How to use is_container_running method in localstack

Best Python code snippet using localstack_python

node.py

Source:node.py

...188        :type: str189        """190        self._transition_reason = transition_reason191    @property192    def is_container_running(self):193        """194        Gets the is_container_running of this Node.195        :return: The is_container_running of this Node.196        :rtype: bool197        """198        return self._is_container_running199    @is_container_running.setter200    def is_container_running(self, is_container_running):201        """202        Sets the is_container_running of this Node.203        :param is_container_running: The is_container_running of this Node.204        :type: bool205        """206        self._is_container_running = is_container_running207    @property208    def os_version(self):209        """210        Gets the os_version of this Node.211        :return: The os_version of this Node.212        :rtype: str213        """214        return self._os_version...

test_elasticsearch_eager.py

Source:test_elasticsearch_eager.py

...31    "Content-Type": "application/json",32    "Authorization": "Basic ZWxhc3RpYzpkZWZhdWx0X3Bhc3N3b3Jk",33}34ATTRS = ["name", "gender", "age", "fare", "vip", "survived"]35def is_container_running():36    """Check whether the elasticsearch container is up and running37    with the correct port being exposed.38    """39    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)40    status = sock.connect_ex(("127.0.0.1", 9200))41    if status == 0:42        return True43    else:44        return False45@pytest.mark.skipif(not is_container_running(), reason="The container is not running")46def test_create_index():47    """Create an index in the cluster"""48    create_index_url = "{}/{}".format(NODE, INDEX)49    res = requests.put(create_index_url, headers=HEADERS)50    assert res.status_code == 20051@pytest.mark.parametrize(52    "record",53    [54        (("person1", "Male", 20, 80.52, False, 1)),55        (("person2", "Female", 30, 40.88, True, 0)),56        (("person3", "Male", 40, 20.73, True, 0)),57        (("person4", "Female", 50, 100.99, False, 1)),58    ],59)60@pytest.mark.skipif(not is_container_running(), reason="The container is not running")61def test_populate_data(record):62    """Populate the index with data"""63    put_item_url = "{}/{}/{}".format(NODE, INDEX, DOC_TYPE)64    data = {}65    for idx, attr in enumerate(ATTRS):66        data[attr] = record[idx]67    res = requests.post(put_item_url, json=data, headers=HEADERS)68    # The 201 status code indicates the documents have been properly indexed69    assert res.status_code == 20170    # allow the cluster to index in the background.71    time.sleep(1)72@pytest.mark.skipif(not is_container_running(), reason="The container is not running")73def test_elasticsearch_io_dataset():74    """Test the functionality of the ElasticsearchIODataset"""75    dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(76        nodes=[NODE], index=INDEX, doc_type=DOC_TYPE, headers=HEADERS77    )78    assert issubclass(type(dataset), tf.data.Dataset)79    for item in dataset:80        for attr in ATTRS:81            assert attr in item82@pytest.mark.skipif(not is_container_running(), reason="The container is not running")83def test_elasticsearch_io_dataset_no_auth():84    """Test the functionality of the ElasticsearchIODataset when basic auth is85    required but the associated header is not passed.86    """87    try:88        dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(89            nodes=[NODE], index=INDEX, doc_type=DOC_TYPE90        )91    except ConnectionError as e:92        assert str(93            e94        ) == "No healthy node available for the index: {}, please check the cluster config".format(95            INDEX96        )97@pytest.mark.skipif(not is_container_running(), reason="The container is not running")98def test_elasticsearch_io_dataset_batch():99    """Test the functionality of the ElasticsearchIODataset"""100    BATCH_SIZE = 2101    dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(102        nodes=[NODE], index=INDEX, doc_type=DOC_TYPE, headers=HEADERS103    ).batch(BATCH_SIZE)104    assert issubclass(type(dataset), tf.data.Dataset)105    for item in dataset:106        for attr in ATTRS:107            assert attr in item108            assert len(item[attr]) == BATCH_SIZE109@pytest.mark.skipif(not is_container_running(), reason="The container is not running")110def test_elasticsearch_io_dataset_training():111    """Test the functionality of the ElasticsearchIODataset by training a112    tf.keras model on the structured data.113    """114    BATCH_SIZE = 2115    dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(116        nodes=[NODE], index=INDEX, doc_type=DOC_TYPE, headers=HEADERS117    )118    dataset = dataset.map(lambda v: (v, v.pop("survived")))119    dataset = dataset.batch(BATCH_SIZE)120    assert issubclass(type(dataset), tf.data.Dataset)121    feature_columns = []122    # Numeric column123    fare_column = feature_column.numeric_column("fare")124    feature_columns.append(fare_column)125    # Bucketized column126    age = feature_column.numeric_column("age")127    age_buckets = feature_column.bucketized_column(age, boundaries=[10, 30])128    feature_columns.append(age_buckets)129    # Categorical column130    gender = feature_column.categorical_column_with_vocabulary_list(131        "gender", ["Male", "Female"]132    )133    gender_indicator = feature_column.indicator_column(gender)134    feature_columns.append(gender_indicator)135    # Convert the feature columns into a tf.keras layer136    feature_layer = tf.keras.layers.DenseFeatures(feature_columns)137    # Build the model138    model = tf.keras.Sequential(139        [140            feature_layer,141            layers.Dense(128, activation="relu"),142            layers.Dense(128, activation="relu"),143            layers.Dropout(0.1),144            layers.Dense(1),145        ]146    )147    # Compile the model148    model.compile(149        optimizer="adam",150        loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),151        metrics=["accuracy"],152    )153    # train the model154    model.fit(dataset, epochs=5)155@pytest.mark.skipif(not is_container_running(), reason="The container is not running")156def test_cleanup():157    """Clean up the index"""158    delete_index_url = "{}/{}".format(NODE, INDEX)159    res = requests.delete(delete_index_url, headers=HEADERS)...

