From df4367c45d9436f71a5e3562f59e67968c07e0d1 Mon Sep 17 00:00:00 2001 From: AvivSham Date: Mon, 8 Feb 2021 10:52:08 +0200 Subject: [PATCH] adding delta theta update in comment --- .gitignore | 3 +- experiments/hetro/dataset.py | 7 +- experiments/hetro/local_layers/models.py | 104 ++++++ experiments/hetro/local_layers/node.py | 41 +++ experiments/hetro/local_layers/trainer.py | 322 ++++++++++++++++++ experiments/hetro/trainer.py | 20 +- experiments/hetro/wandb/latest-run | 1 - .../files/config.yaml | 115 ------- .../files/requirements.txt | 46 --- .../files/wandb-metadata.json | 39 --- .../files/wandb-summary.json | 1 - .../run-5s6q46n4.wandb | Bin 2636 -> 0 bytes .../files/config.yaml | 115 ------- .../files/requirements.txt | 46 --- .../files/wandb-metadata.json | 39 --- .../files/wandb-summary.json | 1 - .../run-9fy3pra7.wandb | Bin 2636 -> 0 bytes .../files/config.yaml | 115 ------- .../files/requirements.txt | 46 --- .../files/wandb-metadata.json | 39 --- 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normalize=True, val_size=10000): - if data_name == 'mnist': - normalization = transforms.Normalize( - (0.1307,), (0.3081,) - ) - data_obj = MNIST - elif data_name =='cifar10': + if data_name =='cifar10': normalization = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) data_obj = CIFAR10 elif data_name == 'cifar100': diff --git a/experiments/hetro/local_layers/models.py b/experiments/hetro/local_layers/models.py new file mode 100644 index 0000000..94b59cd --- /dev/null +++ b/experiments/hetro/local_layers/models.py @@ -0,0 +1,104 @@ +import torch.nn.functional as F +from torch import nn +from torch.nn.utils import spectral_norm + + +class LocalLayer(nn.Module): + + def __init__(self, n_input=84, n_output=2, nonlinearity=False): + super().__init__() + self.nonlinearity = nonlinearity + layers = [] + if nonlinearity: + layers.append(nn.ReLU()) + + layers.append(nn.Linear(n_input, n_output)) + self.layer = nn.Sequential(*layers) + + def forward(self, x): + return self.layer(x) + + +class CNNHyper(nn.Module): + def __init__( + self, n_nodes, embedding_dim, in_channels=3, out_dim=10, n_kernels=16, hidden_dim=100, + spec_norm=False, n_hidden=1): + super().__init__() + + self.in_channels = in_channels + self.out_dim = out_dim + self.n_kernels = n_kernels + self.embeddings = nn.Embedding(num_embeddings=n_nodes, embedding_dim=embedding_dim) + + layers = [ + spectral_norm(nn.Linear(embedding_dim, hidden_dim)) if spec_norm else nn.Linear(embedding_dim, hidden_dim), + ] + for _ in range(n_hidden): + layers.append(nn.ReLU(inplace=True)) + layers.append( + spectral_norm(nn.Linear(hidden_dim, hidden_dim)) if spec_norm else nn.Linear(hidden_dim, hidden_dim), + ) + + self.mlp = nn.Sequential(*layers) + + self.c1_weights = nn.Linear(hidden_dim, self.n_kernels * self.in_channels * 5 * 5) + self.c1_bias = nn.Linear(hidden_dim, self.n_kernels) + self.c2_weights = nn.Linear(hidden_dim, 2 * self.n_kernels * self.n_kernels * 5 * 5) + self.c2_bias = nn.Linear(hidden_dim, 2 * self.n_kernels) + self.l1_weights = nn.Linear(hidden_dim, 120 * 2 * self.n_kernels * 5 * 5) + self.l1_bias = nn.Linear(hidden_dim, 120) + self.l2_weights = nn.Linear(hidden_dim, 84 * 120) + self.l2_bias = nn.Linear(hidden_dim, 84) + # self.l3_weights = nn.Linear(hidden_dim, self.out_dim * 84) + # self.l3_bias = nn.Linear(hidden_dim, self.out_dim) + + if spec_norm: + self.c1_weights = spectral_norm(self.c1_weights) + self.c1_bias = spectral_norm(self.c1_bias) + self.c2_weights = spectral_norm(self.c2_weights) + self.c2_bias = spectral_norm(self.c2_bias) + self.l1_weights = spectral_norm(self.l1_weights) + self.l1_bias = spectral_norm(self.l1_bias) + self.l2_weights = spectral_norm(self.l2_weights) + self.l2_bias = spectral_norm(self.l2_bias) + # self.l3_weights = spectral_norm(self.l3_weights) + # self.l3_bias = spectral_norm(self.l3_bias) + + def forward(self, idx): + emd = self.embeddings(idx) + features = self.mlp(emd) + + weights = { + "conv1.weight": self.c1_weights(features).view(self.n_kernels, self.in_channels, 5, 5), + "conv1.bias": self.c1_bias(features).view(-1), + "conv2.weight": self.c2_weights(features).view(2 * self.n_kernels, self.n_kernels, 5, 5), + "conv2.bias": self.c2_bias(features).view(-1), + "fc1.weight": self.l1_weights(features).view(120, 2 * self.n_kernels * 5 * 5), + "fc1.bias": self.l1_bias(features).view(-1), + "fc2.weight": self.l2_weights(features).view(84, 120), + "fc2.bias": self.l2_bias(features).view(-1), + # "fc3.weight": self.l3_weights(features).view(self.out_dim, 84), + # "fc3.bias": self.l3_bias(features).view(-1), + } + return weights + + +class CNNTarget(nn.Module): + def __init__(self, in_channels=3, n_kernels=16, out_dim=10): + super().__init__() + + self.conv1 = nn.Conv2d(in_channels, n_kernels, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(n_kernels, 2 * n_kernels, 5) + self.fc1 = nn.Linear(2 * n_kernels * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + # self.fc3 = nn.Linear(84, out_dim) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(x.shape[0], -1) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + # x = self.fc3(x) + return x diff --git a/experiments/hetro/local_layers/node.py b/experiments/hetro/local_layers/node.py new file mode 100644 index 0000000..6c5c18e --- /dev/null +++ b/experiments/hetro/local_layers/node.py @@ -0,0 +1,41 @@ +from experiments.hetro.dataset import gen_random_loaders + + +class BaseNodesForLocal: + def __init__( + self, + data_name, + data_path, + n_nodes, + base_layer, + layer_config, + base_optimizer, + optimizer_config, + device, + batch_size=128, + classes_per_node=2, + ): + + self.data_name = data_name + self.data_path = data_path + self.n_nodes = n_nodes + self.classes_per_node = classes_per_node + self.batch_size = batch_size + + self.local_layers = [ + base_layer(**layer_config).to(device) for _ in range(self.n_nodes) + ] + self.local_optimizers = [ + base_optimizer(self.local_layers[i].parameters(), **optimizer_config) for i in range(self.n_nodes) + ] + + self.train_loaders, self.val_loaders, self.test_loaders = gen_random_loaders( + self.data_name, + self.data_path, + self.n_nodes, + self.batch_size, + self.classes_per_node + ) + + def __len__(self): + return self.n_nodes diff --git a/experiments/hetro/local_layers/trainer.py b/experiments/hetro/local_layers/trainer.py new file mode 100644 index 0000000..f508282 --- /dev/null +++ b/experiments/hetro/local_layers/trainer.py @@ -0,0 +1,322 @@ +import argparse +import logging +import random +import json +from pathlib import Path +from collections import defaultdict +import wandb + +import numpy as np +import torch +import torch.utils.data +from tqdm import trange + +from experiments.lookahead import Lookahead +from experiments.utils