<jupyter_start><jupyter_text>Benchmark a stochastic gradient descent algorithm on CIFAR10See the [github repository](https://github.com/ifeherva/optimizer-benchmark) for details. Clone github repository<jupyter_code>!git clone -l -s git://github.com/ifeherva/optimizer-benchmark.git cloned-repo
%cd cloned-repo
!pip install tqdm wandb
from train_cifar10 import parse_args, train_cifar10
run_configuration = [
    '--model', 'resnet34',
    '--optim', 'sgd',  # Which optimizer to use
    '--epochs', '200',  # Default is 200
    '--wandb-project', 'optimizer-evaluation' # Uncomment line to store data in Weights and Biases, dont' forget to create the project first!
]
run_config = parse_args(run_configuration)<jupyter_output><empty_output><jupyter_text>Execute training<jupyter_code>train_cifar10(*run_config)<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '440187', 'license': 'Apache-2.0', 'n_tokens_mistral': 298, 'n_tokens_neox': 278, 'n_words': 67, 'path': 'Optimizer_benchmark_pytorch.ipynb', 'repo_name': 'ifeherva/optimizer-benchmark', 'size': '841'}