Other Frameworks
In this tutorial we'll briefly cover running tensorflow and PopART for Machine Learning, and writing IPU programs directly via the PopLibs library in C++. Extra links and resources will be provided for more in-depth information.
Terminology
Within Graphcore, Poplar refers to the tools (e.g. Poplar Graph Engine or Poplar Graph Compiler) and libraries (PopLibs) for programming on IPUs.
The Poplar SDK is a package of software development tools, including
- TensorFlow 1 and 2 for the IPU
- PopTorch (Wrapper around PyTorch for running on IPU)
- PopART (Poplar Advanced Run-Time, provides support for importing, creating, and running ONNX graphs on the IPU)
- Poplar and PopLibs
- PopDist (Poplar Distributed Configuration Library) and PopRun (Command line utility to launch distributed applications)
- Device drivers and command line tools for managing the IPU
For more details see here.
Other ML frameworks: Tensorflow and PopART
Besides being able to run PyTorch code, as demonstrated in the previous lessons, the Poplar SDK also supports running ML learning applications with tensorflow or PopART.
Tensorflow
The Poplar SDK includes implementation of TensorFlow and Keras for the IPU.
For more information, refer to Targeting the IPU from TensorFlow 2 and TensorFlow 2 Quick Start.
These are available from the image graphcore/tensorflow:2.
For a quick example, we will run an example script from https://github.com/graphcore/examples/tree/master/tutorials/simple_applications/tensorflow2/mnist. To get started, save the following yaml and run kubectl create -f <yaml-file> to create the IPUJob:
apiVersion: graphcore.ai/v1alpha1
kind: IPUJob
metadata:
generateName: tensorflow-example-
spec:
jobInstances: 1
ipusPerJobInstance: "1"
workers:
template:
spec:
containers:
- name: tensorflow-example
image: graphcore/tensorflow:2
command: [/bin/bash, -c, --]
args:
- |
apt update;
apt upgrade -y;
apt install git -y;
cd;
mkdir build;
cd build;
git clone https://github.com/graphcore/examples.git;
cd examples/tutorials/simple_applications/tensorflow2/mnist;
python -m pip install -r requirements.txt;
python mnist_code_only.py --epochs 1
resources:
limits:
cpu: 32
memory: 200Gi
securityContext:
capabilities:
add:
- IPC_LOCK
volumeMounts:
- mountPath: /dev/shm
name: devshm
restartPolicy: Never
hostIPC: true
volumes:
- emptyDir:
medium: Memory
sizeLimit: 10Gi
name: devshm
Running kubectl logs <pod> should show the results similar to the following
...
2023-10-25 13:21:40.263823: I tensorflow/compiler/plugin/poplar/driver/poplar_platform.cc:43] Poplar version: 3.2.0 (1513789a51) Poplar package: b82480c629
2023-10-25 13:21:42.203515: I tensorflow/compiler/plugin/poplar/driver/poplar_executor.cc:1619] TensorFlow device /device:IPU:0 attached to 1 IPU with Poplar device ID: 0
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
11501568/11490434 [==============================] - 0s 0us/step
2023-10-25 13:21:43.789573: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2023-10-25 13:21:44.164207: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-10-25 13:21:57.935339: I tensorflow/compiler/jit/xla_compilation_cache.cc:376] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
Epoch 1/4
2000/2000 [==============================] - 17s 8ms/step - loss: 0.6188
Epoch 2/4
2000/2000 [==============================] - 1s 427us/step - loss: 0.3330
Epoch 3/4
2000/2000 [==============================] - 1s 371us/step - loss: 0.2857
Epoch 4/4
2000/2000 [==============================] - 1s 439us/step - loss: 0.2568
PopART
The Poplar Advanced Run Time (PopART) enables importing and constructing ONNX graphs, and running graphs in inference, evaluation or training modes. PopART provides both a C++ and Python API.
For more information, see the PopART User Guide
PopART is available from the image graphcore/popart.
