我正在尝试 wandb 库并运行wandb.watch，但这似乎不适用于我的代码。它不应该是任何复杂的东西，所以我很困惑为什么它不起作用。
代码：
"""
https://docs.wandb.ai/guides/track/advanced/distributed-training
import wandb
# 1. Start a new run
wandb.init(project='playground', entity='brando')
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
# 3. Log gradients and model parameters
wandb.watch(model)
for batch_idx, (data, target) in enumerate(train_loader):
...
if batch_idx % args.log_interval == 0:
# 4. Log metrics to visualize performance
wandb.log({"loss": loss})
Notes:
- call wandb.init and wandb.log only from the leader process
"""
from argparse import Namespace
from pathlib import Path
from typing import Union
import torch
from torch import nn
from torch.nn.functional import mse_loss
from torch.optim import Optimizer
import uutils
from uutils.torch_uu import r2_score_from_torch
from uutils.torch_uu.distributed import is_lead_worker
from uutils.torch_uu.models import get_simple_model
from uutils.torch_uu.tensorboard import log_2_tb_supervisedlearning
import wandb
def log_2_wandb_nice(it, loss, inputs, outputs, captions):
wandb.log({"loss": loss, "epoch": it,
"inputs": wandb.Image(inputs),
"logits": wandb.Histogram(outputs),
"captions": wandb.HTML(captions)})
def log_2_wandb(**metrics):
""" Log to wandb """
new_metrics: dict = {}
for key, value in metrics.items():
key = str(key).strip('_')
new_metrics[key] = value
wandb.log(new_metrics)
def log_train_val_stats(args: Namespace,
it: int,
train_loss: float,
train_acc: float,
valid,
log_freq: int = 10,
ckpt_freq: int = 50,
force_log: bool = False, # e.g. at the final it/epoch
save_val_ckpt: bool = False,
log_to_tb: bool = False,
log_to_wandb: bool = False
):
"""
log train and val stats.
Note: Unlike save ckpt, this one does need it to be passed explicitly (so it can save it in the stats collector).
"""
from uutils.torch_uu.tensorboard import log_2_tb
from matplotlib import pyplot as plt
# - is it epoch or iteration
it_or_epoch: str = 'epoch_num' if args.training_mode == 'epochs' else 'it'
# if its
total_its: int = args.num_empochs if args.training_mode == 'epochs' else args.num_its
print(f'-- {it == total_its - 1}')
print(f'-- {it}')
print(f'-- {total_its}')
if (it % log_freq == 0 or is_lead_worker(args.rank) or it == total_its - 1 or force_log) and is_lead_worker(args.rank):
print('inside log')
# - get eval stats
val_loss, val_acc = valid(args, args.mdl, save_val_ckpt=save_val_ckpt)
# - print
args.logger.log('\n')
args.logger.log(f"{it_or_epoch}={it}: {train_loss=}, {train_acc=}")
args.logger.log(f"{it_or_epoch}={it}: {val_loss=}, {val_acc=}")
# - record into stats collector
args.logger.record_train_stats_stats_collector(it, train_loss, train_acc)
args.logger.record_val_stats_stats_collector(it, val_loss, val_acc)
args.logger.save_experiment_stats_to_json_file()
fig = args.logger.save_current_plots_and_stats()
# - log to wandb
if log_to_wandb:
# if it == 0:
# # -- todo why isn't this working?
# wandb.watch(args.mdl)
# print('watching model')
# log_2_wandb(train_loss=train_loss, train_acc=train_acc)
print('inside wandb log')
wandb.log(data={'train loss': train_loss, 'train acc': train_acc, 'val loss': val_loss, 'val acc': val_acc}, step=it)
wandb.log(data={'it': it}, step=it)
if it == total_its - 1:
print(f'logging fig at {it=}')
wandb.log(data={'fig': fig}, step=it)
plt.close('all')
# - log to tensorboard
if log_to_tb:
log_2_tb_supervisedlearning(args.tb, args, it, train_loss, train_acc, 'train')
log_2_tb_supervisedlearning(args.tb, args, it, train_loss, train_acc, 'val')
# log_2_tb(args, it, val_loss, val_acc, 'train')
# log_2_tb(args, it, val_loss, val_acc, 'val')
# - log ckpt
if (it % ckpt_freq == 0 or it == total_its - 1 or force_log) and is_lead_worker(args.rank):
save_ckpt(args, args.mdl, args.optimizer)
def save_ckpt(args: Namespace, mdl: nn.Module, optimizer: torch.optim.Optimizer,
dirname: Union[None, Path] = None, ckpt_name: str = 'ckpt.pt'):
"""
Saves checkpoint for any worker.
