Dell Precision Data
Science Workstation
Intel User andInstallation Guide

Contents

Introduction

Pages 4-5
The way forward
Choosing the right Dell Data Science
Workstation

Pages 10-11

Powering up your performance
Setup and optimizations

Pages 12-13
Appendix
Ubuntu Installation Guide

Pages 14-17
AppendixIntel AI Analytics Kit installation guide

Pages 18-23
References and resources

Pages 24-25

23

Introduction
Capturing opportunities when they present themselves and driving
technological innovations is central to business success, particularly intoday’s unpredictable world. It’s been said, “the measure of intelligence isthe ability to change” then the smartest organizations prioritize their ability
to adapt.
Artificial Intelligence (AI) based on machine learning (ML) and deeplearning (DL) allows hardware to learn from previous computations to
produce reliable, repeatable decisions and results. It gives organizations
that all important adaptability, and can create new products and businessinitiatives, enhance existing markets as well as creating entirely new ones.
But if AI relies on human guidance and intervention to achieve this, howuseful can its impact really be?
By 2024, International Data Corporation(IDC) industry analysts believe the global
machine learning market will grow to a
total of $30.6 billion, attaining a compound
annual growth rate (CAGR) of 43 percent.1
In that same year, analysts also predictthat 143 zettabytes of data will becreated, captured, copied, and consumed
throughout the world.1
For AI, machine learning, and deep learning
developers, it’s an intriguing business
opportunity that challenges them tobuild high-performance applications withflexible deployment options. One hardware
architecture can no longer do it all; we needto take Einstein’s idea on board and change
how we approach the technology and our
mindset towards it.
Dell Technologies has developed theData Science Workstations (DSWs), a
tailored set of Precision workstations
pre-bundled with hardware and software,
designed for machine learning and deeplearning developers. They’re built with thelatest Intel® Xeon® CPUs, ECC memory,
professional graphics and advanced
storage solutions.

4

We have created this step-by-step guideto provide users with a template toquickly set up the OS and Intel® oneAPI
Data Science Software on their Dell
DSWs. There are numerous DSW-certified
and tested configurations, designed tomeet the different development cycles
required by our customers. This rigorous
testing ensures that the configuration ourcustomers purchase will meet, or exceed,
their performance requirements right outof the box.
The Intel® oneAPI CPU-accelerated
software and the Dell Data Science
Workstations are designed to allow thedevelopers to start working on large
datasets with the optimized libraries on thesame day they receive their DSW.

Based on customer feedback,we learned that many data
scientists are interested in how
they can boost performancein the pre-processing and ETLphase of data science. With
data analytics software tools
from Intel, data scientists haveseen 10x performance gains.

We’ve learned from our customers thatmany data scientists spend 70% to 80%of their time cleaning, organizing, andvisualizing their data before going to
production. Interestingly, most solutionsin the market today are focused on the
production phase where deep learning
is prevalent rather than the data science
phase in AI.
We’ve taken this feedback and developed asolution that focuses on machine learningand the pre-processing phase in AI. Using a
combination of optimized Python tools andlibraries from Intel, hardware instruction
sets like AVX512, and increased memory
capacity, you can significantly reduce
the time it takes to develop the models
and algorithms necessary to achieve theinsights you need.
Intel® oneAPI optimized software
reduces the overall cost of your entry
into AI development, as it gives you theperformance needed to utilize today’s AI
analytics software tools without a GPU.

The Dell data science workstation platforms
are purpose-built for data science.
The data science workstation includes
Intel AVX instruction sets and the ultimate
memory capacity needed for data
scientists to iterate on various datasets,
visualize, and analyze complex workloads
at scale. Data Science tasks need thehighest memory5Data Science tasks need the highest
memory span to fit the dataset in-memory
for complex analysis before AI/ML. WithIntel Optane Persistent Memory (PMem),these workstations can fit full datasets andperform at a fraction of the cost of a GPUbased, AI-ready workstation.

The newest 3rd Generation Intel® Xeon
Scalable processors enhance AI, cloudcomputing, security, and many other areas.
Intel has optimized an array of softwaretools, libraries, and frameworks so thatapplications can easily take advantage ofthe latest hardware advances. The results
are impressive.

