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https://github.com/jrieke/traintool
🔧 Train off-the-shelf machine learning models in one line of code
https://github.com/jrieke/traintool
deep-learning machine-learning neural-network python pytorch sklearn tensorflow
Last synced: 25 days ago
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🔧 Train off-the-shelf machine learning models in one line of code
- Host: GitHub
- URL: https://github.com/jrieke/traintool
- Owner: jrieke
- License: apache-2.0
- Created: 2020-09-30T22:23:05.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-12T01:44:04.000Z (over 3 years ago)
- Last Synced: 2024-10-01T15:44:23.785Z (about 1 month ago)
- Topics: deep-learning, machine-learning, neural-network, python, pytorch, sklearn, tensorflow
- Language: Python
- Homepage: https://traintool.jrieke.com
- Size: 592 KB
- Stars: 12
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Train off-the-shelf machine learning models in one line of code
Try it out in Google Colab • Documentation
---
traintool is the easiest Python library for **applied machine learning**. It allows you
to train off-the-shelf models with minimum code: Just give your data
and the model name, and traintool takes care of the rest. It combines **pre-implemented
models** (built on top of sklearn & pytorch) with powerful **utilities** that get you
started in seconds (automatic visualizations, experiment tracking, intelligent data
preprocessing, API deployment).
Alpha Release: traintool is in an early alpha release. The API can and will change
without notice. If you find a bug, please file an issue on
[Github](https://github.com/jrieke/traintool) or
[write me](mailto:[email protected]).
## Installation
```bash
pip install traintool
```
## Features
- **Minimum coding —** traintool is designed to require as few lines of code as
possible. It offers a sleek and intuitive interface that gets you started in seconds.
Training a model just takes a single line:
```python
traintool.train("resnet18", train_data, test_data, config={"optimizer": "adam", "lr": 0.1})
```
- **Pre-implemented models —** The heart of traintool are fully implemented and tested
models – from simple classifiers to deep neural networks; built on sklearn, pytorch,
or tensorflow. Here are only a few of the models you can use:
```python
"svc", "random-forest", "alexnet", "resnet50", "inception_v3", ...
```
- **Automatic visualizations & experiment tracking —** traintool automatically
calculates metrics, creates beautiful visualizations (in
[tensorboard](https://www.tensorflow.org/tensorboard) or
[comet.ml](https://www.comet.ml/)), and stores experiment data and
model checkpoints – without needing a single additional line of code.
- **Ready for your data —** traintool understands numpy arrays, pytorch datasets,
and files. It automatically converts and preprocesses everything based on the model you
use.
- **Instant deployment —** In one line of code, you can deploy your model to a REST
API that you can query from anywhere. Just call:
```python
model.deploy()
```
## Example: Image classification on MNIST
Run this example interactively in Google Colab:
[](https://colab.research.google.com/github/jrieke/traintool/blob/master/docs/tutorial/quickstart.ipynb)
```python
import mnist
import traintool
# Load MNIST data as numpy
train_data = [mnist.train_images(), mnist.train_labels()]
test_data = [mnist.test_images(), mnist.test_labels()]
# Train SVM classifier
svc = traintool.train("svc", train_data=train_data, test_data=test_data)
# Train ResNet with custom hyperparameters
resnet = traintool.train("resnet", train_data=train_data, test_data=test_data,
config={"lr": 0.1, "optimizer": "adam"})
# Make prediction
result = resnet.predict(test_data[0][0])
print(result["predicted_class"])
# Deploy to REST API
resnet.deploy()
# Get underlying pytorch model (e.g. for custom analysis)
pytorch_model = resnet.raw()["model"]
```
For more information, check out the
[complete tutorial](https://traintool.jrieke.com/tutorial/quickstart/).
## Get in touch!
You have a question on traintool, want to use it in production, or miss a feature? I'm
happy to hear from you! Write me at [[email protected]](mailto:[email protected]).