2. Installation

PopTorch is included with the Poplar SDK (see the Getting Started guide for your system for how to install the Poplar SDK.). PopTorch is packaged as a Python wheel file that can be installed using pip.

Important

pip >= 18.1 is required for PopTorch dependencies to be installed properly.

To update pip:

$ pip install -U pip

2.1. Version compatibility

The following are the corresponding torch, torchvision, torchaudio and torch_scatter versions and supported Python versions.

poptorch	torch	torchvision	torchaudio	torch_scatter	python
3.4	2.0.1	0.15.2	2.0.1	>=2.0.9 and <=2.1.1	>=3.8
3.3	2.0.1	0.15.2	2.0.1	>=2.0.9 and <=2.1.1	>=3.8
3.2	1.13.1	0.14.1	0.13.1	>=2.0.9 and <=2.1.0	>=3.7
3.1	1.13.0	0.14.0	0.13.0	>=2.0.9 and <=2.1.0	>=3.7
3.0	1.10.0	0.11.1	0.10.0	N/A	>=3.6
2.6	1.10.0	0.11.1	0.10.0	N/A	>=3.6
2.5	1.10.0	0.11.1	0.10.0	N/A	>=3.6
2.4	1.10.0	0.11.1	0.10.0	N/A	>=3.6
2.3	1.9.0	0.10.0	0.9.0	N/A	>=3.6
2.2	1.9.0	0.10.0	0.9.0	N/A	>=3.6
2.1	1.7.1	0.8.2	0.7.1	N/A	>=3.6
2.0	1.7.1	0.8.2	0.7.1	N/A	>=3.6
1.4	1.6.0	0.7.0	0.6.0	N/A	>=3.6

Based on https://github.com/pytorch/vision/blob/master/README.md

Note

To ensure version compatibility, torchvision and torchaudio are automatically installed with PopTorch in Poplar SDK 3.3 and later.

2.2. Using a Python virtual environment

We recommend creating and activating a virtual environment to isolate your PopTorch environment from the system Python environment. You can use the Python tool virtualenv for this. You can create a virtual environment and install PopTorch as shown below:

$ virtualenv -p python3 poptorch_test
$ source poptorch_test/bin/activate
$ pip install -U pip
$ pip install <sdk_path>/poptorch_x.x.x.whl

2.3. Setting the environment variables

The PopART and Poplar runtime libraries are required to use PopTorch, so you will need to set the library search paths, using the scripts provided in the SDK:

# Enable the Python environment containing PopTorch (if not already enabled)
$ source poptorch_test/bin/activate

# Add the Poplar and PopART runtime libraries to the search path
$ source <sdk_path>/poplar-ubuntu_<os_ver>-<poplar_ver>+<build>/enable.sh
$ source <sdk_path>/popart-ubuntu_<os_ver>-<poplar_ver>+<build>/enable.sh

where <sdk_path> is the location of the Poplar SDK on your system. <os_ver> is the version of Ubuntu on your system, <poplar_ver> is the software version number of the Poplar SDK and <build> is the build information.

2.4. Validating the setup

You can run this simple example to verify that the system is working as expected. This example can be found in the Poplar SDK examples directory.

Listing 2.1 Simple adder example

#!/usr/bin/env python3
# Copyright (c) 2020 Graphcore Ltd. All rights reserved.

import torch
import torch.nn as nn
import poptorch

# This simple example demonstrates compiling a model to add
# two tensors together using the IPU.


class SimpleAdder(nn.Module):
    def forward(self, x, y):
        return x + y


model = SimpleAdder()
inference_model = poptorch.inferenceModel(model)

t1 = torch.tensor([1.])
t2 = torch.tensor([2.])

assert inference_model(t1, t2) == 3.0
print("Success")