如何解决在 ray tune 中跨多个调用获取不同的配置集 等等，但是为什么
我正在努力使我的代码可重现。我已经添加了 np.random.seed(...) 和 random.seed(...)，目前我没有使用 pytorch 或 tf，因此没有调度程序或搜索程序可以引入任何随机问题。使用上述代码生成的配置集在多次调用中应该始终相同。然而，事实并非如此。
有人可以帮忙吗？
谢谢！
代码如下：
import ray
from ray import tune
import random
import numpy as np
def training_function(config,data_init):
print('CONfig:',config)
tune.report(end_of_training=1,acc=0,f=0)
if __name__ == '__main__':
ray.init(num_cpus=12)
tune_config = {'sentence_classification': False,'norm_word_emb': tune.choice(['True','False']),'use_crf': tune.choice(['True','use_char': tune.choice(['True','word_seq_feature': tune.choice(['CNN','LSTM','GRU']),'char_seq_feature': tune.choice(['CNN','seed_num': 1267}
data = {'a': 1}
tune_seed = tune_config['seed_num']
random.seed(tune_seed)
np.random.seed(tune_seed)
n_samples = 15
exp_name = 'experiment_name'
analysis = tune.run(
tune.with_parameters(training_function,data_init={'data': data}),name=exp_name,metric="f",mode="max",queue_trials=True,config=tune_config,num_samples=n_samples,resources_per_trial={"cpu": 1},checkpoint_at_end=True,max_failures=0,)
解决方法
函数级 API 无法重现（ray v1.1.0，可能会发生变化）。
等等，但是为什么
-
tune.run创建一个Experiment对象，将您的函数传递到那里。 -
Experimentregisters 可通过调用register_trainable 训练的函数
-
register_trainable使用wrap_function 包装您的函数
-
wrap_function将通过继承FunctionRunner类来创建类级 API（射线 Actor）。 -
FunctionRunner对setup方法没有任何回调访问权限。
Actor 的工作方式过于简单，它在工作人员之间分配，然后使用 setup 方法在不同的进程中初始化。这就是为什么在自定义 Trainable 中传递种子和实现初始化逻辑至关重要，如 this 答案中所述。需要播种是因为 tune.choice 只是 random/np.random 函数的包装器。您可以在 tune/sample.py 中观察到这一点。
看例子：
import ray
from ray import tune
import random
import numpy as np
class Tunable(tune.Trainable):
def setup(self,config):
self.config = config
self.seed = config['seed_num']
random.seed(self.seed)
np.random.seed(self.seed)
def step(self):
print('CONFIG:',self.config)
return {tune.result.DONE: 'done','acc': 0,'f': 0}
if __name__ == '__main__':
ray.init(num_cpus=12)
tune_config = {'sentence_classification': False,'norm_word_emb': tune.choice(['True','False']),'use_crf': tune.choice(['True','use_char': tune.choice(['True','word_seq_feature': tune.choice(['CNN','LSTM','GRU']),'char_seq_feature': tune.choice(['CNN','seed_num': 1267}
data = {'a': 1}
tune_seed = tune_config['seed_num']
n_samples = 15
exp_name = 'experiment_name'
analysis = tune.run(
Tunable,name=exp_name,metric="f",mode="max",queue_trials=True,config=tune_config,num_samples=n_samples,resources_per_trial={"cpu": 1},checkpoint_at_end=False,max_failures=0,)
我看到了播种工作的行为。我运行了这个脚本：
var sql = "INSERT into restaurant_reviews.restaurants(average_rating) SELECT AVG(rating) FROM restaurant_reviews.reviews INNER JOIN restaurant_reviews.restaurants ON restaurants_idrestaurant = idrestaurant
我跑过的地方：
import ray
from ray import tune
import numpy as np
import random
def training_function(config,data_init):
print('CONFIG:',config)
tune.report(end_of_training=1,acc=0,f=0)
if __name__ == '__main__':
# ray.init(num_cpus=12)
tune_config = {'sentence_classification': False,'seed': 1267}
data = {'a': 1}
tune_seed = tune_config['seed']
random.seed(tune_seed)
np.random.seed(tune_seed)
n_samples = 15
analysis = tune.run(
tune.with_parameters(training_function,data_init={'data': data}),#name=exp_name,verbose=2,)
以及随后的运行：
Resources requested: 0/16 CPUs,0/0 GPUs,0.0/27.0 GiB heap,0.0/9.28 GiB objects
Current best trial: 84b84_00014 with f=0 and parameters={'sentence_classification': False,'norm_word_emb': 'False','use_crf': 'True','use_char': 'False','word_seq_feature': 'LSTM','char_seq_feature': 'GRU','seed': 1267}
Number of trials: 15/15 (15 TERMINATED)
+--------------------+------------+-------+--------------------+-----------------+------------+-----------+--------------------+--------+------------------+-------------------+-------+-----+
| Trial name | status | loc | char_seq_feature | norm_word_emb | use_char | use_crf | word_seq_feature | iter | total time (s) | end_of_training | acc | f |
|--------------------+------------+-------+--------------------+-----------------+------------+-----------+--------------------+--------+------------------+-------------------+-------+-----|
| _inner_84b84_00000 | TERMINATED | | LSTM | True | False | False | LSTM | 1 | 0.00149202 | 1 | 0 | 0 |
| _inner_84b84_00001 | TERMINATED | | CNN | False | True | False | CNN | 1 | 0.0014801 | 1 | 0 | 0 |
| _inner_84b84_00002 | TERMINATED | | GRU | False | False | True | GRU | 1 | 0.00152397 | 1 | 0 | 0 |
| _inner_84b84_00003 | TERMINATED | | GRU | False | False | False | GRU | 1 | 0.00165081 | 1 | 0 | 0 |
| _inner_84b84_00004 | TERMINATED | | CNN | False | False | False | CNN | 1 | 0.00173998 | 1 | 0 | 0 |
| _inner_84b84_00005 | TERMINATED | | LSTM | True | True | True | CNN | 1 | 0.00219083 | 1 | 0 | 0 |
| _inner_84b84_00006 | TERMINATED | | GRU | True | False | False | LSTM | 1 | 0.00192428 | 1 | 0 | 0 |
| _inner_84b84_00007 | TERMINATED | | LSTM | True | False | False | CNN | 1 | 0.00208902 | 1 | 0 | 0 |
| _inner_84b84_00008 | TERMINATED | | LSTM | True | True | True | GRU | 1 | 0.00146484 | 1 | 0 | 0 |
| _inner_84b84_00009 | TERMINATED | | CNN | False | False | True | CNN | 1 | 0.00152087 | 1 | 0 | 0 |
| _inner_84b84_00010 | TERMINATED | | LSTM | False | True | False | CNN | 1 | 0.00124121 | 1 | 0 | 0 |
| _inner_84b84_00011 | TERMINATED | | LSTM | True | True | True | CNN | 1 | 0.00124812 | 1 | 0 | 0 |
| _inner_84b84_00012 | TERMINATED | | LSTM | True | True | True | LSTM | 1 | 0.00133514 | 1 | 0 | 0 |
| _inner_84b84_00013 | TERMINATED | | LSTM | True | False | True | CNN | 1 | 0.00142407 | 1 | 0 | 0 |
| _inner_84b84_00014 | TERMINATED | | GRU | False | False | True | LSTM | 1 | 0.00120211 | 1 | 0 | 0 |
+--------------------+------------+-------+--------------------+-----------------+------------+-----------+--------------------+--------+------------------+-------------------+-------+-----+
请注意，试验和它们的配置完全相同（以相同的顺序）。
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