For a quick example, we will run an example script from https://github.com/graphcore/tutorials/tree/sdk-release-3.1/simple_applications/popart/mnist. To get started, save the following yaml and run kubectl create -f <yaml-file> to create the IPUJob:
apiVersion: graphcore.ai/v1alpha1
kind: IPUJob
metadata:
generateName: popart-example-
spec:
jobInstances: 1
ipusPerJobInstance: "1"
workers:
template:
spec:
containers:
- name: popart-example
image: graphcore/popart:3.3.0
command: [/bin/bash, -c, --]
args:
- |
cd ;
mkdir build;
cd build ;
git clone https://github.com/graphcore/tutorials.git;
cd tutorials;
git checkout sdk-release-3.1;
cd simple_applications/popart/mnist;
python3 -m pip install -r requirements.txt;
./get_data.sh;
python3 popart_mnist.py --epochs 1
resources:
limits:
cpu: 32
memory: 200Gi
securityContext:
capabilities:
add:
- IPC_LOCK
volumeMounts:
- mountPath: /dev/shm
name: devshm
restartPolicy: Never
hostIPC: true
volumes:
- emptyDir:
medium: Memory
sizeLimit: 10Gi
name: devshm
Running kubectl logs <pod> should show the results similar to the following
...
Creating ONNX model.
Compiling the training graph.
Compiling the validation graph.
Running training loop.
Epoch #1
Loss=16.2605
Accuracy=88.88%
Writing IPU programs directly with PopLibs
The Poplar libraries are a set of C++ libraries consisting of the Poplar graph library and the open-source PopLibs libraries.
The Poplar graph library provides direct access to the IPU by code written in C++. You can write complete programs using Poplar, or use it to write functions to be called from your application written in a higher-level framework such as TensorFlow.
The PopLibs libraries are a set of application libraries that implement operations commonly required by machine learning applications, such as linear algebra operations, element-wise tensor operations, non-linearities and reductions. These provide a fast and easy way to create programs that run efficiently using the parallelism of the IPU.
For more information, see Poplar Quick Start and Poplar and PopLibs User Guide.
These are available from the image graphcore/poplar.
When using the PopLibs libraries, you will have to include the include files in the include/popops directory, e.g.
#include <include/popops/ElementWise.hpp>
and to link the relevant PopLibs libraries, in addition to the Poplar library, e.g.
g++ -std=c++11 my-program.cpp -lpoplar -lpopops
For a quick example, we will run an example from https://github.com/graphcore/examples/tree/master/tutorials/simple_applications/poplar/mnist. To get started, save the following yaml and run kubectl create -f <yaml-file> to create the IPUJob:
apiVersion: graphcore.ai/v1alpha1
kind: IPUJob
metadata:
generateName: poplib-example-
spec:
jobInstances: 1
ipusPerJobInstance: "1"
workers:
template:
spec:
containers:
- name: poplib-example
image: graphcore/poplar:3.3.0
command: ["bash"]
args: ["-c", "cd && mkdir build && cd build && git clone https://github.com/graphcore/examples.git && cd examples/tutorials/simple_applications/poplar/mnist/ && ./get_data.sh && make && ./regression-demo -IPU 1 50"]
resources:
limits:
cpu: 32
memory: 200Gi
securityContext:
capabilities:
add:
- IPC_LOCK
volumeMounts:
- mountPath: /dev/shm
name: devshm
restartPolicy: Never
hostIPC: true
volumes:
- emptyDir:
medium: Memory
sizeLimit: 10Gi
name: devshm
Running kubectl logs <pod> should show the results similar to the following
...
Using the IPU
Trying to attach to IPU
Attached to IPU 0
Target:
Number of IPUs: 1
Tiles per IPU: 1,472
Total Tiles: 1,472
Memory Per-Tile: 624.0 kB
Total Memory: 897.0 MB
Clock Speed (approx): 1,850.0 MHz
Number of Replicas: 1
IPUs per Replica: 1
Tiles per Replica: 1,472
Memory per Replica: 897.0 MB
Graph:
Number of vertices: 5,466
Number of edges: 16,256
Number of variables: 41,059
Number of compute sets: 20
...
Epoch 1 (99%), accuracy 76%