update README

p3/README.md

@@ -24,7 +24,7 @@ Before starting, please review the [general project directions](../projects.md).
 
 * none yet
 
-## Part 1: Communication
+## Part 1: Communication (gRPC)
 
 In this project, three client programs (upload.py, csvsum.py, and
 parquetsum.py) will communicate with a server, server.py, via gRPC.
@@ -46,19 +46,22 @@ servicer code for your server.  All field types will be strings,
 except `total` and `csv_data`, which should be `int64` and 
 `bytes` respectively.
 
+### Local Testing (Recommended)
+
 Now build the .proto on your VM.  Install the tools like this:
 
 ```sh
 python3 -m venv venv
 source venv/bin/activate
-pip3 install grpcio==1.66.1 grpcio-tools==1.66.1 numpy==2.1.1 protobuf==5.27.2 pyarrow==17.0.0 setuptools==75.1.0
+pip install grpcio-tools==1.70.0 grpcio==1.70.0 protobuf==5.29.3
 ```
 
 Then use `grpc_tools.protoc` to build your .proto file.
 
-In your server, override the two RPC methods for the generated
-servicer.  For now, you can just return messages with the error field
-set to "TODO", leaving any other field unspecified.
+Create a server.py program that uses the generated gRPC code.  Your
+server should override the two RPC methods for the servicer.
+For now, you can just return messages with the error field set to
+"TODO", leaving any other field unspecified.
 
 If communication is working correctly so far, you should be able to
 start a server and used a client to get back a "TODO" error message
@@ -70,7 +73,9 @@ python3 upload.py simple.csv
 # should see "TODO"
 ```
 
-Create a Dockerfile to build an image that will also let you run your
+### Docker Deployment (Required)
+
+Create a Dockerfile to build an image that will let you run your
 server in a container.  It should be possible to build and run your
 server like this:
 
@@ -81,12 +86,32 @@ docker run -d -m 512m -p 127.0.0.1:5440:5440 p3
 
 The client programs should then be able to communicate with the
 Dockerized programs the same way they communicated with the server
-outside of a container.
+outside of a container (in the "Local Testing" example above).
+
+Note that we won't install any Python packages (such as the gRPC
+tools) on our test VM (like we asked you to do for manual testing), so
+it is important that compiling your .proto file is one of the steps
+that happens during Docker build.
+
+Your Dockerfile should also copy in the client programs we provide
+into your Docker image, and your image should include whatever these
+clients need to run.  When we test your code, we will run the clients
+in the same container as the server, like this:
+
+```
+docker run --name=server -d -m 512m -p 127.0.0.1:5440:5440 -v ./inputs:/inputs p3   # server
+docker exec server python3 upload.py /inputs/test1.csv                              # client
+```
+
+Note that we are using a volume mount to share make input data visible
+inside your container so that we can `exec` clients inside your
+container with that data.
 
-If you want to make code changes without rebuilding the image each
-time, consider using a volume mount to make the latest version of
-server.py on your VM replace the server.py in the file (if the server
-is not at /server.py inside the container, modify accordingly):
+You may also wish to use volume mounts to speed up your development
+process.  For example, if you volume mount your server code (on your
+VM) over your server.py file inside your container, you will be able
+to re-run your container with newer server.py code without rebuilding
+first.  Here's an example:
 
 ```
 docker run --rm -m 512m -p 127.0.0.1:5440:5440 -v ./server.py:/server.py p3
@@ -190,4 +215,4 @@ your code must NOT hold the lock when doing file I/O.
 
 ## Tester
 
-Not released yet.  Details coming soon!
\ No newline at end of file
+Not released yet.  Details coming soon!