def test_multi_gpu_model_ddp_test_only(tmpdir, as_module):
# call the script
call_training_script(ddp_model, CLI_ARGS, "test", tmpdir, as_module=as_module)
# load the results of the script
result_path = os.path.join(tmpdir, "ddp.result")
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result["status"] == "complete"
def test_multi_gpu_model_ddp_test_only(tmpdir):
# call the script
call_training_script(ddp_model, CLI_ARGS, 'test', tmpdir)
# load the results of the script
result_path = os.path.join(tmpdir, 'ddp.result')
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result['status'] == 'complete'
def test_multi_gpu_model_ddp_fit_test(tmpdir, as_module):
# call the script
call_training_script(ddp_model, CLI_ARGS, "fit_test", tmpdir, timeout=20, as_module=as_module)
# load the results of the script
result_path = os.path.join(tmpdir, "ddp.result")
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result["status"] == "complete"
model_outs = result["result"]
for out in model_outs:
assert out["test_acc"] > 0.7
def test_multi_gpu_model_ddp_fit_test(tmpdir):
# call the script
call_training_script(ddp_model, CLI_ARGS, 'fit_test', tmpdir, timeout=20)
# load the results of the script
result_path = os.path.join(tmpdir, 'ddp.result')
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result['status'] == 'complete'
model_outs = result['result']
for out in model_outs:
assert out['test_acc'] > 0.7
def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
# call the script
std, err = call_training_script(ddp_model, cli_args, 'fit', tmpdir, timeout=120)
# load the results of the script
result_path = os.path.join(tmpdir, 'ddp.result')
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result['status'] == 'complete'
