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 > train_data.tsv
1 2
4 3
5 6
EOS
cat << EOS > train_label.tsv
1
2
3
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 data=train_data.tsv \
-P label=train_label.tsv \
-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/${run_uuid}/artifacts/model \
--content-type csv \
--input-path test_data.csv \
--output-path test_label.csv
You have results in test_label.csv
:
cat test_label.csv
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
Serve prediction API
You can also serve a Web API for prediction:
mlflow models serve \
--model-uri {Full Path} \
--host localhost \
--port 5001
Request a prediction with HTTP POST method:
curl http://localhost:5001/invocations -H 'Content-Type: application/json' -d '{
"columns": ["t1", "t2", "t3"],
"data": [
[0.1, 0.2, 0.7],
[0.4, 0.5, 0.1]
]
}'
See for details https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models
Run as a Python package
Assume you have installed Python 3.8 or above. Then, install the package:
pip install pytorch-bsf
Then, run torch_bsf as a module:
python -m torch_bsf \
--model-uri file://`pwd`/mlruns/0/${run_uuid}/artifacts/model \
--content-type csv \
--input-path test_data.csv \
--output-path test_label.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
[
[3/3, 0/3, 0/3],
[2/3, 1/3, 0/3],
[2/3, 0/3, 1/3],
[1/3, 2/3, 0/3],
[1/3, 1/3, 1/3],
[1/3, 0/3, 2/3],
[0/3, 3/3, 0/3],
[0/3, 2/3, 1/3],
[0/3, 1/3, 2/3],
[0/3, 0/3, 3/3],
]
)
xs = 1 - ts * ts # values corresponding to the parameters
# Train a model
bs = torch_bsf.fit(params=ts, values=xs, degree=3, max_epochs=100)
# Predict by the trained model
t = [[0.2, 0.3, 0.5]]
x = bs(t)
print(f"{t} -> {x}")