4. Executing graphs

The Session class is used to run graphs on an IPU device. Before the graph can be run, the way in which data will be transferred to and from the IPU must be specified. Then an IPU device can be selected to execute the graph.

4.1. Setting input/output data buffers for an execution

Input and output data is passed to and from a Session object via IStepIO objects. Each call to session.run(...) takes such a IStepIO object. For every input tensor, this object contains a number of buffers that the session can read input data from. And for every anchored tensor, it contains a number of buffers to write output data to. There is more information about anchors in Section 4.1.1, Retrieving results.

The number and shape of these buffers depend on a variety of factors including

  1. the shape of associated tensor in the ONNX model

  2. the DataFlow configuration (see next section) as passed to the Session object’s constructor
  3. the number of local replicas, and

  4. the accumulation factor.

This is explained in more detail in the C++ API documentation under the IStepIO class (for inputs) and under the DataFlow class (for outputs).

When using Python, the PyStepIO class is a convenient way of providing a session with input and output buffers. For both input and output, this class takes a dictionary with tensor names as keys and Python (or Numpy) arrays as values. PopART splits up these arrays internally to provide the Session object with the buffers that it needs.

Note that Session has a convenience method, initAnchorArrays, that can create the output dictionary that PyStepIO needs automatically.

An alternative to PyStepIO is the PyStepIOCallback class, which you can use to implement IStepIO by means of a callback mechanism.

The C++ equivalents of PyStepIO and PyStepIOCallback are StepIO and StepIOCallback, respectively.

Below is an example of how to use PyStepIO:

# Create buffers to receive results from the execution
anchors = session.initAnchorArrays()

# Generate some random input data
data_a = np.random.rand(1).astype(np.float32)
data_b = np.random.rand(1).astype(np.float32)

stepio = popart.PyStepIO({'a': data_a, 'b': data_b}, anchors)


If there are any pre-defined inputs (such as weights or biases) in the graph then they will not be specified in the IStepIO object. However, before executing the graph, they will need to the copied to the hardware. If there are any optimiser-specific parameters which can be modified, then these must be written to the device. For example:


These can also be updated between executions.

# Update learning rate parameter between training steps
stepLr = learningRate[step]

4.1.1. Retrieving results

The DataFlow class describes how to execute the graph. When you construct a DataFlow class it expects two parameters:

df = popart.DataFlow(1, {o: popart.AnchorReturnType("ALL")})

The first argument is batchesPerStep. This is the the number of batches a call to session.run(...) executes for before returning control to the caller.

The second argument is a Python dictionary with keys that are the names of the tensors to retrieve from the model via the IStepIO object. We call such tensors anchors. The associated values are AnchorReturnType values, which are one of:

  • popart.AnchorReturnType("ALL"): a vector of results is returned, one for each iteration of the graph.

  • popart.AnchorReturnType("EVERYN", N): a vector containing the tensor, but only for iterations which are divisible by N.

  • popart.AnchorReturnType("FINAL"): the value of the tensor on the final iteration through the graph.

  • popart.AnchorReturnType("SUM"): the sum of the values of the tensor from each iteration through the graph.

The effect of this setting on the number of output buffers is explained in more detail in our C++ API documentation documentation (see documentation for the DataFlow class).

Note that the set of tensors that are anchored may differ from those tensors marked as ONNX model outputs (via builder.addOutputTensor(...)). That is, a model’s output tensor need not be anchored and an anchored tensor need not be a model output – any tensor can be anchored. It is the anchored tensors that are considered ‘output’ in the context of a IStepIO object.

4.2. Selecting a device for execution

The device manager allows the selection of an IPU configuration for executing the session. The device must be passed into the session constructor.

df = popart.DataFlow(1, {o: popart.AnchorReturnType("ALL")})
device = popart.DeviceManager().createCpuDevice()
s = popart.InferenceSession("onnx.pb", deviceInfo=device, dataFlow=df)

The device manager can enumerate the available devices with the enumerateDevices method. The acquireAvailableDevice method will acquire the next available device. The first parameter specifies how many IPUs to acquire.

# Acquire a two-IPU pair
dev = popart.DeviceManager().acquireAvailableDevice(2)

Using acquireDeviceById will select a device from the list of IPU configurations, as given by the enumerateDevices method, or by the gc-info command-line tool. This may be a single IPU or a group of IPUs.

