Graphcore Poplar SDK Container Images Now Available on Docker Hub

Graphcore’s Poplar® SDK is available for developers to  , with Graphcore joining Docker’s Verified P

Graphcore’s Poplar® SDK is available for developers to  , with Graphcore joining Docker’s Verified Publisher Program. Together with Docker, we’re distributing our software stack as container images, enabling developers to easily build, manage and deploy ML applications on Graphcore IPU systems. We continue to enhance the developer experience to make our hardware and software even easier to use. Just over a year ago we introduced a selection of pre-built Docker containers for users. Now, we are making our Poplar SDK, PyTorch for IPU, TensorFlow for IPU and Tools fully accessible to everyone in the Docker Hub community as part of our mission to drive innovation. Here’s more information on what’s in it for developers and how to get started. Docker has become the primary source for pulling container images – according to   there’s been a total of   all-time pulls on Docker Hub. Furthermore, Docker Hub remains one of the “  developer tools based on the   answered by 80,000 developers. For IPU developers, our Docker container images simplify and accelerate application development workflows being deployed in production on IPU systems by supplying pre-packaged runtime environments for applications built using PyTorch, TensorFlow or directly with Graphcore’s Poplar SDK. Containerised applications provide increased portability of applications with consistent, repeatable execution and are an important enabler for many MLOps frameworks. As of today, developers can freely install our Poplar Software stack, co-designed with the IPU (Intelligence Processing Unit), specifically for machine intelligence applications. Poplar is Graphcore’s graph toolchain which sits at the core of our easy-to-use and flexible software development environment fully integrated with standard machine learning frameworks so developers can easily port existing models. For developers who want full control to exploit maximum performance from the IPU, Poplar enables direct IPU programming in Python and C++ through PopART™ (Poplar Advanced Runtime). Our Poplar SDK images can be pulled via the following repositories: And as part of the Docker Verified Publisher Program, Graphcore container images are exempt from rate limiting — meaning that developers have unlimited container image requests for Poplar, regardless of their Docker Hub subscription. The Poplar Docker containers encapsulate everything needed to run models in IPU in a complete filesystem (i.e. Graphcore’s Poplar® SDK, runtime environment, system tools, configs, and libraries). In order to use these images and run IPU code, you will need to complete the following steps:

1. Install Docker on the host machine
2. Pull Graphcore’s Poplar SDK container images from Docker Hub
3. Prepare Access to IPUs
4. Verify IPU accessibility with Docker container
5. Sample App Code on IPUs Docker installation varies based on operating system, version, and processor.  You can follow  . Once Docker is installed, you can run commands to download our hosted images from Docker Hub and run them in the host machine. The Poplar SDK container images can be pulled from the  repository on Docker Hub. There are four repositories and these repositories may contain multiple images based on the SDK version, OS, and architecture. Pulling from the framework repo downloads the latest version of the SDK compiled for AMD host processor by default. To pull the latest TensorFlow image use: If you want to select a specific build for a specific SDK version and processor, you can configure the tags based on  . To talk to the IPUs in PODs, we must configure the connection between the host machines and the IPUs – the IPU over Fabric (IPUoF). The information that Poplar needs to access devices can be passed via an IPUoF configuration file which is, by default, written to a directory in your home directory ( ). The configuration files are useful when the Poplar hosts do not have direct network access to the V-IPU controller (for security reasons, for example).  If you are using Graphcloud, the IPUoF default config file is generated every time a new user is created and added to the POD. Check if there are .conf files inside that folder (e.g.  ). If you have this setup, you can proceed to the next step. If not available, you will need to configure Poplar to connect to the V-IPU server by following the  . Take note to save your IPUoF config file in the folder   to run the scripts in the next section. Now that you have the container ready, you can check if the IPU is accessible from inside the container. List the IPU devices within the context of the container by running the following: First, get the code from the Graphcore tutorials repository in GitHub. The Docker container is an isolated environment. It will come empty and not have access to the host machine’s file system. In order to use data from your host machine, the data needs to be accessible within the Docker container.  You can make the data accessible by mounting directories as volumes to share data between the host machine and the Docker container environment.  A common pattern when working with a Docker-based development environment is to mount the current directory into the container (as described in  ), then set the working directory inside the container with  . For example,  . To run the mnist example in the TensorFlow container, you can use the following command which mounts the tutorials repo into the docker container and runs it.