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# Cifar10/Pytorch on **ruche**
## Cifar source code
Pytorch code for cifar10 from the Pytorch sample : [https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html]
## Setting Conda environment
Launch an interactive session on a GPU server.
```shell
srun --nodes=1 --time=01:00:00 -p gpu --pty /bin/bash
```
OR
```shell
./interactive_gpu.sh
```
In this session :
```shell
module load anaconda3/2020.02/gcc-9.2.0
module load cuda/10.2.89/intel-19.0.3.199
conda create --name cifar10
source activate cifar10
conda install pytorch torchvision matplotlib -c pytorch
conda env export > config/environment.yml # save conda environment description
```
OR
```shell
./config/config_env.sh
```
## Cifar Data
Cifar data is automatically downloaded in current directory (342MB).
## Script detail
Scripts :
* `show_images.py` : display 4 images. Use this script for data exploration
* `train_network.py` : train the network and save state into `output/network.save`
* `train_network_gpu.py` : train the network with GPU acceleration
* `test_network.py` : test the `output/network.save` network predictions on 4 images from the validation dataset. Display the images, the tags and the predicted values.
## Launch configuration and run the sample network
### Configure environment
```shell
# configure environment
sbatch pbs_configure.sh
```
### Train the network
Train the network on CPU.
```shell
# once configuration is over, run code
sbatch pbs_train.sh
```
Train the network on GPU.
```shell
# once configuration is over, run code
sbatch pbs_train_gpu.sh
```
Output network is saved on the `output/network.save`