Figure 2.
Accelerating XGBoost on Spark: CPU Instances vs GPU Instance(Training time. Lower is better)
500
450

GPU init.cost

400
350

Time (seconds)

Dell Data Science Workstation platforms
are purpose-built for data science. The Data
Science Workstation includes Intel AVX
instruction sets and the ultimate memory
capacity needed for data scientists toiterate on various datasets, visualize, andanalyze complex workloads at scale.

1.8x

300
GPU init.cost

250
GPU init.cost

200

GPU init.cost

1.6x

1.6x

150
2.3x

100
50
0
Rapids 0.17
XGBoost on 1
node p4d.24xlargeIntel® AVX instruction sets provide huge
performance boosts to many linear
algebra-based operations used in data
science. With the Intel® AI Analytics
software stack, data scientists canperform AI experiments on small machines
like laptops and then scale out seamlessly
to production servers, IoT and edge
devices such as workstations, as well as
the cloud.

XGBoost 1.3 with
optimizations on
8 nodes c5.metal

Mortgage (600M x 45)

XGBoost 1.3 with
optimizations on
8 nodes c5.metal

HiBench (600M x 50)

Data Loading• Intel - Optimized Modin• Intel® Distribution of Python(Pandas and Numba)

ETL / Data
pre-processing

Classical ML Training /Prediction

Deep Learning Training /Interference• Intel - Optimized Modin• Numply• SciPy• Pandas• Intel® distribution of Python• Optimized XGBoost• Daal4py accelerated
Scikit-learn• Intel - Optimized TensorFlow• Intel - Optimized PyTorch• Intel® Low - precision
Optimization tool
• Model Zoo

Distributed DL Horovod /oneCCL

Distributed processing - Daal4py, Modin / Omnisci / Spark / Ray

DEPLOY
oneAPI
Libaries

Rapids 0.17
XGBoost on 1
node p4d.24xlarge

HiBench (900M x 50)

XGBoost 1.3 with
optimizations on
8 nodes c5.metal

HiBench (1200M x 50)

Figure 3.

DEEP LEARNING

MACHINE LEARNING

x

SCALE

XGBoost 1.3 with
optimizations on
8 nodes c5.metal

Intel® oneAPI AI Analytics Toolkit(databases, file systems,
onject stores, streams)(parquet, orc, csv, json.. orpng, mp4)

Rapids 0.17
XGBoost on 1
node p4d.24xlarge

Figure 2 shows a scenario where the optimized XGBoost running on a Spark clusterof eight c5.metal instances (Intel® Xeon Platinum 8275CL processors) outperforms
an equivalently priced p4d.24large instance (NVIDIA A100 processors).

Figure 1.
Data Sources

Rapids 0.17
XGBoost on 1
node p4d.24xlarge

oneTBB

DPC++

oneDAL

oneCCLIntel-optimized XGBoost
Intel Optimization for PyTorch®

DATA ANALYTICS

Model Zoo for Intel® Architecture

Cloud, on-premise, containers, VMs, ISV integrations
oneMKLIntel-optimized Scikit-learn
Intel® Optimization for TensorFlow

oneDNN

Data scientists create multiphase, end-to-end pipelines for AI and ML applications.
The phases include data ingestion, data cleaning, data exploration, and featureengineering followed by prototyping, model building, and finally, model deployment.

Intel® Distribution of Modin

Intel® Low Precision
Optimization Tool

OmniSci Backend

CORE PYTHONIntel-Opt NumPy

Intel-Opt SciPy

Intel-Opt Numba

DPPY

The Intel® oneAPI AI Analytics Toolkit (AI Kit) provides high-performance APIs and Python
packages to accelerate the phases of these pipelines.

67

Figure 4

Figure 5

Relative Throughput improvement with oneDNN optimizations (training)

Unleash Python* DataScience Productivity and PerformanceIntel® oneAPI AI Analytics Toolkit

MachineLearning

Up to3

faster ETL

faster performancecompared to Pandas

than stock scikit-learn on geo mean of 17 ML workloads

2.5

38X

221X

2

Data Preprocessing

Model Training

Model Inference

1.5
1compared to
NVIDIA* GPU

Use Case —Census 2020
dataset workload

compared to
AMD* CPU

21X

38X

S
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ip
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a
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.0

4.5X

faster inferencecompared to
NVIDIA* GPU

tf2

1.47X

tf2

1.59X

be

Avg oftra

Avg of

ns

de
w

id

e_

faster fit & predict compared to...

