---
sidebar_label: "Training-Pytorch-Model"
sidebar_position: 2
---
%%bash
git clone https://github.com/pytorch/examples

%%bash
python ./examples/mnist_rnn/main.py --save-model

%%bash
mkdir ./data

from torchvision import datasets
from torchvision.transforms import ToTensor

training_data = datasets.MNIST(
    root="./data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.MNIST(
    root="./data",
    train=False,
    download=True,
    transform=ToTensor()
)

!curl -sL https://get.bacalhau.org/install.sh | bash

%%bash --out job_id
bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
--wait \
--id-only \
pytorch/pytorch \
-w /outputs \
 -v QmdeQjz1HQQdT9wT2NHX86Le9X6X6ySGxp8dfRUKPtgziw:/data \
-u https://raw.githubusercontent.com/pytorch/examples/main/mnist_rnn/main.py \
-- python ../inputs/main.py --save-model

%env JOB_ID={job_id}

%%bash
bacalhau list --id-filter ${JOB_ID}

%%bash
bacalhau describe ${JOB_ID}

%%bash
rm -rf results && mkdir -p results
bacalhau get $JOB_ID --output-dir results

%%bash
ls results/

%%bash
cat results/combined_results/stdout

%%bash
ls results/combined_results/outputs/