MLFLOW：无法将工件存储到S3
我正在远程服务器上的Docker容器中运行我的MLFLOW跟踪服务器，并尝试将MLFlow从本地计算机运行，最终的目标是我团队中的任何人都可以将其运行数据发送到同一跟踪服务器。我将跟踪URI设置为http：//＆lt; ip of Remote Server＆gt;：＆lt; docker容器上的端口＆gt;。我没有明确设置本地计算机上的任何AWS凭据，因为我希望能够在本地训练并登录到远程服务器（将数据运行到RDS和伪像到S3）。我在记录RDS数据库的运行时没有问题，但是当尝试记录工件时，我会遇到以下错误：botocore.exceptions.nocredentialserror：无法找到凭证。我是否必须在跟踪服务器之外使用凭据才能工作（即：在发生MLFlow运行的本地计算机上）？我知道我的所有凭据都可以在托管跟踪服务器的Docker容器中可用。我可以使用托管我的跟踪服务器的容器中的AWS CLI将文件上传到我的S3存储桶中，以便我知道它是访问权限。我可以将可以登录到RD而不是S3的事实感到困惑。我不确定我在这一点上做错了什么。 tia。
I'm running my mlflow tracking server in a docker container on a remote server and trying to log mlflow runs from local computer with the eventual goal that anyone on my team can send their run data to the same tracking server. I've set the tracking URI to be http://<ip of remote server >:<port on docker container>. I'm not explicitly setting any of the AWS credentials on the local machine because I would like to just be able to train locally and log to the remote server (run data to RDS and artifacts to S3). I have no problem logging my runs to an RDS database but I keep getting the following error when it get to the point of trying to log artifacts: botocore.exceptions.NoCredentialsError: Unable to locate credentials. Do I have to have the credentials available outside of the tracking server for this to work (ie: on my local machine where the mlflow runs are taking place)? I know that all of my credentials are available in the docker container that is hosting the tracking server. I've be able to upload files to my S3 bucket using the aws cli inside of the container that hosts my tracking server so I know that it as access. I'm confused by the fact that I can log to RDS but not S3. I'm not sure what I'm doing wrong at this point. TIA.
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是的，显然我也需要向本地客户端提供凭证。
Yes, apparently I do need to have the credentials available to the local client as well.