Intel® DevCloud for oneAPI

Overview Get Started Documentation Forum external link
1

Connect to DevCloud

Sign In to Connect to the DevCloud using SSH Clients.

2

Hello World!

Get Started by running a simple sample on DevCloud.

3

Run AI Analytics Toolkit Samples on DevCloud

Explore the samples already installed in Step 2.

Getting Started

oneDAL Hello World

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This Hello World sample code shows how to do batch linear regression using the python API package daal4py for Intel(R) oneAPI Data Analytics Library (oneDAL).

View code on GitHub*

PyTorch* Hello World

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This sample shows how to train a PyTorch* model and run the inference with Intel(R) oneAPI Deep Neural Network Library (oneDNNL) enabled.

View code on GitHub*

TensorFlow* Hello World

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This sample shows how Intel-optimized TensorFlow enables oneDNNL calls by default. It implements an example neural network with one convolution layer and one ReLU layer.

View code on GitHub*

Intel Modin Getting Started

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This Getting Started sample code show how to use distributed Pandas using the Modin package.

View code on GitHub*

Intel Python XGBoost Getting Started

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XGBoost* is a widely used gradient boosting library in the classical ML area. Designed for flexibility, performance, and portability, XGBoost* includes optimized distributed gradient boosting frameworks and implements Machine Learning algorithms underneath.

View code on GitHub*

LPOT sample for TensorFlow

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Intel(R) Low Precision Optimization Tool (LPOT) helps the user to simplify the processing to convert the fp32 model to int8/bf16.

View code on GitHub*

Features and Functionality

Distributed Linear Regression Training and Prediction

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This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for (oneDAL).

View code on GitHub*

Distributed K-Means Training and Prediction

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This sample code shows how to train and predict with a distributed k-means model using the python API package daal4py for (oneDAL).

View code on GitHub*

Distributed TensorFlow with Horvod Sample

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This sample code shows how to get started with scaling out a neural network's training in TensorFlow on multiple compute nodes in a cluster. The sample uses Horovod*, a distributed deep learning training framework, to facilitate the task of distributing the workload. Horovod's core principles are based on MPI concepts such as size, rank, local rank, allreduce, allgather and, broadcast.

View code on GitHub*

TensorFlow Performance Analysis by using Intel Model Zoo Sample

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This sample helps demonstrate AI workloads and deep learning models optimized by Intel and validated to run on Intel hardware. Using the Tensorflow Timeline, you can analyze the performance benefits from Intel Optimizations for Tensorflow* and oneDNN among different layers to efficiently execute, train, and deploy Intel-optimized models

View code on GitHub*

IntelTensorFlow_ModelZoo_Inference_with_FP32_Int8

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The example uses Intel's pretrained model published as part of Intel Model Zoo. The example also illustrates how to utilize TensorFlow and MKL run time settings to maximize CPU performance on ResNet50 workload

View code on GitHub*

End-to-End Workloads

Census

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This sample code illustrates how to use Intel® Distribution of Modin for ETL operations and ridge regression algorithm from the Intel® oneAPI Data Analytics Library (oneDAL) accelerated scikit-learn library to build and run an end to end machine learning workload

View code on GitHub*

Point Pillers

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This sample performs 3D object detection and classification using data (point cloud) from a LIDAR sensor as input.

View code on GitHub*