Alternative
HardwareComparisonst5(FP32 vs Int8)

ne(FP32 vs Int8)

0

re
s

faster quantized
inferences

faster DLRM
inference

ep(FP32 vs BF16)

0.5

_d

faster DLRM training

2.8X

rg
e

2.8X

1.4X

Up to

_la

Up to

Up to

Deep
Learning

For training, up to 2.4x performance gains were observed with previous TensorFlow 1.x
graph models and the newer TensorFlow 2.x eager execution-based models.

faster fit & predict

faster ETL

40% accelerated performance
See all benchmarks configurations - https://techdecoded.intel.io/resources/deliver-blazing-fast-python-data-science-and-ai-performance-on-cpus-with-minimal-code-changes
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly availableupdates. See backup for configuration details. No product or component can beabsolutely secure.
Intel technologies may require enabled hardware, software or service activation.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

Intel® oneAPI Python Data Science Productivity and Performance benchmarks.
Maximise performance with theIntel® oneAPI AI Analytics Toolkit
Achieving peak performance with Python
across a variety of architectures is criticalto the success of modern data science.
Python is a powerful, scalable, and easyto-use language, but it’s not built from theground up for blazing-fast performance.
Many developers rely on vendor-specificoptimizations for frameworks and libraries
such as TensorFlow, PyTorch, or scikitlearn to achieve the speeds they need on
their hardware of choice.

8

However, when new hardware emerges or
the market evolves to require deployment
on additional hardware types, they haveto find new optimizations to get similar
results.
That’s where the Intel® oneAPI AI Analytics
Toolkit (AI Kit) comes in.
The toolkit maximizes performance frompreprocessing to ML and DL training andinference, on Dell Technologies and Intel
hardware architectures commonly used
today, as well as those set to become
industry standards in the future.

The Intel® oneAPI AI Analytics Toolkit
provides optimized Python libraries,
deep learning frameworks, and alightweight parallel dataframe—all built
using oneAPI libraries—to maximize
performance for end-to-end data
science and AI workflows on Dell Data
Science Workstations.
The toolkit allows data scientists andAI developers to get the latest Intel
deep learning and machine learning
optimizations from a single resourcewith seamless interoperability and out-ofthe-box performance.

91. The way forward – Choosing theright Dell Data Science Workstation
Our Dell Precision Data Science workstations come in a variety of form
factors to work seamlessly in your environment. It all depends on what
your needs are.
Find more configuration information by visiting the Dell Precision
Workstation Solutions webpage.

Precision for your Data
Science requirements

Precision 7560:
Dell’s most powerful 15”
mobile workstation is AI
ready, with Intel® Xeon® CPUs.

Precision 7760:
The most powerful Precisionmobile workstation is AI
ready, with Intel® Xeon® CPUs.

Precision 5820 Tower:
Ideal for cognitive solution
development and inference
applications, with Intel®
Xeon® CPUs.

Precision 7920 Tower and Rack:
Handles learning model training and larger
solution frameworks with ease, with Intel® Xeon®
CPUs and Intel® Optane™ technology.

1011

2. Powering up your performance –Setup and Optimizations (optional)
The following optimizations are recommended to increase theperformance and productivity for machine learning and deeplearning workloads.

Step 1. Disable idle user power management configuration
To complete this action, we recommend disabling power management settings for Idle Usercase, since in most cases during installation and development, the machine will download thesoftware/dataset over the internet or run ML/DL workflow unattended for an extended periodof time.

Step 4. Is swap active?
sudo swapon --show
If it is, it will show something similar to the below

Disable power management settings with the command below.
/usr/bin/gsettings set org.gnome.settings-daemon.plugins.power sleep-inactive-ac-timeout 0
Step 2. Disable Hibernate settings
There is no advantage in enabling “hibernate” on Dell Data Science workstations, quite simply
because it is unnecessary and consumes valuable storage space. Disable it as per below.
sudo systemctl mask hibernate.target
Step 3. Remove swap file
In addition, we recommend removing the “swap file”. Swap file sizes are typically 1.5x the sizeof installed RAM. On large RAM systems this can be a prohibitive amount of SSD storage.

