AutoTS报错和运行异常

[StevenL2017]

执行代码

官方示例

from paddlets.datasets.repository import get_dataset tsdataset = get_dataset("UNI_WTH") from paddlets.models.forecasting import MLPRegressor from paddlets.automl.autots import AutoTS autots_model = AutoTS(MLPRegressor, 96, 2) autots_model.fit(tsdataset)

运行得到输出如下

加粗部分为报错，然后程序不终止，一直不断打印==Status==之后的内容，Number of trials也不变，这个是什么原因呢？

C:\ProgramData\Anaconda3\lib\site-packages\paddlets\automl\searcher.py:4: DeprecationWarning: The module ray.tune.suggest has been moved to ray.tune.search and the old location will be deprecated soon. Please adjust your imports to point to the new location. Example: Do a global search and replace ray.tune.suggest with ray.tune.search. from ray.tune.suggest import BasicVariantGenerator C:\ProgramData\Anaconda3\lib\site-packages\paddlets\automl\searcher.py:5: DeprecationWarning: The module ray.tune.suggest.optuna has been moved to ray.tune.search.optuna and the old location will be deprecated soon. Please adjust your imports to point to the new location. Example: Do a global search and replace ray.tune.suggest.optuna with ray.tune.search.optuna. from ray.tune.suggest.optuna import OptunaSearch C:\ProgramData\Anaconda3\lib\site-packages\paddlets\automl\searcher.py:6: DeprecationWarning: The module ray.tune.suggest.flaml has been moved to ray.tune.search.flaml and the old location will be deprecated soon. Please adjust your imports to point to the new location. Example: Do a global search and replace ray.tune.suggest.flaml with ray.tune.search.flaml. from ray.tune.suggest.flaml import CFO C:\ProgramData\Anaconda3\lib\site-packages\paddlets\automl\searcher.py:8: DeprecationWarning: The module ray.tune.suggest.bohb has been moved to ray.tune.search.bohb and the old location will be deprecated soon. Please adjust your imports to point to the new location. Example: Do a global search and replace ray.tune.suggest.bohb with ray.tune.search.bohb. from ray.tune.suggest.bohb import TuneBOHB C:\ProgramData\Anaconda3\lib\site-packages\paddlets\automl\search_space_configer.py:8: DeprecationWarning: The module ray.tune.sample has been moved to ray.tune.search.sample and the old location will be deprecated soon. Please adjust your imports to point to the new location. Example: Do a global search and replace ray.tune.sample with ray.tune.search.sample. from ray.tune.sample import Float, Integer, Categorical 2022-12-27 14:21:00,844 INFO worker.py:1538 -- Started a local Ray instance. C:\ProgramData\Anaconda3\lib\site-packages\ray\tune\search\optuna\optuna_search.py:694: FutureWarning: IntUniformDistribution has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use :class:~optuna.distributions.IntDistribution instead. return ot.distributions.IntUniformDistribution( C:\ProgramData\Anaconda3\lib\site-packages\ray\tune\search\optuna\optuna_search.py:682: FutureWarning: UniformDistribution has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use :class:~optuna.distributions.FloatDistribution instead. return ot.distributions.UniformDistribution( [I 2022-12-27 14:21:02,351] A new study created in memory with name: optuna C:\ProgramData\Anaconda3\lib\site-packages\optuna\distributions.py:766: FutureWarning: IntUniformDistribution(high=128, low=8, step=8) is deprecated and internally converted to IntDistribution(high=128, log=False, low=8, step=8). See https://github.com/optuna/optuna/issues/2941. warnings.warn(message, FutureWarning) C:\ProgramData\Anaconda3\lib\site-packages\optuna\distributions.py:766: FutureWarning: IntUniformDistribution(high=600, low=30, step=30) is deprecated and internally converted to IntDistribution(high=600, log=False, low=30, step=30). See https://github.com/optuna/optuna/issues/2941. warnings.warn(message, FutureWarning) C:\ProgramData\Anaconda3\lib\site-packages\optuna\distributions.py:766: FutureWarning: IntUniformDistribution(high=50, low=5, step=5) is deprecated and internally converted to IntDistribution(high=50, log=False, low=5, step=5). See https://github.com/optuna/optuna/issues/2941. warnings.warn(message, FutureWarning) C:\ProgramData\Anaconda3\lib\site-packages\optuna\distributions.py:766: FutureWarning: UniformDistribution(high=0.01, low=0.0001) is deprecated and internally converted to FloatDistribution(high=0.01, log=False, low=0.0001, step=None). See https://github.com/optuna/optuna/issues/2941. warnings.warn(message, FutureWarning) == Status == Current time: 2022-12-27 14:21:07 (running for 00:00:05.18) Memory usage on this node: 12.3/31.7 GiB Using FIFO scheduling algorithm. Resources requested: 0/20 CPUs, 0/1 GPUs, 0.0/12.44 GiB heap, 0.0/6.22 GiB objects (0.0/1.0 accelerator_type:G) Result logdir: C:\Users\Steven\ray_results\run_trial_2022-12-27_14-21-02 Number of trials: 1/20 (1 PENDING) +--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+	Trial name	status	loc	batch_size	hidden_config	max_epochs	optimizer_params/lea	patience	use_bn
	rning_rate
--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------
run_trial_41b3f5c7	PENDING		104	Choice_2: [64, _66c0	300	0.00969596	45	False