Intended use is to save by worker that got a val loss that improved.
"""
import dill
dirname = args.log_root if (dirname is None) else dirname
# - pickle ckpt
assert uutils.xor(args.training_mode == 'epochs', args.training_mode == 'iterations')
pickable_args = uutils.make_args_pickable(args)
torch.save({'state_dict': mdl.state_dict(),
'epoch_num': args.epoch_num,
'it': args.it,
'optimizer': optimizer.state_dict(),
'args': pickable_args,
'mdl': mdl},
pickle_module=dill,
f=dirname / ckpt_name) # f'mdl_{epoch_num:03}.pt'
def get_args() -> Namespace:
args = uutils.parse_args_synth_agent()
# we can place model here...
args = uutils.setup_args_for_experiment(args)
return args
def valid_for_test(args: Namespace, mdl: nn.Module, save_val_ckpt: bool = False):
import torch
for t in range(1):
x = torch.randn(args.batch_size, 5)
y = (x ** 2 + x + 1).sum(dim=1)
y_pred = mdl(x).squeeze(dim=1)
val_loss, val_acc = mse_loss(y_pred, y), r2_score_from_torch(y_true=y, y_pred=y_pred)
if val_loss.item() < args.best_val_loss and save_val_ckpt:
args.best_val_loss = val_loss.item()
save_ckpt(args, args.mdl, args.optimizer, ckpt_name='ckpt_best_val.pt')
return val_loss, val_acc
def train_for_test(args: Namespace, mdl: nn.Module, optimizer: Optimizer, scheduler=None):
# wandb.watch(args.mdl)
for it in range(args.num_its):
x = torch.randn(args.batch_size, 5)
y = (x ** 2 + x + 1).sum(dim=1)
y_pred = mdl(x).squeeze(dim=1)
train_loss, train_acc = mse_loss(y_pred, y), r2_score_from_torch(y_true=y, y_pred=y_pred)
optimizer.zero_grad()
train_loss.backward() # each process synchronizes it's gradients in the backward pass
optimizer.step() # the right update is done since all procs have the right synced grads
scheduler.step()
log_train_val_stats(args, it, train_loss, train_acc, valid_for_test,
log_freq=2, ckpt_freq=10,
save_val_ckpt=True, log_to_tb=True, log_to_wandb=True)
return train_loss, train_acc
def debug_test():
args: Namespace = get_args()
args.num_its = 12
# - get mdl, opt, scheduler, etc
args.mdl = get_simple_model(in_features=5, hidden_features=20, out_features=1, num_layer=2)
wandb.watch(args.mdl)
args.optimizer = torch.optim.Adam(args.mdl.parameters(), lr=1e-1)
args.scheduler = torch.optim.lr_scheduler.ExponentialLR(args.optimizer, gamma=0.999, verbose=False)
# - train
train_loss, train_acc = train_for_test(args, args.mdl, args.optimizer, args.scheduler)
print(f'{train_loss=}, {train_loss=}')
# - eval
val_loss, val_acc = valid_for_test(args, args.mdl)
print(f'{val_loss=}, {val_acc=}')
# - make sure wandb closes properly
if args.log_to_wandb:
wandb.finish()
if __name__ == '__main__':
import os
# print(os.environ['WANDB_API_KEY'])
import time
start = time.time()
debug_test()
duration_secs = time.time() - start
print(f"\nSuccess, time passed: hours:{duration_secs / (60 ** 2)}, minutes={duration_secs / 60}, seconds={duration_secs}")
print('Done!\a')