import common_parser + +from experiments.hetro.local_layers.models import CNNHyper, CNNTarget, LocalLayer +from experiments.hetro.local_layers.node import BaseNodesForLocal +from experiments.utils import set_seed, set_logger, get_device, str2bool + + +def eval_model(nodes, num_nodes, hnet, net, criteria, device, split): + curr_results = evaluate(nodes, num_nodes, hnet, net, criteria, device, split=split) + total_correct = sum([val['correct'] for val in curr_results.values()]) + total_samples = sum([val['total'] for val in curr_results.values()]) + avg_loss = np.mean([val['loss'] for val in curr_results.values()]) + avg_acc = total_correct / total_samples + + all_acc = [val['correct'] / val['total'] for val in curr_results.values()] + + return curr_results, avg_loss, avg_acc, all_acc + + +@torch.no_grad() +def evaluate(nodes: BaseNodesForLocal, num_nodes, hnet, net, criteria, device, split='test'): + hnet.eval() + results = defaultdict(lambda: defaultdict(list)) + + for node_id in range(num_nodes): # iterating over nodes + + running_loss, running_correct, running_samples = 0., 0., 0. + if split == 'test': + curr_data = nodes.test_loaders[node_id] + elif split == 'val': + curr_data = nodes.val_loaders[node_id] + else: + curr_data = nodes.train_loaders[node_id] + + weights = hnet(torch.tensor([node_id], dtype=torch.long).to(device)) + net.load_state_dict(weights) + + for batch_count, batch in enumerate(curr_data): + img, label = tuple(t.to(device) for t in batch) + net_out = net(img) + pred = nodes.local_layers[node_id](net_out) + running_loss += criteria(pred, label).item() + running_correct += pred.argmax(1).eq(label).sum().item() + running_samples += len(label) + + results[node_id]['loss'] = running_loss / (batch_count + 1) + results[node_id]['correct'] = running_correct + results[node_id]['total'] = running_samples + + return results + + +def train( + data_path: str, steps: int, data_name: int, optim: str, lr: float, n_kernels: int, bs: int, + device, eval_every: int, save_path: Path +) -> None: + + # -------------------------- + # Datasets + Subsets Indexes + # -------------------------- + num_nodes = args.num_nodes + + nodes = BaseNodesForLocal( + data_name=data_name, + data_path=data_path, + n_nodes=args.num_nodes, + base_layer=LocalLayer, + layer_config={'n_input': 84, 'n_output': 10 if data_name == 'cifar10' else 100}, + base_optimizer=torch.optim.SGD, optimizer_config=dict(lr=args.inner_lr, momentum=.9, weight_decay=args.la_wd), + device=device, + batch_size=bs, + classes_per_node=args.classes_per_node, + ) + + embed_dim = args.embed_dim + if embed_dim == -1: + logging.info("auto embedding size") + embed_dim = int(1 + num_nodes / 4) + + hnet = CNNHyper( + num_nodes, embed_dim, hidden_dim=args.hyper_hid, n_hidden=args.n_hidden, + n_kernels=n_kernels, spec_norm=args.spec_norm + ) + net = CNNTarget(n_kernels=n_kernels) + + hnet = hnet.to(device) + net = net.to(device) + + embed_lr = args.embed_lr if args.embed_lr is not None else lr + optimizers = { + 'sgd': torch.optim.SGD( + [ + {'params': [p for n, p in hnet.named_parameters() if 'embed' not in n]}, + {'params': [p for n, p in hnet.named_parameters() if 'embed' in n], 'lr': embed_lr} + ], lr=lr, momentum=0.9, weight_decay=args.wd + ), + 'adam': torch.optim.Adam(params=hnet.parameters(), lr=lr) + } + optimizer = optimizers[optim] + criteria = torch.nn.CrossEntropyLoss() + last_eval = -1 + best_step = -1 + best_acc = -1 + test_best_based_on_step, test_best_min_based_on_step = -1, -1 + test_best_max_based_on_step, test_best_std_based_on_step = -1, -1 + step_iter = trange(steps) + + results = defaultdict() + for step in step_iter: + hnet.train() + node_id = random.choice(range(num_nodes)) + + weights = hnet(torch.tensor([node_id], dtype=torch.long).to(device)) + net.load_state_dict(weights) + look_optim = Lookahead( + torch.optim.SGD( + net.parameters(), lr=args.inner_lr, momentum=.9, weight_decay=args.la_wd + ), + la_steps=args.la_steps, la_alpha=args.la_lr + ) + + # NOTE: evaluation on sent model + with torch.no_grad(): + net.eval() + batch = next(iter(nodes.test_loaders[node_id])) + img, label = tuple(t.to(device) for t in batch) + net_out = net(img) + pred = nodes.local_layers[node_id](net_out) + prvs_loss = criteria(pred, label) + prvs_acc = pred.argmax(1).eq(label).sum().item() / len(label) + net.train() + + for i in range(args.la_steps): + net.train() + look_optim.zero_grad() + optimizer.zero_grad() + nodes.local_optimizers[node_id].zero_grad() + + batch = next(iter(nodes.train_loaders[node_id])) + img, label = tuple(t.to(device) for t in batch) + + net_out = net(img) + pred = nodes.local_layers[node_id](net_out) + + loss = criteria(pred, label) + loss.backward() + torch.nn.utils.clip_grad_norm_(net.parameters(), 50) + _, grad_outputs = look_optim.step() + nodes.local_optimizers[node_id].step() + + optimizer.zero_grad() + + hnet_grads = torch.autograd.grad( + list(weights.values()), hnet.parameters(), grad_outputs=list(grad_outputs.values()) + ) + + for p, g in zip(hnet.parameters(), hnet_grads): + p.grad = g + + torch.nn.utils.clip_grad_norm_(hnet.parameters(), 50) + optimizer.step() + + step_iter.set_description( + f"Step: {step+1}, Node ID: {node_id}, Loss: {prvs_loss:.4f}, Acc: {prvs_acc:.4f}" + ) + + if step % eval_every == 0: + last_eval = step + step_results, avg_loss, avg_acc, all_acc = eval_model( + nodes, num_nodes, hnet, net, criteria, device, split="test" + ) + logging.info(f"\nStep: {step+1}, AVG Loss: {avg_loss:.4f}, AVG Acc: {avg_acc:.4f}") + results[step+1] = step_results + + if args.wandb: + wandb.log( + {'test_avg_loss': avg_loss, 'test_avg_acc': avg_acc}, + step=step + ) + + _, val_avg_loss, val_avg_acc, _ = eval_model(nodes, num_nodes, hnet, net, criteria, device, split="val") + if best_acc < val_avg_acc: + best_acc = val_avg_acc + best_step = step + test_best_based_on_step = avg_acc + test_best_min_based_on_step = np.min(all_acc) + test_best_max_based_on_step = np.max(all_acc) + test_best_std_based_on_step = np.std(all_acc) + + if args.wandb: + wandb.log( + { + 'val_avg_loss': val_avg_loss, 'val_avg_acc': val_avg_acc, + "best_step": best_step, "best_val_acc": best_acc, + 'best_test_acc_based_on_val_beststep': test_best_based_on_step, + 'test_best_min_based_on_step': test_best_min_based_on_step, + 'test_best_max_based_on_step': test_best_max_based_on_step, + 'test_best_std_based_on_step': test_best_std_based_on_step + }, + step=step + ) + + if step != last_eval: + _, val_avg_loss, val_avg_acc, _ = eval_model(nodes, num_nodes, hnet, net, criteria, device, split="val") + step_results, avg_loss, avg_acc, all_acc = eval_model(nodes, num_nodes, hnet, net, criteria, device, split="test") + logging.info(f"\nStep: {step + 1}, AVG Loss: {avg_loss:.4f}, AVG Acc: {avg_acc:.4f}") + results[step + 1] = step_results + if best_acc < val_avg_acc: + best_acc = val_avg_acc + best_step = step + test_best_based_on_step = avg_acc + test_best_min_based_on_step = np.min(all_acc) + test_best_max_based_on_step = np.max(all_acc) + test_best_std_based_on_step = np.std(all_acc) + + if args.wandb: + wandb.log( + { + 'test_avg_loss': avg_loss, 'test_avg_acc': avg_acc, + "val_avg_loss": val_avg_loss, "val_avg_acc": val_avg_acc, + "best_step": best_step, "best_val_acc": best_acc, + 'best_test_acc_based_on_val_beststep': test_best_based_on_step, + 'test_best_min_based_on_step': test_best_min_based_on_step, + 'test_best_max_based_on_step': test_best_max_based_on_step, + 'test_best_std_based_on_step': test_best_std_based_on_step + }, + step=step + ) + + save_path = Path(save_path) + save_path.mkdir(parents=True, exist_ok=True) + with open(str(save_path / "results.json"), "w") as file: + json.dump(results, file, indent=4) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description="Federated Hypernetwork with local layers experiment", parents=[common_parser] + ) + + ############################# + # Dataset Args # + ############################# + parser.add_argument( + "--data-name", type=str, default="cifar10", choices=['cifar10', 'cifar100'], help="data name" + ) + parser.add_argument("--data-path", type=str, default='/cortex/data/images', help='data path') + parser.add_argument("--num-nodes", type=int, default=50) + parser.add_argument("--classes-per-node", type=int, default=2) + + ################################## + # Optimization args # + ################################## + parser.add_argument("--num-steps", type=int, default=5000) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--la-steps", type=int, default=50, help="lookahead steps") + parser.add_argument("--optim", type=str, default='sgd', choices=['adam', 'sgd'], help="learning rate") + + ################################ + # Model Prop args # + ################################ + parser.add_argument("--n-hidden", type=int, default=3, help="num. hidden layers") + parser.add_argument("--inner-lr", type=float, default=5e-3, help="learning rate for inner optimizer") + parser.add_argument("--la-lr", type=float, default=1e-2, help="lookahead learning rate") + parser.add_argument("--lr", type=float, default=1e-2, help="learning rate") + parser.add_argument("--wd", type=float, default=1e-3, help="weight decay") + parser.add_argument("--la-wd", type=float, default=5e-5, help="lookahead weight decay") + parser.add_argument("--embed-dim", type=int, default=-1, help="embedding dim") + parser.add_argument("--embed-lr", type=float, default=None, help="embedding learning rate") + parser.add_argument("--hyper-hid", type=int, default=100, help="hypernet hidden dim") + parser.add_argument("--spec-norm", type=str2bool, default=False, help="hypernet hidden dim") + parser.add_argument("--nkernels", type=int, default=16, help="number of kernels for cnn model") + + ############################# + # General args # + ############################# + parser.add_argument("--gpu", type=int, default=0, help="gpu device ID") + parser.add_argument("--eval-every", type=int, default=10, help="eval every X selected epochs") + parser.add_argument("--save-path", type=str, default="cifar_local_layers", help="dir path for output file") + parser.add_argument("--seed", type=int, default=42, help="seed