# Acquire IPU configuration 5
dev = popart.DeviceManager().acquireDeviceById(5)

The method createIpuModelDevice is used to create a Poplar software emulation of an IPU device. Similarly, the method createCpuDevice creates a simple Poplar CPU backend. See the PopART C++ API Reference for details.

By default the functions acquireAvailableDevice and acquireDeviceById will attach the device immediately to the running process. You can pass the DeviceConnectionType.OnDemand option to the DeviceManager to defer the device attachment until it is required by PopART.

# Acquire four IPUs on demand
dev = popart.DeviceManager().acquireAvailableDevice(4, connectionType=connectionType)

4.3. Executing a session

Once the device has been selected, the graph can be compiled for it, and loaded into the hardware. The prepareDevice method is used for this:


To execute the session you need to call the session’s run method.


If the session is created for inference, the user is responsible for ensuring that the forward graph finishes with the appropriate operation for an inference. If losses are provided to the inference session the forward pass and the losses will be executed, and the final loss value will be returned.

If the session was created for training, any pre-initialised parameters will be updated to reflect the changes made to them by the optimiser.

4.4. Saving and loading a model

The method modelToHost writes a model with updated weights to the specified file.


Note that if you plan to run your program in multiple processes simultaneously, you should avoid possible race conditions by writing to different files, for example by using temporary files.

A file of saved parameters, for example from an earlier execution session, can be loaded into the current session.


4.5. Retrieving profiling reports

Poplar can provide profiling information on the compilation and execution of the graph. Profiling is not enabled by default.

To get profiling reports in PopART, you will need to enable profiling in the Poplar engine. For example:

opts = popart.SessionOptions()
opts.engineOptions = {"autoReport.all": "true"}

You can also control what information is included in the profiling report:

opts.reportOptions = {"showExecutionSteps": "true"}

There are two method functions of the session object to access the profiling information:

  • getSummaryReport retrieves a text summary of the compilation and execution of the graph.

  • getReport returns a libpva Report object containing details of the compilation and execution of the graph. the graph

If profiling is not enabled, then the summary report will say ‘Execution profiling not enabled’ and the report will contain no information in the execution.

For more information on the libpva Report, see the pva user guide and api document: * Libpva User Guide * Libpva C++ API Reference * Libpva Python API Reference.

For more information on profiling control and the information returned by these functions, see the Profiling chapter of the Poplar and PopLibs User Guide.

4.6. Turning on execution tracing

PopART contains an internal logging system that can show the progress of graph compilation and execution.

Logging information is generated from the following modules:


Generic PopART module, if no module specified


The ONNX session (the PopART API)


The intermediate representation


The Poplar backend


The transform module


The pattern module


The builder module


The op module


The opx module


The constant expression module


The Python module


An unidentified module

The logging levels, in decreasing verbosity, are shown below.


The highest level, shows the order of method calls








Only critical errors


No logging

The default is “OFF”. You can change this, and where the logging information is written to, by setting environment variables, see Environment variables.

4.6.1. Programming interface

You can also control the logging level for each module in your program.

For example, in Python:

# Set all modules to DEBUG level
# Turn off logging for the session module

And in C++:

// Set all modules to DEBUG level
popart::logger::setLevel("popart", "DEBUG")
// Turn off logging for the session module
popart::logger::setLevel("session", "OFF")

4.6.2. Output format

The information is output in the following format:

[<timestamp>] [<module>] [<level>] <logging string>

For example:

[2019-10-16 13:55:05.359] [popart:devicex] [debug] Creating poplar::Tensor 1
[2019-10-16 13:55:05.359] [popart:devicex] [debug] Creating host-to-device FIFO 1
[2019-10-16 13:55:05.359] [popart:devicex] [debug] Creating device-to-host FIFO 1

4.7. Errors

The full hierarchy of error that can be thrown from a popart program is:


4.7.1. Application errors

Application errors will be due to a bug in either the users code or in the framework. These are:


4.7.2. System errors

These are:


An instance of a poplar_recoverable_error has an attribute recoveryAction which contains the action required to recover from this error. This will be one of the values:


A poplar_unrecoverable_error suggests that the user needs to contact Graphcore support and that this issue could either be an SDK bug or a machine issue.

An unknown_runtime_error could be either recoverable or unrecoverable, but there is not enough information to know for sure. In this instance, the 3 recovery options (IPU_RESET, PARTITION_RESET, and POWER_CYCLE) should be tried and if none resolve the issue, then Graphcore support should be contacted.