I was following this tutorial to use tensorflow serving using my object detection model. I am using tensorflow object detection for generating the model. I have created a frozen model using this exporter (the generated frozen model works using python script).
The frozen graph directory has following contents ( nothing on variables directory)
variables/
saved_model.pb
Now when I try to serve the model using the following command,
tensorflow_model_server --port=9000 --model_name=ssd --model_base_path=/serving/ssd_frozen/
It always shows me
...
tensorflow_serving/model_servers/server_core.cc:421] (Re-)adding model: ssd 2017-08-07 10:22:43.892834: W tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:262] No versions of servable ssd found under base path /serving/ssd_frozen/ 2017-08-07 10:22:44.892901: W tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:262] No versions of servable ssd found under base path /serving/ssd_frozen/
...
I had same problem, the reason is because object detection api does not assign version of your model when exporting your detection model. However, tensorflow serving requires you to assign a version number of your detection model, so that you could choose different versions of your models to serve. In your case, you should put your detection model(.pb file and variables folder) under folder: /serving/ssd_frozen/1/. In this way, you will assign your model to version 1, and tensorflow serving will automatically load this version since you only have one version. By default tensorflow serving will automatically serve the latest version(ie, the largest number of versions).
Note, after you created 1/ folder, the model_base_path is still need to be set to --model_base_path=/serving/ssd_frozen/.