value") + + args = parser.parse_args() + assert args.gpu <= torch.cuda.device_count(), f"--gpu flag should be in range [0,{torch.cuda.device_count() - 1}]" + + set_logger() + set_seed(args.seed) + + device = get_device(gpus=args.gpu) + + if args.wandb: + name = f"local_layer_{args.data_name}_lr_{args.lr}_inlr_{args.inner_lr}_embedlr_{args.embed_lr}" \ + f"_la_steps_{args.la_steps}_seed_{args.seed}" \ + f"_kernels_{args.nkernels}" + wandb.init(project="fhn", entity='ax2', name=name) + wandb.config.update(args) + + train( + data_path=args.data_path, + data_name=args.data_name, + steps=args.num_steps, + optim=args.optim, + lr=args.lr, + n_kernels=args.nkernels, + bs=args.batch_size, + device=device, + eval_every=args.eval_every, + save_path=args.save_path + ) diff --git a/experiments/hetro/trainer.py b/experiments/hetro/trainer.py index eecbba3..22027fa 100644 --- a/experiments/hetro/trainer.py +++ b/experiments/hetro/trainer.py @@ -15,7 +15,7 @@ from experiments.lookahead import Lookahead from experiments.utils import common_parser -from experiments.hetro.models import DNNHyper, DNNTargetLook, CNNHyper, CNNTargetLook +from experiments.hetro.models import CNNHyper, CNNTargetLook from experiments.node import BaseNodes from experiments.utils import set_seed, set_logger, get_device, str2bool @@ -78,10 +78,7 @@ def train(data_name: str, data_path: str, num_nodes: int, steps: int, optim: str logging.info("auto embedding size") embed_dim = int(1 + num_nodes / 4) - if data_name == "mnist": - hnet = DNNHyper(num_nodes, embedding_dim=embed_dim, hidden_dim=args.hyper_hid) - net = DNNTargetLook() - elif data_name == "cifar10": + if data_name == "cifar10": hnet = CNNHyper(num_nodes, embed_dim, hidden_dim=args.hyper_hid, n_hidden=args.n_hidden, n_kernels=n_kernels) net = CNNTargetLook(n_kernels=n_kernels) elif data_name == "cifar100": @@ -127,7 +124,12 @@ def train(data_name: str, data_path: str, num_nodes: int, steps: int, optim: str la_steps=args.la_steps, la_alpha=args.la_lr ) - # NOTE: evaluation on sent model + # look_optim = torch.optim.SGD( + # net.parameters(), lr=args.inner_lr, momentum=.9, weight_decay=args.la_wd + # ) + # theta = [p.detach().clone() for p in net.parameters()] + + # NOTE: evaluation on sent model with torch.no_grad(): net.eval() batch = next(iter(nodes.test_loaders[node_id])) @@ -152,12 +154,16 @@ def train(data_name: str, data_path: str, num_nodes: int, steps: int, optim: str loss.backward() torch.nn.utils.clip_grad_norm_(net.parameters(), 50) _, grad_outputs = look_optim.step() + # look_optim.step() optimizer.zero_grad() hnet_grads = torch.autograd.grad( list(weights.values()), hnet.parameters(), grad_outputs=list(grad_outputs.values()) ) + # delta_theta = [p_i - p for p, p_i in zip(net.parameters(), theta) if p.requires_grad] + # hnet_grads = torch.autograd.grad( + # list(weights.values()), hnet.parameters(), grad_outputs=delta_theta for p, g in zip(hnet.parameters(), hnet_grads): p.grad = g @@ -246,7 +252,7 @@ def train(data_name: str, data_path: str, num_nodes: int, steps: int, optim: str ############################# parser.add_argument( - "--data-name", type=str, default="cifar10", choices=['mnist', 'cifar10', 'cifar100'], help="dir path for MNIST dataset" + "--data-name", type=str, default="cifar10", choices=['cifar10', 'cifar100'], help="dir path for MNIST