Training AMD GPU ROCm PyTorch TensorFlow - Optimizing AI in Datacenter Production

$4,620

  • 4 days
  • Onsite
  • 10 people max.
Ref: FEU953
10 people max.
$4,620
In-company rate: from $7,865 for the whole team
On-site and certification exam on quote
4 days
Onsite

Don't let this gap widen

Why this program matters

  • Without ROCm mastery, teams face training times multiplied by three, recurring GPU failures, and technical debt that delays AI projects.

  • Downtime can cost up to 150 000 euros per day in production.

  • Unpatched flaws expose teams to security incidents and 40 % higher energy consumption.

  • Deployment delays cause lost competitiveness against rivals who fully leverage AMD Instinct GPUs.

Allan BUSI, Learni trainer · AI expert
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.

AMD GPU ROCm PyTorch TensorFlow training objectives

  • Configure ROCm drivers and AMD libraries for Instinct GPU clusters
  • Deploy optimized PyTorch and TensorFlow models on ROCm architecture
  • Optimize training and inference performance in production
  • Monitor AI workloads with AMD monitoring tools
  • Troubleshoot compatibility and scalability issues in datacenter environments
  • Secure ROCm environments against hardware and software vulnerabilities

AMD GPU ROCm PyTorch TensorFlow curriculum: 4 modules over 4 days

Module 1 : ROCm Installation and PyTorch Integration: AMD Instinct Server Configuration (drivers, Docker containers, benchmark validation)

Participants install ROCm on AMD Instinct servers, configure dependencies for PyTorch and verify hardware compatibility. They perform practical work deploying optimized containers, measure initial performance gains and produce a production-ready installation report.

Module 2 : TensorFlow Training on ROCm: Distributed Implementation and Memory Optimization (multi-GPU, profiling, batch tuning)

The day covers running TensorFlow on ROCm with multi-GPU support, using AMD profiling tools and optimizing memory consumption. Concrete vision and NLP model training cases deliver optimized scripts and performance metrics.

Module 3 : PyTorch Inference and Scalability: Container Deployment and CI/CD Pipelines (TorchServe, Kubernetes, monitoring)

Learners deploy PyTorch models for high-performance inference via TorchServe and ROCm, integrate pipelines into Kubernetes and configure real-time monitoring. Practical work on real workloads results in scalable architectures delivered with documentation and automation scripts.

Module 4 : Advanced Troubleshooting and Hardening: Incident Analysis, Hardening and Datacenter Best Practices (AMD logs, updates, encryption)

This final day addresses common ROCm incidents, advanced diagnostic methods and security hardening. Participants resolve real production cases, apply AMD recommendations and deliver a complete maintenance plan with resilience measures.

How AMD GPU ROCm PyTorch TensorFlow skills are assessed

  • MCQ validating ROCm and framework concepts
  • Graded labs on model deployment and optimization
  • Datacenter troubleshooting simulation exercises

How the AMD GPU ROCm PyTorch TensorFlow course is taught

  • Theoretical content focused on AMD Instinct architecture and ROCm
  • Hands-on labs on real GPU servers with measurable metrics
  • Analysis of real company case studies that migrated to ROCm
  • Trainer feedback on incidents and optimizations

AMD GPU ROCm PyTorch TensorFlow training: access and practical arrangements

The Training AMD GPU ROCm PyTorch TensorFlow - Optimizing AI in Datacenter Production program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training AMD GPU ROCm PyTorch TensorFlow - Optimizing AI in Datacenter Production program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training AMD GPU ROCm PyTorch TensorFlow - Optimizing AI in Datacenter Production program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.

The Learni story

Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.

Our method

How Learni guarantees the quality of your AMD GPU ROCm PyTorch TensorFlow training

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

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Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

  • Backpack, notebooks and essential supplies
  • Distributed through our partner charities
  • Included, at no extra cost to you
Enroll and offer a kit
FAQ

AMD GPU ROCm PyTorch TensorFlow: frequently asked questions

Which tools are used in AMD GPU ROCm PyTorch TensorFlow training?+
Hands-on work runs on drivers, docker containers, benchmark validation, multi-GPU, profiling and batch tuning. Those tools come from the AMD GPU ROCm PyTorch TensorFlow program itself, delivered on site over 4 days, so participants practise on the stack they will use at work.
What can participants do after AMD GPU ROCm PyTorch TensorFlow training?+
They can configure ROCm drivers and AMD libraries for Instinct GPU clusters, deploy optimized PyTorch and TensorFlow models on ROCm architecture and optimize training and inference performance in production. Those are the stated objectives of the AMD GPU ROCm PyTorch TensorFlow program at Advanced level, checked by the end-of-session assessment.
What does the AMD GPU ROCm PyTorch TensorFlow program cover, day by day?+
Day 1: ROCm Installation and PyTorch Integration: AMD Instinct Server Configuration (drivers, Docker containers, benchmark validation). Day 2: TensorFlow Training on ROCm: Distributed Implementation and Memory Optimization (multi-GPU, profiling, batch tuning). Day 3: PyTorch Inference and Scalability: Container Deployment and CI/CD Pipelines (TorchServe, Kubernetes, monitoring). Day 4: Advanced Troubleshooting and Hardening: Incident Analysis, Hardening and Datacenter Best Practices (AMD logs, updates, encryption). The AMD GPU ROCm PyTorch TensorFlow program runs over 4 days, delivered on site.
How is AMD GPU ROCm PyTorch TensorFlow training assessed?+
Assessment relies on MCQ validating ROCm and framework concepts, graded labs on model deployment and optimization and datacenter troubleshooting simulation exercises. Learni issues a completion certificate together with the individual assessment report at the end of the AMD GPU ROCm PyTorch TensorFlow program, which an L&D team can file as evidence.
How much does AMD GPU ROCm PyTorch TensorFlow training cost per participant?+
AMD GPU ROCm PyTorch TensorFlow training costs $4,620 per participant. For an in-company team, the flat rate starts at $7,865 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does AMD GPU ROCm PyTorch TensorFlow training take?+
4 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for AMD GPU ROCm PyTorch TensorFlow training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before AMD GPU ROCm PyTorch TensorFlow training?+
Confirmed proficiency in Python, PyTorch and TensorFlow frameworks, advanced Linux administration and GPU parallelism concepts.
Is a certificate issued after AMD GPU ROCm PyTorch TensorFlow training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for AMD GPU ROCm PyTorch TensorFlow training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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