NobleProg Training Place, City Albuquerque, NM - Regus at Marquette Avenue

Albuquerque, NM - Regus at Marquette Avenue

500 Marquette Ave NW, Albuquerque, United States, 87102

The venue is easily accessible by car via I‑25 and I‑40, with ample parking available in nearby public garages and surface lots. For those arriving from out of town, Albuquerque International Sunport is located just 5 miles away, providing convenient air travel access. Additionally, the location is well-served by public transit, within walking distance of several ABQ RIDE bus routes and near the Downtown Rail Runner station.

Santa Fe, NM - Regus at Washington Avenue

150 Washington Avenue, Santa Fe, United States, 87501

The venue is easily accessible by car via I‑25, with secure parking available both on site and in nearby private garages. For those traveling by air, Albuquerque International Sunport (ABQ) is located approximately 60 miles to the south, with shuttle and rental car options readily available. Public transit is also a convenient option, with multiple Santa Fe Trails bus routes and the Rail Runner shuttle stop within walking distance.

Explore Our Courses

Administration of CUDA

 35 Hours

This instructor-led, live training in New Mexico (online or onsite) is aimed at beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.

By the end of this training, participants will be able to:

  • Understand the architecture, components, and capabilities of CUDA.
  • Install and configure CUDA environments.
  • Manage and optimize CUDA resources.
  • Debug and troubleshoot common CUDA issues.
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GPU Programming with CUDA and Python

 14 Hours

This instructor-led, live training in New Mexico (online or onsite) is aimed at intermediate-level developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.

By the end of this training, participants will be able to:

  • Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
  • Create, compile and launch custom CUDA kernels.
  • Manage GPU memory.
  • Convert a CPU based application into a GPU-accelerated application.
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Testimonials (1)

Very interactive with various examples, with a good progression in complexity between the start and the end of the training.

Jenny - Andheo

Course - GPU Programming with CUDA and Python

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CUDA (Compute Unified Device Architecture) Consulting
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