The OpenVINO™ integration with TensorFlow enables you to speed up the TensorFlow workflow by adding just two lines of code. Enhance performance on Intel platforms while using the familiar TensorFlow APIs. Download this whitepaper to get started.
Do sign up and get the white papers Learn to Accelerate TensorFlow on Intel® Architecture with Minimal Code Changes
This article is taken from Intel “Efficient Heterogeneous Parallel Programming Using OpenMP”. In this article, we will show you how to do CPU+GPU asynchronous calculations using OpenMP.
In some cases, offloading computations to an accelerator like a GPU means that the host CPU sits idle until the offloaded computations are finished. However, using the CPU and GPU resources simultaneously can improve the performance of an application. In OpenMP® programs that take advantage of heterogenous parallelism, the master clause can be used to exploit simultaneous CPU and GPU execution. In this article, we will show you how to do CPU+GPU asynchronous calculation using OpenMP.
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…..The Intel® oneAPI DPC++/C++ Compiler was used with following command-line options:
‑O3 ‑Ofast ‑xCORE‑AVX512 ‑mprefer‑vector‑width=512 ‑ffast‑math ‑qopt‑multiple‑gather‑scatter‑by‑shuffles ‑fimf‑precision=low
‑fiopenmp ‑fopenmp‑targets=spir64=”‑fp‑model=precise”…..
Intel: Efficient Heterogeneous Parallel Programming Using OpenMP (Best Practices to Keep the CPU and GPU Working at the Same Time)
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OpenMP provides true asynchronous, heterogeneous execution on CPU+GPU systems. It’s clear from our timing results and VTune profiles that keeping the CPU and GPU busy in the OpenMP parallel region gives the best performance. We encourage you to try this approach.
This is an interesting 3-part article on OpenVINO Deep Learning Workbench.
Pruning deep learning models, combining network layers, developing for multiple hardware targets—getting from a trained deep learning model to a ready-to-deploy inference model seems like a lot of work, which it can be if you hand code it.
The No-Code Approach to Deploying Deep Learning Models on Intel® Hardware
With Intel® tools you can go from trained model to an optimized, packaged inference model entirely online without a single line of code. In this article, we’ll introduce you to the Intel® toolkits for deep learning deployments, including the Intel® Distribution of OpenVINO™ toolkit and Deep Learning Workbench. After that, we’ll get you signed up for a free Intel DevCloud for the Edge account so that you can start optimizing your own inference models.
For more information, see The No-Code Approach to Deploying Deep Learning Models on Intel® Hardware
If you are using OneAPI Environment, and if you are having this issue
ModuleNotFoundError: No module named 'torch'Here are some steps, you may wish to use to troubleshoot.
Make sure you activated the oneAPI environment using below command
% source /usr/local/intel/oneapi/2021.3/setvars.sh
:: initializing oneAPI environment ...
-bash: BASH_VERSION = 4.2.46(2)-release
:: clck -- latest
:: compiler -- latest
:: dal -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: inspector -- latest
:: intelpython -- latest
:: ipp -- latest
:: itac -- latest
:: LPOT -- latest
:: mkl -- latest
:: modelzoo -- latest
:: mpi -- latest
:: pytorch -- latest
:: tbb -- latest
:: tensorflow -- latest
:: oneAPI environment initialized ::
You might want to check the conda environment
% conda info --envs
# conda environments:
#
myenv /myhome/melvin/.conda/envs/myenv
myfsl /myhome/melvin/.conda/envs/myfsl
base * /usr/local/intel/oneapi/2021.3/intelpython/latest
2021.3.0 /usr/local/intel/oneapi/2021.3/intelpython/latest/envs/2021.3.0
myoneapi /usr/local/intel/oneapi/2021.3/intelpython/latest/envs/myoneapi
pytorch /usr/local/intel/oneapi/2021.3/intelpython/latest/envs/pytorch
pytorch-1.8.0 /usr/local/intel/oneapi/2021.3/intelpython/latest/envs/pytorch-1.8.0
tensorflow /usr/local/intel/oneapi/2021.3/intelpython/latest/envs/tensorflow
tensorflow-2.5.0 /usr/local/intel/oneapi/2021.3/intelpython/latest/envs/tensorflow-2.5.0
/usr/local/intel/oneapi/2021.3/pytorch/1.8.0
/usr/local/intel/oneapi/2021.3/tensorflow/2.5.0
Activate Pytorch
% conda activate pytorch
% python
% (pytorch-1.8.0) [user1@node1 ~]$ python
Python 3.7.10 (default, Jun 4 2021, 06:52:02)
[GCC 9.3.0] :: Intel Corporation on linux
Type "help", "copyright", "credits" or "license" for more information.
Intel(R) Distribution for Python is brought to you by Intel Corporation.