test_elasticsearch.py

Source:test_elasticsearch.py

...31    "Content-Type": "application/json",32    "Authorization": "Basic ZWxhc3RpYzpkZWZhdWx0X3Bhc3N3b3Jk",33}34ATTRS = ["name", "gender", "age", "fare", "vip", "survived"]35def is_container_running():36    """Check whether the elasticsearch container is up and running37    with the correct port being exposed.38    """39    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)40    status = sock.connect_ex(("127.0.0.1", 9200))41    if status == 0:42        return True43    else:44        return False45@pytest.mark.skipif(not is_container_running(), reason="The container is not running")46def test_create_index():47    """Create an index in the cluster"""48    create_index_url = f"{NODE}/{INDEX}"49    res = requests.put(create_index_url, headers=HEADERS)50    assert res.status_code == 20051@pytest.mark.parametrize(52    "record",53    [54        (("person1", "Male", 20, 80.52, False, 1)),55        (("person2", "Female", 30, 40.88, True, 0)),56        (("person3", "Male", 40, 20.73, True, 0)),57        (("person4", "Female", 50, 100.99, False, 1)),58    ],59)60@pytest.mark.skipif(not is_container_running(), reason="The container is not running")61def test_populate_data(record):62    """Populate the index with data"""63    put_item_url = f"{NODE}/{INDEX}/{DOC_TYPE}"64    data = {}65    for idx, attr in enumerate(ATTRS):66        data[attr] = record[idx]67    res = requests.post(put_item_url, json=data, headers=HEADERS)68    # The 201 status code indicates the documents have been properly indexed69    assert res.status_code == 20170    # allow the cluster to index in the background.71    time.sleep(1)72@pytest.mark.skipif(not is_container_running(), reason="The container is not running")73def test_elasticsearch_io_dataset():74    """Test the functionality of the ElasticsearchIODataset"""75    dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(76        nodes=[NODE], index=INDEX, doc_type=DOC_TYPE, headers=HEADERS77    )78    assert issubclass(type(dataset), tf.data.Dataset)79    for item in dataset:80        for attr in ATTRS:81            assert attr in item82@pytest.mark.skipif(not is_container_running(), reason="The container is not running")83def test_elasticsearch_io_dataset_no_auth():84    """Test the functionality of the ElasticsearchIODataset when basic auth is85    required but the associated header is not passed.86    """87    try:88        dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(89            nodes=[NODE], index=INDEX, doc_type=DOC_TYPE90        )91    except ConnectionError as e:92        assert str(93            e94        ) == "No healthy node available for the index: {}, please check the cluster config".format(95            INDEX96        )97@pytest.mark.skipif(not is_container_running(), reason="The container is not running")98def test_elasticsearch_io_dataset_batch():99    """Test the functionality of the ElasticsearchIODataset"""100    BATCH_SIZE = 2101    dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(102        nodes=[NODE], index=INDEX, doc_type=DOC_TYPE, headers=HEADERS103    ).batch(BATCH_SIZE)104    assert issubclass(type(dataset), tf.data.Dataset)105    for item in dataset:106        for attr in ATTRS:107            assert attr in item108            assert len(item[attr]) == BATCH_SIZE109@pytest.mark.skipif(not is_container_running(), reason="The container is not running")110def test_elasticsearch_io_dataset_training():111    """Test the functionality of the ElasticsearchIODataset by training a112    tf.keras model on the structured data.113    """114    BATCH_SIZE = 2115    dataset = tfio.experimental.elasticsearch.ElasticsearchIODataset(116        nodes=[NODE], index=INDEX, doc_type=DOC_TYPE, headers=HEADERS117    )118    dataset = dataset.map(lambda v: (v, v.pop("survived")))119    dataset = dataset.batch(BATCH_SIZE)120    assert issubclass(type(dataset), tf.data.Dataset)121    feature_columns = []122    # Numeric column123    fare_column = feature_column.numeric_column("fare")124    feature_columns.append(fare_column)125    # Bucketized column126    age = feature_column.numeric_column("age")127    age_buckets = feature_column.bucketized_column(age, boundaries=[10, 30])128    feature_columns.append(age_buckets)129    # Categorical column130    gender = feature_column.categorical_column_with_vocabulary_list(131        "gender", ["Male", "Female"]132    )133    gender_indicator = feature_column.indicator_column(gender)134    feature_columns.append(gender_indicator)135    # Convert the feature columns into a tf.keras layer136    feature_layer = tf.keras.layers.DenseFeatures(feature_columns)137    # Build the model138    model = tf.keras.Sequential(139        [140            feature_layer,141            layers.Dense(128, activation="relu"),142            layers.Dense(128, activation="relu"),143            layers.Dropout(0.1),144            layers.Dense(1),145        ]146    )147    # Compile the model148    model.compile(149        optimizer="adam",150        loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),151        metrics=["accuracy"],152    )153    # train the model154    model.fit(dataset, epochs=5)155@pytest.mark.skipif(not is_container_running(), reason="The container is not running")156def test_cleanup():157    """Clean up the index"""158    delete_index_url = f"{NODE}/{INDEX}"159    res = requests.delete(delete_index_url, headers=HEADERS)...

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