def test_cifar_resnet_distributed(device_id):
    params = [ "-datadir", base_path]
    str_out = mpiexec_execute(script_under_test, mpiexec_params, params)

    #Training loss of the generator at worker: {0} is: {2.201804}, time taken is: {40} seconds
    results = re.findall("Training loss of the generator at worker: \{.+?\} is: \{.+?\}", str_out)
    assert(len(results) == 4)
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def test_finalize_with_exception_no_hang():
    str_out = mpiexec_execute(__file__, ["-n", "2"], [])

    results = re.findall("Completed with exception.", str_out)
    assert len(results) == 1

    results = re.findall("Completed successfully.", str_out)
    assert len(results) == 0
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def test_finalize_with_exception_no_hang():
    str_out = mpiexec_execute(__file__, ["-n", "2"], [])

    results = re.findall("Completed with exception.", str_out)
    assert len(results) == 1

    results = re.findall("Completed successfully.", str_out)
    assert len(results) == 0
def test_sample_count_with_several_distributed_learners():
    str_out = mpiexec_execute(__file__, ["-n", "2"], [])

    results = re.findall("Completed with exception.", str_out)
    if len(results) != 0:
        print(str_out)
        assert False

    results = re.findall("Completed successfully.", str_out)
    if len(results) != 2:
        print(str_out)
        assert False
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def test_sample_count_with_several_distributed_learners():
    str_out = mpiexec_execute(__file__, ["-n", "2"], [])

    results = re.findall("Completed with exception.", str_out)
    if len(results) != 0:
        print(str_out)
        assert False

    results = re.findall("Completed successfully.", str_out)
    if len(results) != 2:
        print(str_out)
        assert False
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def test_htk_lstm_truncated_distributed_gpu_with_cv(device_id):
    # Make sure that full sequence cross validation
    # works in the middle of bptt training
    params = [ "-n", "2",
               "-datadir", an4_dataset_directory(),
               "-q", "1",
               "-m", "640",
               "-e", "1500",
               "-cvfreq", "1000",
               "-device", str(device_id) ]

    output = mpiexec_execute(device_id=device_id, script=script_under_test, mpiexec_params=mpiexec_params, params=params)
    results = re.findall(r"Finished Evaluation \[.+?\]: Minibatch\[.+?\]: metric = (.+?)%", output)
    assert len(results) == 6, output
def test_cifar_convnet_distributed_block_momentum(device_id):
    params = [ "-n", "1",
               "-m", "64",
               "-e", "13000",
               "-datadir", base_path,
               "-b", "1600",
               "-r",
               "-device", str(device_id) ]
    # 13000 samples / 2 worker / 64 mb_size = 101 minibatchs. 
    # We expect to see only Minibatch[ 1 -100] 
    output = mpiexec_execute(script_under_test, mpiexec_params, params, device_id=device_id)
    results = re.findall(r"Minibatch\[(.+?)\]: loss = .+?%", output)
    assert len(results) == 2
    assert results[0] == '   1- 100'
    assert results[1] == '   1- 100'
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def test_cifar_convnet_distributed_block_momentum(device_id):
    params = [
        "-n", "1", "-m", "64", "-e", "13000", "-datadir", base_path, "-b",
        "1600", "-r", "-device",
        str(device_id)
    ]
    # 13000 samples / 2 worker / 64 mb_size = 101 minibatchs.
    # We expect to see only Minibatch[ 1 -100]
    output = mpiexec_execute(script_under_test,
                             mpiexec_params,
                             params,
                             device_id=device_id)
    results = re.findall("Minibatch\[(.+?)\]: loss = .+?%", output)
    assert len(results) == 2
    assert results[0] == '   1- 100'
    assert results[1] == '   1- 100'