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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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The Training: Mastering the Deployment and Optimization of AI Applications on TPU 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 Training: Mastering the Deployment and Optimization of AI Applications on TPU 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 Training: Mastering the Deployment and Optimization of AI Applications on TPU 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.
Overview of hardware accelerators for AI. Architecture and specifics of Tensor Processing Units. Creating an account and configuring the Cloud environment (Google Cloud/Colab) to use TPUs. Introduction to TensorFlow and PyTorch with TPU. First code benchmarks.
Optimization principles for TPU models: variable formats, distribution strategies, specific limitations. Step-by-step migration of a TensorFlow/PyTorch model for training on TPU. Handling large-scale dataset pipelines, batch management, checkpoints. Practical exercises: adapting classification/convolutional models.
Deploying trained models on TPU for production inference. Performance monitoring, profiling with TensorBoard and Google Cloud tools. Analysis of common errors (OOM, numerical instabilities, API errors). Best practices for cloud cost management. Case studies and final optimization workshop for an AI project on TPU.
Target audience
Developers, data engineers, and researchers wishing to accelerate the training and inference of artificial intelligence models using TPUs
Prerequisites
Good knowledge of Python, initial understanding of neural networks (TensorFlow, PyTorch), basics of hardware architectures for AI
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