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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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Don't let this gap widen
Sans maîtrise de l'optimisation et de l'accélération de l'inférence deep learning avec TensorRT, vos modèles gaspillent jusqu'à 80 % des ressources GPU, avec des latences multipliées par 5 à 10.
Une équipe typique perd 40 000 € annuels en coûts cloud superflus et voit 65 % de ses déploiements en production échouer par sous-performance.
Cela compromet la scalabilité des applications embarquées, érode la compétitivité business et menace les promotions des ingénieurs IA incapables de livrer en temps réel.
Chaque trimestre sans expertise aggrave les risques, laissant vos concurrents dominer le marché.
The Optimiser et Accélérer l'Inférence de Modèles Deep Learning avec TensorRT training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Optimiser et Accélérer l'Inférence de Modèles Deep Learning avec TensorRT training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Optimiser et Accélérer l'Inférence de Modèles Deep Learning avec TensorRT training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Présentation de la plateforme NVIDIA, rôle de TensorRT dans l’écosystème, cas d’usage. Concepts fondamentaux d’inférence optimisée. Architecture interne de TensorRT : builders, engines, calibrators, plugins. Premiers benchmarks de base.
Conversion de modèles TensorFlow/PyTorch en ONNX. Manipulation et conversion ONNX-to-TensorRT. Techniques d’optimisation : pruning, fusion d’opérations, quantification INT8, mixed precision. Introduction à l’API Python et C++. Déploiement sur GPU, tests de performance, profiling avec trtexec.
Intégration de TensorRT dans un pipeline applicatif complexe (serveur et embarqué). Tests avancés, gestion des erreurs et limitations. Optimisation sur-mesure via plugins personnalisés. Études de cas pratiques (vision, NLP, etc). Résolution des goulots et tuning selon le matériel (Jetson, T4, A100). Conseils pour la maintenance et le monitoring à long terme.
Target audience
Ingénieurs IA, data scientists, développeurs d'applications embarquées nécessitant des performances optimales en inférence deep learning
Prerequisites
Maîtrise du deep learning (concepts, frameworks comme TensorFlow ou PyTorch), notions d'optimisation logicielle, expérience avec CUDA recommandée
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