code stringlengths 3 6.57k |
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logger(self) |
create_logger(self) |
register_controller(self, controller) |
self.controller_queue.append(controller) |
run(self) |
controller(self) |
logging.getLogger(__name__) |
loss_fn(pred, target) |
F.nll_loss(input=pred, target=target) |
Net(nn.Module) |
__init__(self) |
super(Net, self) |
__init__() |
nn.Conv2d(1, 20, 5, 1) |
nn.Conv2d(20, 50, 5, 1) |
nn.Linear(4 * 4 * 50, 500) |
nn.Linear(500, 10) |
forward(self, x) |
F.relu(self.conv1(x) |
F.max_pool2d(x, 2, 2) |
F.relu(self.conv2(x) |
F.max_pool2d(x, 2, 2) |
x.view(-1, 4 * 4 * 50) |
F.relu(self.fc1(x) |
self.fc2(x) |
F.log_softmax(x, dim=1) |
define_and_get_arguments(args=sys.argv[1:]) |
parser.add_argument("--batch_size", type=int, default=32, help="batch size of the training") |
parser.add_argument("--lr", type=float, default=0.1, help="learning rate") |
parser.add_argument("--cuda", action="store_true", help="use cuda") |
parser.add_argument("--seed", type=int, default=1, help="seed used for randomization") |
parser.add_argument("--save_model", action="store_true", help="if set, model will be saved") |
parser.parse_args(args=args) |
round (for logging purposes) |
min(max_nr_batches, nr_available_batches) |
train_config.send(worker) |
format(train_config.lr) |
worker.async_fit(dataset_key="mnist", return_ids=[0]) |
logger.info("Training round: %s, worker: %s, avg_loss: %s", curr_round, worker.id, loss.mean() |
train_config.model_ptr.get() |
evaluate_models_on_test_data(test_loader, results) |
np.set_printoptions(formatter={"float": "{: .0f}".format}) |
evaluate_model(worker_id, worker_model, "cpu", test_loader, print_target_hist=False) |
evaluate_model(worker_id, model, device, test_loader, print_target_hist=False) |
model.eval() |
np.zeros(10) |
np.zeros(10) |
torch.no_grad() |
data.to(device) |
target.to(device) |
np.histogram(target, bins=10, range=(0, 10) |
model(data) |
loss_fn(output, target) |
item() |
output.argmax(dim=1, keepdim=True) |
np.histogram(pred, bins=10, range=(0, 10) |
pred.eq(target.view_as(pred) |
sum() |
item() |
len(test_loader.dataset) |
logger.info("Target histogram: %s", hist_target) |
logger.info("Prediction hist.: %s", hist_pred) |
s (%s) |
format(test_loss) |
len(test_loader.dataset) |
format(100.0 * correct / len(test_loader.dataset) |
main() |
define_and_get_arguments() |
sy.TorchHook(torch) |
workers.WebsocketClientWorker(id="alice", port=8777, **kwargs_websocket) |
workers.WebsocketClientWorker(id="bob", port=8778, **kwargs_websocket) |
workers.WebsocketClientWorker(id="charlie", port=8779, **kwargs_websocket) |
torch.cuda.is_available() |
torch.manual_seed(args.seed) |
torch.device("cuda" if use_cuda else "cpu") |
transforms.ToTensor() |
transforms.Normalize((0.1307,) |
Net() |
to(device) |
test_loader.__iter__() |
next() |
torch.jit.trace(model, data) |
range(1, args.training_rounds + 1) |
logger.info("Starting training round %s/%s", curr_round, args.training_rounds) |
evaluate_models_on_test_data(test_loader, results) |
utils.federated_avg(models) |
max(0.98 * learning_rate, args.lr * 0.01) |
torch.save(model.state_dict() |
logging.getLogger("run_websocket_server") |
s(l:%(lineno) |
logging.basicConfig(format=FORMAT) |
logger.setLevel(level=logging.DEBUG) |
logging.getLogger("websockets") |
websockets_logger.setLevel(logging.INFO) |
websockets_logger.addHandler(logging.StreamHandler() |
asyncio.get_event_loop() |
run_until_complete(main() |
BaseArrow(object) |
__init__(self, width, height, offset) |
TriggerDict() |
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