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How to use inspect_network method in localstack

Best Python code snippet using localstack_python

network_test.py

Source:network_test.py

...20        networks_by_name = self.client.networks(names=[net_name])21        self.assertEqual([n['Id'] for n in networks_by_name], [net_id])22        networks_by_partial_id = self.client.networks(ids=[net_id[:8]])23        self.assertEqual([n['Id'] for n in networks_by_partial_id], [net_id])24    def test_inspect_network(self):25        net_name, net_id = self.create_network()26        net = self.client.inspect_network(net_id)27        self.assertEqual(net, {28            u'Name': net_name,29            u'Id': net_id,30            u'Driver': 'bridge',31            u'Containers': {},32            u'IPAM': {u'Config': [{}], u'Driver': 'default'},33            u'Options': {},34            u'Scope': 'local'35        })36    def test_create_network_with_host_driver_fails(self):37        net_name = 'dockerpy{}'.format(random.getrandbits(24))[:14]38        with pytest.raises(docker.errors.APIError):39            self.client.create_network(net_name, driver='host')40    def test_remove_network(self):41        initial_size = len(self.client.networks())42        net_name, net_id = self.create_network()43        self.assertEqual(len(self.client.networks()), initial_size + 1)44        self.client.remove_network(net_id)45        self.assertEqual(len(self.client.networks()), initial_size)46    def test_connect_and_disconnect_container(self):47        net_name, net_id = self.create_network()48        container = self.client.create_container('busybox', 'top')49        self.tmp_containers.append(container)50        self.client.start(container)51        network_data = self.client.inspect_network(net_id)52        self.assertFalse(network_data.get('Containers'))53        self.client.connect_container_to_network(container, net_id)54        network_data = self.client.inspect_network(net_id)55        self.assertEqual(56            list(network_data['Containers'].keys()),57            [container['Id']])58        self.client.disconnect_container_from_network(container, net_id)59        network_data = self.client.inspect_network(net_id)60        self.assertFalse(network_data.get('Containers'))61    def test_connect_on_container_create(self):62        net_name, net_id = self.create_network()63        container = self.client.create_container(64            image='busybox',65            command='top',66            host_config=self.client.create_host_config(network_mode=net_name),67        )68        self.tmp_containers.append(container)69        self.client.start(container)70        network_data = self.client.inspect_network(net_id)71        self.assertEqual(72            list(network_data['Containers'].keys()),73            [container['Id']])74        self.client.disconnect_container_from_network(container, net_id)75        network_data = self.client.inspect_network(net_id)...

bnn_test.py

Source:bnn_test.py

...9    bnn = BinaryNeuralNetwork(in_dim=in_dim, out_dim=out_dim)10    x = np.random.randn(n_imgs, *in_dim)11    y = np.random.randint(out_dim, size=n_imgs)12    bnn.fit(x, y, x, y)13    vals = bnn.inspect_network(x, y)14    param_file = bnn.param_file15    bnn = BinaryNeuralNetwork(in_dim=in_dim, out_dim=out_dim,16                              param_file=param_file)17    vals1 = bnn.inspect_network(x, y)18    assert np.array_equal(vals['score'], vals1['score'])19def test_tnn():20    """Test a ternary neural network."""21    network_params = {22        'conv': [32],23        'max_pool': [True, False],24        'filter_size': 3,25        'fc': [10],26        'bin_weights': [True] * 1,27        'bin_acts': [True] * 1,28        'levels': 3,29        'threshold': 0.2,30    }31    in_dim = (28, 28, 1)32    out_dim = 1033    n_imgs = 10034    bnn = BinaryNeuralNetwork(in_dim=in_dim, out_dim=out_dim,35                              network_params=network_params)36    x = np.random.randn(n_imgs, *in_dim)37    y = np.random.randint(out_dim, size=n_imgs)38    bnn.fit(x, y, x, y)39    vals = bnn.inspect_network(x, y)40    param_file = bnn.param_file41    bnn = BinaryNeuralNetwork(in_dim=in_dim, out_dim=out_dim,42                              param_file=param_file,43                              network_params=network_params)44    vals1 = bnn.inspect_network(x, y)45    assert np.array_equal(vals['score'], vals1['score'])46if __name__ == '__main__':47    test_bnn()...

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