12

Step 5. Deactivate the swap
sudo swapoff -v /swapfile
Step 6. Remove it from /etc/fstab
sudo sed -i ′/^\/swapfile/d′ /etc/fstab
Step 7. Delete the actual filesudo rm /swapfile13

Appendix: Ubuntu Installation Guide
In order to start a fresh install you will need to disable “Secure Boot” inthe bios. See steps below”

These steps require reformatting thestorage drive and will wipe out any existing
drive content, so be sure to save any
important drive content before starting.

D.

Click Yes.

E.

Click OK

Unless instructed otherwise, do NOT use‘sudo’, and use a standard user account in
a Bash Linux shell (i.e., terminal window).
Step 1. Download Ubuntu 20.04 media here.
Step 2. Create installation media
A.
Download and run Rufus.
B.
Insert your USB media device.
C.
Configure as shown below
		and click Start.

Continued 

1415

Appendix: Ubuntu Installation Guide
In order to start a fresh install you will need to disable“Secure Boot” in the bios. See steps below”

Step 4. Install Ubuntu 20.04.
A.
B.

Step 3. Configure system BIOS.
A.

Power up or reboot system. When the Dell logo appears, immediately tap F2 		
repeatedly to load the one-time boot menu.

B.

Disable Secure Boot
(1)
Settings - Secure Boot - Select Disabled

C.

Change SATA setting to AHCI
(1)
Settings - System Configuration - SATA Operation - Select AHCI

If yours is a Precision Mobile Workstation, skip this step and go to step 3.0 (e).
(1)
Settings - Advanced Boot Options - Check Enable Legacy Option ROMs
(2)
Settings - Boot Sequence - Select Legacy(3)Note: This will cause systems equipped with NVMe hard disk carriages to 				
produce a flashing yellow disk health light. This is normal and is the result of		
using the Legacy boot option.
(4)
Note: If this is a Precision Mobile Workstation please keep UEFI selected.

C.
D.
E.
F.
G.
H.
I.
J.
K.
L.
M.
N.
O.
P.
Q.
R.
S.

Insert USB installation media device created in Appendix Step 2.
Power up or reboot system. When the Dell logo appears, immediately tap F12 		
repeatedly to load the one-time boot menu.
Under UEFI Boot, select your USB installation media device and press <Enter>.
After the installer loads, select preferred language and click Install Ubuntu.
If you boot into Ubuntu, you will need to double click on the Install Ubuntu 20.04.4 icon.
Select keyboard layout and click Continue.
If asked about WIFI, select I don’t want to connect to a WIFI network right now.
If asked about updates and other software, select Minimal Installation.
Uncheck “Download updates while installing Ubuntu.”
Uncheck “Install third-party software for graphics and Wi-Fi hardware and 		
additional media formats.”
Click Continue.
Select “Erase disk and install Ubuntu.”
Click Install Now.
Confirm writing changes to disk by clicking Continue.
Select time zone and click Continue.
Enter name, computer name, username, password and click Continue.
Ubuntu will install for the next several minutes. When it finishes, click Restart Now.
Remove USB installation media device.
Log into the system with username and password entered during installation.

D.

E.

16

Step 5. Proceed with instructions in Dell Data Science Workstation Installation Guide,
Section 3 to install the Intel oneAPI Data Science Software stack.

Click Apply, OK, Exit.

17

Appendix: Intel Installation Guide
Intel AI Analytics Kit 2021.4 Update 2 for OEMs installation guide.
Intel AI Kit is an accelerated integrated environment of popular Machine
Learning Python libraries that is optimized and curated by Intel. It
provides Intel optimized versions of packages for the best possible
performance on Intel Architecture.

Step 1: Update your system
Launch a terminal (CTRL+ALT+T) and enter:sudo apt update && sudo apt upgrade -yComponents of This Toolkit

Step 2: After the update a reboot will be required, enter:sudo reboot
Note: if you are in WSL2 exit the session and relaunch.

