-BSF:

mlflow run https://github.com/rafcc/pytorch-bsf \
-P params=params.csv \
-P values=values.csv \
-P degree=3

After the command finished, you will get a trained model in mlruns directory.

Prediction

mlflow models predict \
  --model-uri file://`pwd`/mlruns/0/${run_uuid}/artifacts/model \
  --content-type csv \
  --input-path params.csv \
  --output-path test_values.csv

You have results in test_values.csv:

cat test_values.csv
[[0.00, 1.00], ...]

Serve prediction API

You can also serve a Web API for prediction:

mlflow models serve \
  --model-uri {Full Path} \
  --host localhost \
  --port 5001

Request a prediction with HTTP POST method:

curl http://localhost:5001/invocations -H 'Content-Type: application/json' -d '{
  "columns": ["t1", "t2"],
  "data": [
     [0.2, 0.8],
     [0.7, 0.3]
  ]
}'

See for details https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models

Run as a Python package

Assume you have installed Python 3.8 or above. Then, install the package:

pip install pytorch-bsf

Then, run torch_bsf as a module:

python -m torch_bsf \
  --model-uri file://`pwd`/mlruns/0/${run_uuid}/artifacts/model \
  --content-type csv \
  --input-path test_params.csv \
  --output-path test_values.csv

Run as Python code

Assume you have installed Python 3.8 or above. Then, install the package:

pip install pytorch-bsf

Train a model by fit(), and call the model to predict.

import torch
import torch_bsf

# Prepare training data
ts = torch.tensor(  # parameters on a simplex
   [
      [8/8, 0/8],
      [7/8, 1/8],
      [6/8, 2/8],
      [5/8, 3/8],
      [4/8, 4/8],
      [3/8, 5/8],
      [2/8, 6/8],
      [1/8, 7/8],
      [0/8, 8/8],
   ]
)
xs = 1 - ts * ts  # values corresponding to the parameters

# Train a model
bs = torch_bsf.fit(params=ts, values=xs, degree=3)

# Predict by the trained model
t = [
   [0.2, 0.8],
   [0.7, 0.3],
]
x = bs(t)
print(f"{t} -> {x}")