Misc updates

README.md

@@ -16,7 +16,7 @@ The basic idea is simple: a client communicates with some remote server using ht
 
 ## `pfms` Specificities
 
-Broadly speaking, `pfms` provides this exact behavior. However, it is uniquely tailored to providing services within the context of the [pl-monai_spleenseg](https://github.com/FNNDSC/pl-monai_spleenseg) ChRIS plugin. Indeed, `pfms` uses this exact plugin as a internal module to perform the same segmentation. Moreover, unlike more conventional MLop "model servers", `pfms` accepts as input NIfTI volumes and returns NIfTI volumes as resultants. This is considerable more efficient than a JSON serialization of an image.
+Broadly speaking, `pfms` provides this exact behavior. However, it is uniquely tailored to providing services within the context of the [pl-monai_spleenseg](https://github.com/FNNDSC/pl-monai_spleenseg) ChRIS plugin. Indeed, `pfms` uses this exact plugin as a internal module to perform the same segmentation. Moreover, unlike more conventional MLop "model servers", `pfms` accepts as input NIfTI volumes and returns NIfTI volumes as resultants. This is considerably more efficient than a JSON serialization and deserialization of payload data to encode an image.
 
 
 ## `pfms` Deployment
@@ -33,7 +33,7 @@ docker build --build-arg UID=UID -t local/pfms .
 
 ### dockerhub
 
-To use the version available on dockerhub:
+To use the version available on dockerhub (note, might not be available at time of reading):
 
 ```bash
 docker pull fnndsc/pfms
@@ -44,7 +44,7 @@ docker pull fnndsc/pfms
 To start the services
 
 ```bash
-SESSIONUSER=localhost
+SESSIONUSER=localuser
 docker run --gpus all --privileged          \
         --env SESSIONUSER= $SESSIONUSER     \
         --name pfms --rm -it -d             \
@@ -55,7 +55,7 @@ docker run --gpus all --privileged          \
 To start with source code debugging and live refreshing:
 
 ```bash
-SESSIONUSER=localhost
+SESSIONUSER=localuser
 docker run --gpus all --privileged          \
         --env SESSIONUSER= $SESSIONUSER     \
         --name pfms --rm -it -d             \
@@ -70,7 +70,7 @@ docker run --gpus all --privileged          \
 
 ### Upload model file in pth format
 
-`pfms` can host/provide multiple "models" -- a model is understood here to be simply a pre-trained weights file in `pth` format as generated by `pl-monai_spleenseg` during a training phase. This `pth` file can be uploaded to `pfms` by POSTing the file this endpoint:
+`pfms` can host/provide multiple "models" -- a model is understood here to be simply a pre-trained weights file in `pth` format as generated by `pl-monai_spleenseg` during a training phase. This `pth` file can be uploaded to `pfms` by POSTing the file to this endpoint:
 
 ```html
 POST :2024/api/v1/spleenseg/modelpth/?modelID=<modelID>
@@ -86,7 +86,7 @@ To run the segmentation on a volume using a model file, `POST` the NIfTI volume
 POST :2024/api/v1/spleenseg/NIfTIinference/?modelID=<modelID>
 ```
 
-Here, a NIfTI volume is passed as a `FileUpload` request. The `pfms` instance will save/unpack this file within itself, and then run the `pl-monai_spleenseg` inference mode using as model weights the data in the `<modelID>`. The resultant NIfTI file is read and streamed back to the caller, which will typically save this file to disk or do further processing.
+Here, a NIfTI volume is passed as a `FileUpload` request. The `pfms` instance will save/unpack this file within itself, and then run the `pl-monai_spleenseg` inference mode using as model weights the `pth` file associated with `<modelID>`. The resultant NIfTI file, stored within the server, is then read and streamed back to the caller, which will typically save this file to disk or do further processing.
 
 **Note that this call will block until processing complete!** Processing (depending on network speed, etc) is typically less than 30 seconds.