•

Step 3: Launch a terminal (CTRL+ALT+T) and enter:

•
•

•
•
•

PyTorch*: The Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
is included in PyTorch as the default math kernel library for deep learning. See thisarticle on the Intel® Developer Zone for more details.
Intel® Optimization for TensorFlow*: This version integrates primitives from the Intel®
Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) into the TensorFlow
runtime for accelerated performance.
Intel® Distribution for Python*: Get faster Python application performance rightout of the box, with minimal or no changes to your code. This distribution is
integrated with Intel® Performance Libraries such as the Intel® oneAPI Math Kernel
Library and the Intel® oneAPI Data Analytics Library. The distribution also includes
daal4py, a Python module integrated with the Intel® oneAPI Data Analytics Libraryas well as the Python Data Parallel Processing Library (PyDPPL), a light weight
Python wrapper for Data Parallel C++ and SYCL that provides a data parallelinterface and abstractions to efficiently tap into device management features ofCPUs and GPUs running on Intel® Architecture.
Intel® Distribution of Modin*, which enables you to seamlessly scale preprocessing
across multi nodes using this intelligent, distributed dataframe library with an
identical API to pandas.
Low Precision Optimization Tool: Provide a unified, low-precision inference interface
across multiple deep learning frameworks optimized by Intel with this open-source
Python library.
Intel® Extension for Scikit-learn*: a seamless way to speed up your Scikit-learn
application using the Intel® oneAPI Data Analytics Library (oneDAL).
Patching scikit-learn accelerates stock scikit by single line change.

Standard Python installations are fully compatible with the AI Kit, but the Intel®
Distribution for Python* is preferred.

18

Installation
This instance was built using Ubuntu 20.04 LTS. If you are on Windows 11, jump toaddendum to setup the environment and come back here to Step 1.

wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18273/IntelAIkit-2021.4.1-Linux-x86_64.sh
Note: the download is approximately 1.8GB, a stable network connection is desired.
Step 4: Change the permissions of the resulting download so that it is an executable.
chmod 755 Intel-AIkit-2021.4.1-Linux-x86_64.shShould the version change the product page is here.
Step 5: Install the Intel AIkit:./Intel-AIkit-2021.4.1-Linux-x86_64.sh
Follow the steps and make sure to choose yes to the final question “Do you wish the
installer to initialize the Intel-AIkit”
Step 6: You can close the terminal and launch a new one or enter:source .bashrc
This will result in the base conda environment being activated.
Enter:
conda env list
Base * /home/user/intel-aikit should be the result
Enter: conda list
conda list
You should see a list of packages with many coming from the Intel channel. These are
the optimized packages.
Note: This collection of packages has been validated against each other for functionality.
At the time of publishing, this is a stable environment and should be treated as the basepackage environment. Any modifications to this collection should be done in a cloned
environment so that changes can be isolated and you can easily get back to a known good.

19

Appendix: Intel Installation Guide
Intel AI Analytics Kit 2021.4 Update 2 for OEMs installation guide.
Intel AI Kit is an accelerated integrated environment of popular
Machine Learning Python libraries that is optimized and curated byIntel. It provides Intel optimized versions of packages for the best
possible performance on Intel Architecture.

Getting Started
In this section we will:• Create a new environment and add some additional packages.
• Clone the Intel samples repository.
• Run a sample project using a Jupyter Notebook.
Create New Environment
Step 1: Launch a terminal (CTRL+ALT+T) and enter:conda create –-clone base –-namefor example:
conda create -–clone base –-name jupyter
Step 2:
Conda env list
You should see two environments now. The * denotes the active environment.
Activate the new environment:
Conda activate jupyter
If you enter ‘conda env list’ the asterisks should be next to the new environment name andthe prompt should indicate the environment name as well.

Note: this takes a while as conda will check for package conflicts.
Step 4: Clone the Intel oneAPI Samples Repository, Git will likely not be installed
so to install it enter:sudo apt install git
git clone https://github.com/oneapi-src/oneAPI-samples.git
current repo size is ~410M
Step 5:Enter:
jupyter lab
The browser should auto launch. Then on the panel on the left navigate to:~/oneAPI-samples/AI-and-Analystics/Getting-Started-Samples/INC-Sample-forTensorflow/
Double click:
Inc_sample_tensorflow.ipynb

Step 3: Add missing packages.
We need several packages for the getting started samples, let’s add those knowing that if we run
into issues we can always delete this environment and get back to our clean base environment.
Enter:
conda install -c conda-forge jupyterlab
Note: there will be some warnings but they are safe to ignore.
Enter:
Conda install -c intel neural-compressor

20

21

Appendix: Intel Installation Guide
Intel AI Analytics Kit 2021.4 Update 2 for OEMs installation guide.
Intel AI Kit is an accelerated integrated environment of popular
Machine Learning Python libraries that is optimized and curated
by Intel. It provides Intel optimized versions of packages for thebest possible performance on Intel Architecture.

