def top_level_task():
ffconfig = FFConfig()
ffconfig.parse_args()
print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %
(ffconfig.get_batch_size(), ffconfig.get_workers_per_node(),
ffconfig.get_num_nodes()))
ffmodel = FFModel(ffconfig)
dims_input = [ffconfig.get_batch_size(), 3, 229, 229]
input_tensor = ffmodel.create_tensor(dims_input, DataType.DT_FLOAT)
torch_model = PyTorchModel("alexnet.ff")
output_tensors = torch_model.apply(ffmodel, [input_tensor])
ffoptimizer = SGDOptimizer(ffmodel, 0.01)
ffmodel.set_sgd_optimizer(ffoptimizer)
ffmodel.compile(loss_type=LossType.LOSS_SPARSE_CATEGORICAL_CROSSENTROPY,
metrics=[
MetricsType.METRICS_ACCURACY,
MetricsType.METRICS_SPARSE_CATEGORICAL_CROSSENTROPY
])
label_tensor = ffmodel.get_label_tensor()
num_samples = 10000
(x_train, y_train), (x_test, y_test) = cifar10.load_data(num_samples)
full_input_np = np.zeros((num_samples, 3, 229, 229), dtype=np.float32)
for i in range(0, num_samples):
image = x_train[i, :, :, :]
image = image.transpose(1, 2, 0)
pil_image = Image.fromarray(image)
pil_image = pil_image.resize((229, 229), Image.NEAREST)
image = np.array(pil_image, dtype=np.float32)
image = image.transpose(2, 0, 1)
full_input_np[i, :, :, :] = image
full_input_np /= 255
y_train = y_train.astype('int32')
full_label_np = y_train
dataloader_input = ffmodel.create_data_loader(input_tensor, full_input_np)
dataloader_label = ffmodel.create_data_loader(label_tensor, full_label_np)
num_samples = dataloader_input.get_num_samples()
ffmodel.init_layers()
epochs = ffconfig.get_epochs()
ts_start = ffconfig.get_current_time()
ffmodel.fit(x=dataloader_input, y=dataloader_label, epochs=epochs)
ts_end = ffconfig.get_current_time()
run_time = 1e-6 * (ts_end - ts_start)
print("epochs %d, ELAPSED TIME = %.4fs, THROUGHPUT = %.2f samples/s\n" %
(epochs, run_time, num_samples * epochs / run_time))
def top_level_task():
ffconfig = FFConfig()
alexnetconfig = NetConfig()
print(alexnetconfig.dataset_path)
print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %(ffconfig.batch_size, ffconfig.workers_per_node, ffconfig.num_nodes))
ffmodel = FFModel(ffconfig)
dims_input = [ffconfig.batch_size, 1]
input = ffmodel.create_tensor(dims_input, DataType.DT_FLOAT)
torch_model = PyTorchModel("customParam.ff")
output_tensors = torch_model.apply(ffmodel, [input])
t = ffmodel.softmax(output_tensors[0])
ffoptimizer = SGDOptimizer(ffmodel, 0.01)
ffmodel.optimizer = ffoptimizer
ffmodel.compile(loss_type=LossType.LOSS_SPARSE_CATEGORICAL_CROSSENTROPY, metrics=[MetricsType.METRICS_ACCURACY, MetricsType.METRICS_SPARSE_CATEGORICAL_CROSSENTROPY])
label = ffmodel.label_tensor
num_samples = 10000
x_sample_train = np.random.rand(num_samples)
x_sample_test = np.random.rand(num_samples)
(x_train, y_train), (x_test, y_test) = (x_sample_train,x_sample_train), (x_sample_test,x_sample_test)
full_input_np = np.zeros((num_samples, 3, 229, 229), dtype=np.float32)
for i in range(0, num_samples):
image = x_train[i, :, :, :]
image = image.transpose(1, 2, 0)
pil_image = Image.fromarray(image)
pil_image = pil_image.resize((229,229), Image.NEAREST)
image = np.array(pil_image, dtype=np.float32)
image = image.transpose(2, 0, 1)
full_input_np[i, :, :, :] = image
full_input_np /= 255
y_train = y_train.astype('int32')
full_label_np = y_train
dataloader_input = ffmodel.create_data_loader(input, full_input_np)
dataloader_label = ffmodel.create_data_loader(label, full_label_np)
num_samples = dataloader_input.num_samples
assert dataloader_input.num_samples == dataloader_label.num_samples
ffmodel.init_layers()
epochs = ffconfig.epochs
ts_start = ffconfig.get_current_time()
ffmodel.fit(x=dataloader_input, y=dataloader_label, epochs=epochs)
ts_end = ffconfig.get_current_time()
run_time = 1e-6 * (ts_end - ts_start);
print("epochs %d, ELAPSED TIME = %.4fs, THROUGHPUT = %.2f samples/s\n" %(epochs, run_time, num_samples * epochs / run_time));
def top_level_task():
ffconfig = FFConfig()
ffconfig.parse_args()
print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %(ffconfig.get_batch_size(), ffconfig.get_workers_per_node(), ffconfig.get_num_nodes()))
ffmodel = FFModel(ffconfig)
dims = [ffconfig.get_batch_size(), 784]
input_tensor = ffmodel.create_tensor(dims, DataType.DT_FLOAT);
num_samples = 60000
torch_model = PyTorchModel("mlp.ff")
output_tensors = torch_model.apply(ffmodel, [input_tensor])
ffoptimizer = SGDOptimizer(ffmodel, 0.01)
ffmodel.set_sgd_optimizer(ffoptimizer)
