Reputation: 442
I am trying to get MLFlow on another machine in a local network to run and I would like to ask for some help because I don't know what to do now.
I have a mlflow server running on a server. The mlflow server is running under my user on the server and has been started like this:
mlflow server --host 0.0.0.0 --port 9999 --default-artifact-root sftp://<MYUSERNAME>@<SERVER>:<PATH/TO/DIRECTORY/WHICH/EXISTS>
My program which should log all the data to the mlflow server looks like this:
from mlflow import log_metric, log_param, log_artifact, set_tracking_uri
if __name__ == "__main__":
remote_server_uri = '<SERVER>' # this value has been replaced
set_tracking_uri(remote_server_uri)
# Log a parameter (key-value pair)
log_param("param1", 5)
# Log a metric; metrics can be updated throughout the run
log_metric("foo", 1)
log_metric("foo", 2)
log_metric("foo", 3)
# Log an artifact (output file)
with open("output.txt", "w") as f:
f.write("Hello world!")
log_artifact("output.txt")
The parameters get and metrics get transfered to the server but not the artifacts. Why is that so?
Note on the SFTP part: I can log in via SFTP and the pysftp package is installed
Upvotes: 9
Views: 6821
Reputation: 179
I guess your problem is that you need to create also the experiment so using the sftp remote storage
mlflow.create_experiment("my_experiment", artifact_location=sftp_uri)
This fixed it for me.
Upvotes: 4
Reputation: 442
I don't know if I will get an answer to my problem but I did solved it this way.
On the server I created the directory /var/mlruns
. I pass this directory to mlflow via --backend-store-uri file:///var/mlruns
Then I mount this directory via e.g. sshfs
on my local machine under the same path.
I don't like this solution but it solved the problem good enough for now.
Upvotes: 2