Please check out: https://software.intel.com/en-us/python-distribution
>>> import torch
If you are still having the error “ModuleNotFoundError: No module named ‘torch’ “
You may want to install directly if you have root access
% conda install pytorch torchvision cpuonly -c pytorchIf not, you may want to create a private environment similar to Creating Virtual Environment with Python using venv
References:
Intel® Edge AI Certification training courses can be started and completed at no charge. To get officially certified and receive a badge, you must complete the assessment and review process, which costs $99 for one year. Follow up with an annual recertification course to update your skills and credentials.
Certification training includes:
For more information and to Sign up….. Click Here
Heterogeneous computing comes with the challenge of designing code that can work in multi-processor/accelerator environments. Developers need to be equipped with the right set of metrics to make informed design and optimization decisions that take advantage of target hardware.
In Part 1 of this 2-part webinar series, Technical Consulting Engineer Cory Levels focuses on designing software for efficient offload from CPUs to GPUS—even before final hardware is available—using Intel® Advisor. Using a walkthrough of an ISO 3DFD example (3D isotropic Finite Difference), you will learn how to:
For More information, do take a look at the Intel Site Here.
Various processors and pieces of code are often compared to brains, but neuromorphic chips work to much more directly mimic neurological systems through the use of computational “neurons” that communicate with one another. Intel’s first-generation Loihi chip, introduced in 2017, has around 128,000 of those digital neurons. Over the ensuing four years, Loihi has been packed into increasingly large systems, learned to touch and even been taught to smell.
Intel Unveils Loihi 2, Its Second-Generation Neuromorphic Chip, HPCWire
Now, it’s getting a new family member: Loihi 2. In its press release, Intel said that years of testing with the first-generation Loihi chip helped them to design a second generation with up to ten times the processing speed; up to 15 times greater resource density; and up to a million computational neurons per chip – more than seven times those in the first generation. Intel reports that early tests have shown that Loihi 2 required more than 60 times fewer ops per inference when running deep neural networks as compared to Loihi 1 (without a loss in accuracy).
The Detailed Information can be found at Displaying MPI Debug Information
The I_MPI_DEBUG environment variable provides a convenient way to get detailed information about an MPI application at runtime. You can set the variable value from 0 (the default value) to 1000. The higher the value, the more debug information you get.
High values of I_MPI_DEBUG can output a lot of information and significantly reduce performance of your application. A value of I_MPI_DEBUG=5 is generally a good starting point, which provides sufficient information to find common errors.
Displaying MPI Debug Information
To redirect the debug information output from stdout to stderr or a text file, use the I_MPI_DEBUG_OUTPUT environment variable
$ mpirun -genv I_MPI_DEBUG=5 -genv I_MPI_DEBUG_OUTPUT=debug_output.txt -n 32 ./mpi_programI_MPI_DEBUG Arguments
| <level> | Indicate the level of debug information provided. |
| 0 | Output no debugging information. This is the default value. |
| 1 | Output libfabric* version and provider. |
| 2 | Output information about the tuning file used. |
| 3 | Output effective MPI rank, pid and node mapping table. |
| 4 | Output process pinning information. |
| 5 | Output environment variables specific to the Intel® MPI Library. |
| > 5 | Add extra levels of debug information. |
| <flags> | Comma-separated list of debug flags |
| pid | Show process id for each debug message. |
| tid | Show thread id for each debug message for multithreaded library. |
| time | Show time for each debug message. |
| datetime | Show time and date for each debug message. |
| host | Show host name for each debug message. |
| level | Show level for each debug message. |
| scope | Show scope for each debug message. |
| line | Show source line number for each debug message. |
| file | Show source file name for each debug message. |
| nofunc | Do not show routine name. |
| norank | Do not show rank. |
| nousrwarn | Suppress warnings for improper use case (for example, incompatible combination of controls). |
| flock | Synchronize debug output from different process or threads. |
| nobuf | Do not use buffered I/O for debug output. |
References:
What is included in the Intel oneAPI AI Analytics Toolkit? For more information, do take a look at Intel OneAPI Al Analytics Toolkit
Command Line Installation
wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18040/l_AIKit_p_2021.3.0.1370_offline.sh
sudo bash l_AIKit_p_2021.3.0.1370_offline.shInstallation Instruction
Step 1: From the console, locate the downloaded install file.
Step 2: Use $ sudo sh ./<installer>.sh to launch the GUI Installer as the root.
Optionally, use $ sh ./<installer>.sh to launch the GUI Installer as the current user.
Step 3: Follow the instructions in the installer.
Step 4: Explore the Get Started Guide.
References:
What is included in the OneAPI Installer? For more information, do take a look at Get the Intel® oneAPI HPC Toolkit
wget https://registrationcenter-download.intel.com/akdlm/irc_nas/17912/l_HPCKit_p_2021.3.0.3230_offline.sh
sudo bash l_HPCKit_p_2021.3.0.3230_offline.shInstallation Instruction:
References:
The Linux Cluster 