ffmodel.compile(loss_type=LossType.LOSS_SPARSE_CATEGORICAL_CROSSENTROPY, metrics=[MetricsType.METRICS_ACCURACY, MetricsType.METRICS_SPARSE_CATEGORICAL_CROSSENTROPY])
label = ffmodel.get_label_tensor()
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
x_train = x_train.reshape(60000, 784)
x_train = x_train.astype('float32')
x_train /= 255
y_train = y_train.astype('int32')
y_train = np.reshape(y_train, (len(y_train), 1))
dims_full_input = [num_samples, 784]
full_input = ffmodel.create_tensor(dims_full_input, DataType.DT_FLOAT)
dims_full_label = [num_samples, 1]
full_label = ffmodel.create_tensor(dims_full_label, DataType.DT_INT32)
full_input.attach_numpy_array(ffconfig, x_train)
full_label.attach_numpy_array(ffconfig, y_train)
dataloader_input = SingleDataLoader(ffmodel, input_tensor, full_input, num_samples, DataType.DT_FLOAT)
dataloader_label = SingleDataLoader(ffmodel, label, full_label, num_samples, DataType.DT_INT32)
full_input.detach_numpy_array(ffconfig)
full_label.detach_numpy_array(ffconfig)
ffmodel.init_layers()
epochs = ffconfig.get_epochs()
ts_start = ffconfig.get_current_time()
ffmodel.train((dataloader_input, dataloader_label), epochs)
ts_end = ffconfig.get_current_time()
run_time = 1e-6 * (ts_end - ts_start);
print("epochs %d, ELAPSED TIME = %.4fs, THROUGHPUT = %.2f samples/s\n" %(epochs, run_time, num_samples * epochs / run_time));
def top_level_task():
ffconfig = FFConfig()
ffconfig.parse_args()
print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %(ffconfig.get_batch_size(), ffconfig.get_workers_per_node(), ffconfig.get_num_nodes()))
ffmodel = FFModel(ffconfig)
dims_input = [ffconfig.get_batch_size(), 3, 32, 32]
input_tensor = ffmodel.create_tensor(dims_input, DataType.DT_FLOAT)
torch_model = PyTorchModel("cnn.ff")
output_tensors = torch_model.apply(ffmodel, [input_tensor, input_tensor])
t = ffmodel.softmax(output_tensors[0])
ffoptimizer = SGDOptimizer(ffmodel, 0.01)
ffmodel.set_sgd_optimizer(ffoptimizer)
ffmodel.compile(loss_type=LossType.LOSS_SPARSE_CATEGORICAL_CROSSENTROPY, metrics=[MetricsType.METRICS_ACCURACY, MetricsType.METRICS_SPARSE_CATEGORICAL_CROSSENTROPY])
label_tensor = ffmodel.get_label_tensor()
num_samples = 10000
(x_train, y_train), (x_test, y_test) = cifar10.load_data(num_samples)
x_train = x_train.astype('float32')
x_train /= 255
full_input_array = x_train
y_train = y_train.astype('int32')
full_label_array = y_train
dataloader_input = ffmodel.create_data_loader(input_tensor, full_input_array)
dataloader_label = ffmodel.create_data_loader(label_tensor, full_label_array)
num_samples = dataloader_input.get_num_samples()
ffmodel.init_layers()
layers = ffmodel.get_layers()
for layer in layers:
print(layers[layer].name)
layer = ffmodel.get_layer_by_name("relu_1")
print(layer)
epochs = ffconfig.get_epochs()
ts_start = ffconfig.get_current_time()
ffmodel.fit(x=dataloader_input, y=dataloader_label, epochs=epochs)
ts_end = ffconfig.get_current_time()
run_time = 1e-6 * (ts_end - ts_start);
print("epochs %d, ELAPSED TIME = %.4fs, THROUGHPUT = %.2f samples/s\n" %(epochs, run_time, num_samples * epochs / run_time));
def top_level_task():
ffconfig = FFConfig()
print("Python API batchSize(%d) workersPerNodes(%d) numNodes(%d)" %(ffconfig.batch_size, ffconfig.workers_per_node, ffconfig.num_nodes))
ffmodel = FFModel(ffconfig)
dims = [ffconfig.batch_size, 784]
input_tensor = ffmodel.create_tensor(dims, DataType.DT_FLOAT);
num_samples = 60000
model = MLP()
torch_model = PyTorchModel(model=model)
output_tensors = torch_model.apply(ffmodel, [input_tensor])
ffoptimizer = SGDOptimizer(ffmodel, 0.01)
ffmodel.optimizer = ffoptimizer
ffmodel.compile(loss_type=LossType.LOSS_SPARSE_CATEGORICAL_CROSSENTROPY, metrics=[MetricsType.METRICS_ACCURACY, MetricsType.METRICS_SPARSE_CATEGORICAL_CROSSENTROPY])
label_tensor = ffmodel.label_tensor
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
x_train = x_train.reshape(60000, 784)
x_train = x_train.astype('float32')
x_train /= 255
y_train = y_train.astype('int32')
y_train = np.reshape(y_train, (len(y_train), 1))
dataloader_input = ffmodel.create_data_loader(input_tensor, x_train)
dataloader_label = ffmodel.create_data_loader(label_tensor, y_train)
ffmodel.init_layers()
epochs = ffconfig.epochs
ts_start = ffconfig.get_current_time()
ffmodel.fit(x=dataloader_input, y=dataloader_label, epochs=epochs)
ts_end = ffconfig.get_current_time()
run_time = 1e-6 * (ts_end - ts_start);
print("epochs %d, ELAPSED TIME = %.4fs, THROUGHPUT = %.2f samples/s\n" %(epochs, run_time, num_samples * epochs / run_time));
