{"action":"opened","issue":{"url":"https://api.github.com/repos/databricks/mlflow/issues/158","repository_url":"https://api.github.com/repos/databricks/mlflow","labels_url":"https://api.github.com/repos/databricks/mlflow/issues/158/labels{/name}","comments_url":"https://api.github.com/repos/databricks/mlflow/issues/158/comments","events_url":"https://api.github.com/repos/databricks/mlflow/issues/158/events","html_url":"https://github.com/databricks/mlflow/issues/158","id":341643701,"node_id":"MDU6SXNzdWUzNDE2NDM3MDE=","number":158,"title":"Cannot connect to S3","user":{"login":"abaveja313","id":10778111,"node_id":"MDQ6VXNlcjEwNzc4MTEx","avatar_url":"https://avatars3.githubusercontent.com/u/10778111?v=4","gravatar_id":"","url":"https://api.github.com/users/abaveja313","html_url":"https://github.com/abaveja313","followers_url":"https://api.github.com/users/abaveja313/followers","following_url":"https://api.github.com/users/abaveja313/following{/other_user}","gists_url":"https://api.github.com/users/abaveja313/gists{/gist_id}","starred_url":"https://api.github.com/users/abaveja313/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/abaveja313/subscriptions","organizations_url":"https://api.github.com/users/abaveja313/orgs","repos_url":"https://api.github.com/users/abaveja313/repos","events_url":"https://api.github.com/users/abaveja313/events{/privacy}","received_events_url":"https://api.github.com/users/abaveja313/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-07-16T19:16:39Z","updated_at":"2018-07-16T19:16:39Z","closed_at":null,"author_association":"NONE","body":"### System information\r\n- **Have I written custom code (as opposed to using a stock example script provided in MLflow)**: no\r\n- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Mac OS Sierra\r\n- **MLflow installed from (source or binary)**: pip\r\n- **MLflow version (run ``mlflow --version``)**: 0.2.1\r\n- **Python version**: 3.6\r\n- **npm version (if running the dev UI): \r\n- **Exact command to reproduce**: mlflow server --host 0.0.0.0 --artifact-root s3://<my s3>\r\n\r\n### Describe the problem\r\nHi. I am using MLFlow and would like to store all of my model artifacts in S3. However, when I try to execute the above command, it isn't communicating with S3. I think that it is still trying to create a local directory (in my tmp), using this name. When I do `mlflow experiments list`, I get:\r\n```\r\n  Experiment Id  Name     Artifact Location\r\n---------------  -------  -------------------------------\r\n              0  Default  /private/tmp/s3:/amrit-splice/0\r\n```\r\n"}}
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