dataset" ) parser.add_argument("--data-path", type=str, default="data", help="dir path for MNIST dataset") parser.add_argument("--classes_per_user", type=int, default=2, help="N classes assigned to each user") diff --git a/experiments/hetro/wandb/latest-run b/experiments/hetro/wandb/latest-run deleted file mode 120000 index 728fd14..0000000 --- a/experiments/hetro/wandb/latest-run +++ /dev/null @@ -1 +0,0 @@ -run-20210207_144309-rap277k8 \ No newline at end of file diff --git a/experiments/hetro/wandb/run-20210207_125224-5s6q46n4/files/config.yaml b/experiments/hetro/wandb/run-20210207_125224-5s6q46n4/files/config.yaml deleted file mode 100644 index 74607ef..0000000 --- a/experiments/hetro/wandb/run-20210207_125224-5s6q46n4/files/config.yaml +++ /dev/null @@ -1,115 +0,0 @@ -wandb_version: 1 - -_wandb: - desc: null - value: - cli_version: 0.10.12 - framework: torch - is_jupyter_run: false - is_kaggle_kernel: false - python_version: 3.7.5 -batch_size: - desc: null - value: 64 -classes_per_user: - desc: null - value: 2 -data-name: - desc: null - value: cifar10 -data-path: - desc: null - value: /cortex/data/images -data_name: - desc: null - value: cifar10 -data_path: - desc: null - value: /cortex/data/images -embed-dim: - desc: null - value: -1 -embed_dim: - desc: null - value: -1 -embed_lr: - desc: null - value: null -eval-every: - desc: null - value: 25 -eval_every: - desc: null - value: 25 -gpu: - desc: null - value: 0 -hyper-hid: - desc: null - value: 100 -hyper_hid: - desc: null - value: 100 -inner-lr: - desc: null - value: 0.005 -inner_lr: - desc: null - value: 0.005 -la-steps: - desc: null - value: 50 -la_lr: - desc: null - value: 0.01 -la_steps: - desc: null - value: 50 -la_wd: - desc: null - value: 5.0e-05 -lr: - desc: null - value: 0.01 -n-hidden: - desc: null - value: 3 -n_hidden: - desc: null - value: 3 -nkernels: - desc: null - value: 16 -num-nodes: - desc: null - value: 50 -num-steps: - desc: null - value: 5000 -num_nodes: - desc: null - value: 50 -num_steps: - desc: null - value: 5000 -optim: - desc: null - value: sgd -save_path: - desc: null - value: fhn_hetro -seed: - desc: null - value: 27 -spec_norm: - desc: null - value: false -split: - desc: null - value: test -wandb: - desc: null - value: true -wd: - desc: null - value: 0.001 diff --git a/experiments/hetro/wandb/run-20210207_125224-5s6q46n4/files/requirements.txt b/experiments/hetro/wandb/run-20210207_125224-5s6q46n4/files/requirements.txt deleted file mode 100644 index d1d24a5..0000000 --- a/experiments/hetro/wandb/run-20210207_125224-5s6q46n4/files/requirements.txt +++ /dev/null @@ -1,46 +0,0 @@ -cached-property==1.5.2 -certifi==2020.6.20 -chardet==4.0.0 -click==7.1.2 -configparser==5.0.1 -cycler==0.10.0 -dataclasses==0.6 -docker-pycreds==0.4.0 -fhn==1.0.0 -future==0.18.2 -gitdb==4.0.5 -gitpython==3.1.11 -h5py==3.1.0 -idna==2.10 -joblib==0.17.0 -kiwisolver==1.3.1 -matplotlib==3.3.2 -numpy==1.19.4 -pandas==1.2.1 -pillow==8.0.1 -pip==20.2.4 -promise==2.3 -protobuf==3.14.0 -psutil==5.8.0 -pyparsing==2.4.7 -python-dateutil==2.8.1 -pytz==2020.5 -pyyaml==5.3.1 -requests==2.25.1 -scikit-learn==0.23.2 -scipy==1.5.4 -sentry-sdk==0.19.5 -setuptools==50.3.1.post20201107 -shortuuid==1.0.1 -six==1.15.0 -smmap==3.0.4 -subprocess32==3.5.4 -threadpoolctl==2.1.0 -torch==1.7.0 -torchvision==0.8.1 -tqdm==4.51.0 -typing-extensions==3.7.4.3 -urllib3==1.26.2 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