Closed StevenL2017 closed 6 months ago
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;
ray 的版本是 2.2.0 autots 是执行这个命令 pip install paddlets[autots] 安装的
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;
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
.
请问解决这个问题了吗?我也遇到了同样的问题
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
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?
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
您好,超过一个月问题自动关闭。
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?
ray.tune.suggest
has been moved toray.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 replaceray.tune.suggest
withray.tune.search
. from ray.tune.suggest import BasicVariantGenerator C:\ProgramData\Anaconda3\lib\site-packages\paddlets\automl\searcher.py:5: DeprecationWarning: The moduleray.tune.suggest.optuna
has been moved toray.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 replaceray.tune.suggest.optuna
withray.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 toray.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 replaceray.tune.suggest.flaml
withray.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 toray.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 replaceray.tune.suggest.bohb
withray.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 moduleray.tune.sample
has been moved toray.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 replaceray.tune.sample
withray.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_14-21-02Number of trials: 1/20 (1 PENDING) +--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+
+--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+
Number of trials: 1/20 (1 PENDING) +--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+
+--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+
Number of trials: 1/20 (1 PENDING) +--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+
+--------------------+----------+-------+--------------+----------------------+--------------+------------------------+------------+----------+
运行环境
windows 11 vscode powershell paddlepaddle-gpu 2.4.1 paddlets 1.0.2