58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import pytest
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import torch
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import os
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from tests.backends import ddp_model
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from tests.utilities.dist import call_training_script
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@pytest.mark.parametrize('cli_args', [
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pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
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])
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
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# call the script
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std, err = call_training_script(ddp_model, cli_args, 'fit', tmpdir, timeout=120)
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# load the results of the script
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result_path = os.path.join(tmpdir, 'ddp.result')
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result = torch.load(result_path)
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# verify the file wrote the expected outputs
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assert result['status'] == 'complete'
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@pytest.mark.parametrize('cli_args', [
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pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
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])
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
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# call the script
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call_training_script(ddp_model, cli_args, 'test', tmpdir)
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# load the results of the script
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result_path = os.path.join(tmpdir, 'ddp.result')
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result = torch.load(result_path)
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# verify the file wrote the expected outputs
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assert result['status'] == 'complete'
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@pytest.mark.parametrize('cli_args', [
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pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
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])
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):
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# call the script
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call_training_script(ddp_model, cli_args, 'fit_test', tmpdir, timeout=20)
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# load the results of the script
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result_path = os.path.join(tmpdir, 'ddp.result')
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result = torch.load(result_path)
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# verify the file wrote the expected outputs
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assert result['status'] == 'complete'
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model_outs = result['result']
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for out in model_outs:
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assert out['test_acc'] > 0.90
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