As you execute the notebook you will see that your Data Science Workstation supports thelatest set of instructions and has the ability to take advantage of INT8 as opposed to FP32
increasing throughput and reducing latency while maintaining identical accuracy.

Addendum: WSL2 on Windows 11:
Step 1: Tap the Windows Key and enter update, windows update will be the first match. Go
through that process and reboot.
Step 2: Launch windows terminal with administrative privileges. Tap the Windows key andenter terminal. The down carrot on the right will expose the admin privilege option.
Step 3: At the prompt enter: wsl - install (two dashes)
This will automatically install Ubuntu 20.04
Step 4: Restart -- The install actually takes place here and after the reboot, this takes acouple of minutes.
Step 5: Ubuntu will launch automatically, follow the steps to create a new user. It’s a goodidea to have the identical username for Windows as you do Ubuntu.
Step 6: Jump back up to step 1 and follow along, even the update steps.
Note: a reboot in WSL2 is exiting the WSL2 session. To relaunch WSL2 tap the Windows
key, enter terminal and then click on the down carrot and choose Ubuntu. This is a great
way to have multiple terminal sessions in a tabbed environment.

Please explore other Getting Started installation guides with Intel ›22

23

References & Resources

References

Resources

1. Online installation step by step - https://youtu.be/SXTS_hURiAQ -0

1. Artificial Intelligence (AI) technologies powered by Dell Precision workstations

2. Online installation step by step - https://youtu.be/VC_ucczGR_Y
3. (on Intel site) https://software.intel.com/content/www/us/en/develop/tools/oneapi/aianalytics-toolkit/download.html?operatingsystem=linux
4. Installation guide for Package Managers (YUM, APT, NuGet, etc):
https://software.intel.com/content/www/us/en/develop/documentation/installation-

https://www.delltechnologies.com/en-us/ai-technologies/index.htm
2. Here’s a link to educational videos for Intel oneAPI AI Analytics Toolkit:
a. Intel Distribution for Python: https://www.intel.com/content/www/us/en/developer/
videos/fast-scalable-analytics-ml-distribution-for-python.html#gs.e9q58g [intel.com]
b. Intel Distribution for Modin: https://techdecoded.intel.io/essentials/achieve-highperformance-scaling-for-e2e-machine-learning-and-data-analytics-workflows/?elq_

guide-for-intel-oneapi-toolkits-linux/top/installation/install-using-package-managers/

cid=3630800_ts1634864550409&erpm_id=5537033_ts1634864550409#gs.e9q4km

yum-dnf-zypper.html[techdecoded.intel.io]

5. Installation guide for installing with Conda Package Manager:
https://software.intel.com/content/www/us/en/develop/articles/installing-ai-kit-withconda.html#gs.0lmif8
6. Installation via Docker: https://hub.docker.com/r/intel/oneapi-aikitc. Intel Optimization for PyTorch: https://techdecoded.intel.io/essentials/optimizethe-latest-deep-learning-workloads-using-intel-optimized-pytorch/?elq_cid=3630800_
ts1634865655884&erpm_id=5537033_ts1634865655884#gs.e9qlev [techdecoded.intel.io]
d. Intel Optimization for TensorFlow: https://techdecoded.intel.io/essentials/inteloptimization-for-tensorflow-tips-tricks-for-aihpc-convergence/?elq_cid=3630800_
ts1634865961834&erpm_id=5537033_ts1634865961834#gs.e9qpir [techdecoded.
intel.io]e. Intel Optimization for XGBoost: https://techdecoded.intel.io/essentials/
maximize-your-cpu-resources-for-xgboost-training-and-inference/?elq_cid=3630800_
ts1634866041986&erpm_id=5537033_ts1634866041986#gs.e9qqo8 [techdecoded.intel.io]

24

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Learn more about Dell Precision
workstation solutions

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