Python IPC



Mixin for types that implement a python session.



Axon functions for py


Python library.



The Python library is used to execute Python code in a sandboxed Docker container. The default Docker image running in this container is configured to provide access to a number of Python libraries to facilitate scientific computing and machine learning.


The Python library requires access to a Docker instance through use of the docker extension. Docker must have the hxpy image installed in order to execute Python code. The hxpy image can be pulled from the Haxall GitHub package repository:

> docker pull ghcr.io/haxall/hxpy:latest

If you are running SkySpark on a linux-based OS, then you must run SkySpark with Java 17+ in order to connect to the default docker daemon (this is because the default daemon must be accessed using a Unix Domain Socket, and Java didn't add support for that until Java 16). Note also that this Unix domain socket is typically owned by root:docker and you may need to add your user to the docker group in order to connect to the socket. If your user does not have access to the domain socket you will likely see an error like this:

sys::IOErr: java.net.BindException: Permission denied


To run Python code, you must first open a session. Opening a session causes a new Docker container to be created and started. This container is running the hxpy Python module which is responsible for handling the various commands you can issues on a session. Use the py() Axon function to create a session.

The general pattern for using a python session is:

  1. Create a session using py()
  2. Configure the session using funcs like pyDefine(), pyExec(), and pyTimeout()
  3. Evaluate an expression using pyEval() to get a result and close the session.

The following is a simple example demonstrating this pattern. In this example we create a session, define some variables, and evaluate an expression. After this code executes, the val variable will have the value 200.

val: py()
  .pyDefine("a", 100)
  .pyDefine("b", 2)
  .pyExec("def mult(x, y): return x * y\n")
  .pyEval("mult(a, b)")

Persistent Sessions

By default, a Python session is closed after a call to pyEval(). However, if you create a Python session in a task, then the session will remain opened until the task is killed/removed. There is no need to call taskLocalSet() to save the Python session returned by py(). The first call to py() in your task will create a new Python session. Subsequent message processed by your task will re-use the same session. You will typically want to use pyInit() to do any one-time configuration of your python session in your task:

// Example task
(msg) => do
  v: msg["v"]
  // This py() session will be remembered between task invocations
  r: py()
      .pyInit((session) => do
         // this code will only be invoked once for the session
         session.pyExec("def sq(x): return x * x\n")
      .pyDefine("v", v)
  echo("Got: " + r)

Data Marshalling

You can use the pyDefine() function to pass variables to the remote the Python instance for use in your code. The Python library will convert Haystack types to Python types as defined in the table below. Only the marked data types can be returned using pyEval().

Haystack    Python        pyEval()   Notes
--------    ------        --------   -----
null        None          ✓
Marker      Marker        ✓          hxpy.haystack.marker.Marker
NA          NA            ✓          hxpy.haystack.na.NA
Remove      Remove        ✓          hxpy.haystack.remove.Remove
Bool        bool          ✓
Number      int/float     ✓          Units not marshalled
Ref         Ref           ✓          hxpy.haystack.ref.Ref
Str         str           ✓
Uri         str
Time        datetime.time ✓
DateTime    datetime.date ✓
Buf         bytes         ✓
Dict        dict          ✓
List        list          ✓
Grid        Grid          ✓          hxpy.haystack.grid.Grid (see below)
MatrixGrid  numpy.ndarray ✓          see below

Grid Marshalling

The Python library has built-in support for marshalling Grids to Python types that are optimized and suitable for machine learning.

By default, a Grid is marshalled to an hxpy.haystack.grid.Grid type in Python. It is very common in data science applications to work with tabular data using the pandas.DataFrame type. You can get a DataFrame from a Grid in your Python code by using grid.to_dataframe(). You can return a pandas.DataFrame as the result of an pyEval() expression and it will automatically be converted to a Grid. You can also convert a DataFrame to a Grid using the Grid.from_dataframe(frame) static method.

py().pyDefine("grid", grid).pyEval("str(grid.to_dataframe())")

You can also convert a DataFrame to a Grid or GridBuilder using Grid.from_dataframe(frame) or GridBuilder.from_dataframe(frame) respectively. Use the latter when you need to add grid or column meta before creating the Grid.

Any Grid that is converted to a matrix using toMatrix() will be converted to a numpy.ndarray for use in your Python code.

py().pyDefine("data", toMatrix(g)).pyEval("type(data)")

Python Environment

The Docker container running Python will launch the hxpy module. Python code executed in this container has access to several common Python libraries that are useful for scientific computing and machine learning. The full list of available Python libraries is available here


When the hxpy container is started, the current runtime project's io/ directory will be mounted into the container at /io/. This allows you to create/read files between the Haxall environment and the Python container.

// Write a simple text file to the mounted io/ directory
  .pyExec("""def ioTest():
               with open("/io/example.txt", "w") as f:
                 f.write("Testing IO Example")

// read in Axon the contents of the file we just created in Python
s: ioReadStr(`io/example.txt`)

Docker in Docker

In some deployments, the Haxall runtime itself is running in a Docker container. To use the Python lib in this case it becomes necessary to run Docker within Docker. The Haxall Docker instance needs to create a Python Docker instance and connect to it.

You will need to create a Docker network for for your containers to run in. The Haxall runtime must be run in this network and you must use the network option of the py() func to have the python Docker instance also run in that network.