# FastEstimator
[![Build Status](http://54.187.56.209:8080/buildStatus/icon?subject=PR-build&job=fe_git%2Ffastestimator%2Fmaster)](http://54.187.56.209:8080/job/fe_git/job/fastestimator/job/master/)
[![Build Status](http://54.187.56.209:8080/buildStatus/icon?subject=nightly-build&job=nightly)](http://54.187.56.209:8080/job/nightly/)
FastEstimator is a high-level deep learning API. With the help of FastEstimator, you can easily build a high-performance deep learning model and run it anywhere. :wink:
## Prerequisites:
* Python >= 3.5
* TensorFlow2
* GPU: `pip install tensorflow-gpu==2.0.0`
* CPU: `pip install tensorflow==2.0.0`
* Pytorch backend is coming soon!
## Installation
Please choose one:
* I have no idea what FastEstimator is about:
```
pip install fastestimator==1.0b2
```
* I want to keep up to date with the latest:
```
pip install fastestimator-nightly
```
* I'm here to play hardcore mode:
```
git clone https://github.com/fastestimator/fastestimator.git
pip install -e fastestimator
```
## Docker Hub
Docker container creates isolated virtual environment that shares resources with host machine. Docker provides an easy way to set up FastEstimator environment, users can pull image from [Docker Hub](https://hub.docker.com/r/fastestimator/fastestimator/tags).
* GPU: `docker pull fastestimator/fastestimator:1.0b2-gpu`
* CPU: `docker pull fastestimator/fastestimator:1.0b2-cpu`
## Start your first FastEstimator training
```
$ python ./apphub/image_classification/lenet_mnist/lenet_mnist.py
```
## Tutorial
We have [tutorial series](https://github.com/fastestimator/fastestimator/tree/master/tutorial) that walk through everything you need to know about FastEstimator.
## Example
Check out [Application Hub](https://github.com/fastestimator/fastestimator/tree/master/apphub) for end-to-end deep learning examples in FastEstimator.
## Documentation
For more info, check out our [FastEstimator documentation](https://www.fastestimator.org).
## Citation
Please cite FastEstimator in your publications if it helps your research:
```
@misc{dong2019fastestimator,
title={FastEstimator: A Deep Learning Library for Fast Prototyping and Productization},
author={Xiaomeng Dong and Junpyo Hong and Hsi-Ming Chang and Michael Potter and Aritra Chowdhury and
Purujit Bahl and Vivek Soni and Yun-Chan Tsai and Rajesh Tamada and Gaurav Kumar and Caroline Favart and
V. Ratna Saripalli and Gopal Avinash},
year={2019},
eprint={1910.04875},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
[Apache License 2.0](https://github.com/fastestimator/fastestimator/blob/master/LICENSE)
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PyPI 官网下载 | fastestimator-nightly-1.0b2.dev202001211040.tar.gz
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PyPI 官网下载 | fastestimator-nightly-1.0b2.dev202001211040.tar.gz (154个子文件)
setup.cfg 38B
fastestimator 1KB
README.md 3KB
PKG-INFO 619B
PKG-INFO 619B
record_writer.py 22KB
pipeline.py 21KB
visualize.py 20KB
retinanet.py 19KB
cli_util.py 19KB
estimator.py 17KB
augmentation_2d.py 17KB
util_test.py 16KB
caricature.py 12KB
unet_3d.py 12KB
tensorboard.py 12KB
brats.py 12KB
util.py 11KB
trace.py 10KB
mean_avg_precision.py 10KB
unet.py 9KB
log_plot.py 9KB
mscoco.py 9KB
gradcam.py 8KB
network.py 8KB
augmentation_3d.py 7KB
umaps.py 7KB
tfrecord.py 7KB
omniglot.py 7KB
saliency.py 7KB
svhn.py 7KB
pggan.py 6KB
fft_util.py 6KB
saliency_util.py 6KB
logger.py 6KB
lr_controller.py 6KB
log_parse.py 6KB
cyclegan.py 5KB
usps.py 5KB
precision.py 5KB
caricature.py 5KB
umap.py 5KB
recall.py 5KB
f1_score.py 5KB
unet3d_isensee.py 5KB
horse2zebra.py 5KB
stnet.py 4KB
wget_util.py 4KB
early_stopping.py 4KB
srgan.py 4KB
cub200.py 4KB
lr_scheduler.py 4KB
mendeley.py 4KB
xai.py 4KB
grad_cam.py 4KB
mnist.py 4KB
loader.py 4KB
vis_util.py 4KB
resize_image_bbox.py 4KB
saliency.py 4KB
terminate_on_nan.py 4KB
montgomery.py 4KB
umap_util.py 3KB
slackio.py 3KB
nih_chestxray.py 3KB
model_saver.py 3KB
color_util.py 3KB
adversarial_sample.py 3KB
cutmix_batch.py 3KB
filter.py 3KB
csv_logger.py 3KB
resize.py 3KB
confusion_matrix.py 3KB
dice.py 3KB
accuracy.py 3KB
epoch_scheduler.py 2KB
train.py 2KB
mixup_batch.py 2KB
flip_image_and_bbox.py 2KB
update.py 2KB
mixup_loss.py 2KB
op.py 2KB
setup.py 2KB
sub_pixel_conv_2d.py 2KB
weighted_dice_loss.py 2KB
gradients.py 2KB
constant.py 2KB
image_reader.py 2KB
loss.py 2KB
instance_norm.py 2KB
util.py 2KB
binary_crossentropy.py 2KB
pad.py 2KB
__init__.py 2KB
model.py 2KB
mean_squared_error.py 2KB
resize.py 2KB
equalized_lr_conv_2d.py 2KB
minibatch_std.py 2KB
onehot.py 2KB
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