examples/references/classification/imagenet/README.md · Pytorch-Mirror/ignite

# Reproducible ImageNet training with Ignite

In this example, we provide script and tools to perform reproducible experiments on training neural networks on ImageNet
dataset.

Features:
- Distributed training with mixed precision by [nvidia/apex](https://github.com/NVIDIA/apex/)
- Experiments tracking with [MLflow](https://mlflow.org/) or [Polyaxon](https://polyaxon.com/)

![tb_dashboard](assets/tb_dashboard.png)

There are two possible options: 1) Experiments tracking with MLflow or 2) Experiments tracking with Polyaxon. 
Experiments tracking with MLflow is more suitable for a local machine with GPUs. For experiments tracking with Polyaxon
user needs to have Polyaxon installed on a machine/cluster/cloud and can schedule experiments with `polyaxon-cli`.
User can choose one option and skip the descriptions of another option.

- Notes for [experiments tracking with MLflow](NOTES_MLflow.md)
- Notes for [experiments tracking with Polyaxon](NOTES_Polyaxon.md)

## Implementation details

Files tree description:
```
code
  |___ dataflow : module privides data loaders and various transformers
  |___ scripts : executable training scripts
  |___ utils : other helper modules

configs
  |___ train : training python configuration files  
  
experiments 
  |___ mlflow : MLflow related files
  |___ plx : Polyaxon related files
 
notebooks : jupyter notebooks to check specific parts from code modules 
```

## Code and configs

### [py_config_runner](https://github.com/vfdev-5/py_config_runner)

We use [py_config_runner](https://github.com/vfdev-5/py_config_runner) package to execute python scripts with python configuration files.

### Training scripts

Training scripts are located [code/scripts](code/scripts/) and contains

- `mlflow_training.py`, training script with MLflow experiments tracking
- `plx_training.py`, training script with Polyaxon experiments tracking
- `common_training.py`, common training code used by above files
 
Training scripts contain `run` method required by [py_config_runner](https://github.com/vfdev-5/py_config_runner) to 
run a script with a configuration. Training logic is setup inside `training` method and configures a distributed trainer, 
2 evaluators and various logging handlers to tensorboard, mlflow/polyaxon logger and tqdm.

### Configurations

- [baseline_resnet50.py](configs/train/baseline_resnet50.py) : trains ResNet50

### Results

Model | Training Top-1 Accuracy | Training Top-5 Accuracy | Test Top-1 Accuracy | Test Top-5 Accuracy
---|---|---|---|---
ResNet-50 | 78% | 92% | 77% | 94%

## Acknowledgements

Part of trainings was done within [Tesla GPU Test Drive](https://www.nvidia.com/en-us/data-center/tesla/gpu-test-drive/) 
on 2 Nvidia V100 GPUs.

![tb_dashboard_images](assets/tb_dashboard_images.png)

Pytorch-Mirror/ignite

简介

发行版

贡献者

近期动态

Pytorch-Mirror/ignite .gitee-modal { width: 500px !important; }

简介

发行版

贡献者

近期动态

搜索帮助

Pytorch-Mirror/ignite