+--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+

2022-12-27 14:21:12,864 WARNING worker.py:1851 -- This worker was asked to execute a function that has not been registered ({type=PythonFunctionDescriptor, module_name=ray.util.placement_group, class_name=, function_name=_export_bundle_reservation_check_method_if_needed..bundle_reservation_check_func, function_hash=c0f5d1653dc84f95bbeed36cb66471cd}, node=127.0.0.1, worker_id=e226e520eafc567ab1ccf4e297e0559f5116718dbeda073aa17dee4a, pid=9048). You may have to restart Ray. (pid=9048) 2022-12-27 14:21:12,863 ERROR function_manager.py:415 -- This worker was asked to execute a function that has not been registered ({type=PythonFunctionDescriptor, module_name=ray.util.placement_group, class_name=, function_name=_export_bundle_reservation_check_method_if_needed..bundle_reservation_check_func, function_hash=c0f5d1653dc84f95bbeed36cb66471cd}, node=127.0.0.1, worker_id=e226e520eafc567ab1ccf4e297e0559f5116718dbeda073aa17dee4a, pid=9048). You may have to restart Ray. == Status == Current time: 2022-12-27 14:21:12 (running for 00:00:10.23) Memory usage on this node: 12.3/31.7 GiB Using FIFO scheduling algorithm. Resources requested: 0/20 CPUs, 0/1 GPUs, 0.0/12.44 GiB heap, 0.0/6.22 GiB objects (0.0/1.0 accelerator_type:G) Result logdir: C:\Users\Steven\ray_results\run_trial_2022-12-27_14-21-02 Number of trials: 1/20 (1 PENDING) +--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+	Trial name	status	loc	batch_size	hidden_config	max_epochs	optimizer_params/lea	patience	use_bn
	rning_rate
--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------
run_trial_41b3f5c7	PENDING		104	Choice_2: [64, _66c0	300	0.00969596	45	False

+--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+

== Status == Current time: 2022-12-27 14:21:18 (running for 00:00:15.30) Memory usage on this node: 12.3/31.7 GiB Using FIFO scheduling algorithm. Resources requested: 0/20 CPUs, 0/1 GPUs, 0.0/12.44 GiB heap, 0.0/6.22 GiB objects (0.0/1.0 accelerator_type:G) Result logdir: C:\Users\Steven\ray_results\run_trial_2022-12-27_14-21-02 Number of trials: 1/20 (1 PENDING) +--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+	Trial name	status	loc	batch_size	hidden_config	max_epochs	optimizer_params/lea	patience	use_bn
	rning_rate
--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------
run_trial_41b3f5c7	PENDING		104	Choice_2: [64, _66c0	300	0.00969596	45	False

+--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+

运行环境

windows 11 vscode powershell paddlepaddle-gpu 2.4.1 paddlets 1.0.2

[LinWencong]

请检查一下 ray 的版本，是否为1.13.0或以上版本； ray的 issue 能看到类似的问题修复；解决环境问题成本应该最小，可以考虑重装 autots 的依赖；

[StevenL2017]

ray 的版本是 2.2.0 autots 是执行这个命令 pip install paddlets[autots] 安装的

[LinWencong]

建议可以尝试，pip install -U ray ray[tune] ray[defaults] optuna，升级一下相关依赖；考虑还是依赖冲突导致；

[mendjks]

from paddlets.automl.autots import AutoTS from paddlets.models.forecasting import MLPRegressor from paddlets.datasets.repository import get_dataset tsdataset = get_dataset("UNI_WTH") 请教一下使用paddleTS手册autots例子中的上面四行代码出现下面报错是什么原因 DeprecationWarning: The module ray.tune.suggest.optuna has been moved to ray.tune.search.optuna and the old location has been deprecated. Please adjust your imports to point to the new location. Example: Do a global search and replace ray.tune.suggest.optuna with ray.tune.search.optuna.

[huayuocean]

请问解决这个问题了吗？我也遇到了同样的问题

[akari0216]

paddlets的autots依赖ray这个包，然后ray.suggest弃用了，取而代之的是ray.search 手动将paddlets/automl下的： search.py中的第4~8行以及第11行的的ray.tune.suggest改为ray.tune.search search_configure_space.py下的第8行和第9行的ray.tune.sample改为ray.tune.search.sample optimize_runner.py下的第12行的ray.tune.sample改为ray.tune.search.sample 修改完并保存后就不会再出现DeprecationWarning了

[wazjajl]

paddlets的autots依赖ray这个包，然后ray.suggest弃用了，取而代之的是ray.search 手动将paddlets/automl下的： search.py中的第4~8行以及第11行的的ray.tune.suggest改为ray.tune.search search_configure_space.py下的第8行和第9行的ray.tune.sample改为ray.tune.search.sample optimize_runner.py下的第12行的ray.tune.sample改为ray.tune.search.sample 修改完并保存后就不会再出现DeprecationWarning了

已经看到main分支中的代码已经改掉了，但是通过pip安装的包中的代码还没有改（最新版1.1.0），这个是不是只能等到pip包的下一个版本才会更新掉了？

[jiaohuix]

安装了"ray[tune]"==2.6.3 ，报错：The actor ImplicitFunc is too large error ，这个怎么办，paddlets=1.1.0 @akari0216

[Sunting78]

您好，超过一个月问题自动关闭。