Troubleshooting

Here are few things that can go wrong:

• When creating the groups variable, you used a different upper case for specifying the well columns, but the load_data CSV uses lower case (i.e. A01 vs a01). You won't be able to figure out this problem from the Fiji error log, so make sure that the groups variable matches the load_data exactly.

• The SSH_KEY_NAME in the config.py file contains the name of the key pair used to access AWS. This field is the name of the file with the .pem extension (SSH_KEY_NAME = "MyKeyPair.pem"). The same name is used in the fleet configuration file (e.g. exampleFleet.json) but without using the .pem extension ("KeyName": "MyKeyPair").

• Avoid using web-browser to create folders directly on S3 site console.aws.amazon.com/s3/. This would result in inaccessible folders for the script.

• Setting # SQS QUEUE INFORMATION: in config.py : SECONDS_TO_START is the delay between individual CP processes starting up in the Docker container - 5 minutes is reasonable. SQS_MESSAGE_VISIBILITY is the one that user should match to the length of processing job- for analysis it's usually between 30-90 minutes, for illumination correction it should be set to the max 12 hour limit (720*60). If set too short, it would result in a single plate being processed multiple time, wasting the resource.

• If Fiji hangs for a long time without starting, check if your input directory is set to a folder with a large number of files- Fiji may be trying to read the whole directory before running. This can also happen if you are trying to load images that are very large.

• Make sure if you are grouping based on multiple variables there are no spaces between them in your listing- ie "Metadata_Plate=Plate1,Metadata_Well=A01" is correct, "Metadata_Plate=Plate1, Metadata_Well=A01" is incorrect.