TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use.
This application relies heavily on the GPU to perform calculations, therefore a processor with a low core count and low clockspeed is acceptable.
Benchmarks indicate that TensorFlow performs best when the amount of system memory is double (or more) the graphics card memory. So if you have one Titan Xp card (which is 12GB), then we recommended 32GB RAM. If you have two Titan Xp cards (24GB), then we recommended 64GB RAM.
This application relies more on reading data than writing data as it analyzes and processes training datasets. Extreme read rates are not required however, therefore we recommend a 512GB SSD for the operating system and program files. Furthermore, because deep learning requires large datasets to be processed, storing this data locally is important. Therefore a 4TB, 6TB or 8TB hard drive (or multiple hard drives) is recommended depending on your storage needs.
The Titan Xp has become the defacto graphics card for deep learning and TensorFlow offsets all the computing of data to the GPU. For maximum performance, recommend two NVIDIA GeForce GTX Titan Xp graphics cards.
The Z840 is recommended if you require dual processors or dual high performance graphics cards. The Z840 can support up to 6 hard drives or 10 SSDs.
The Z640 is recommended if you require a single high-end processor or single high-performance graphics card. You can always add a second processor and double the memory at a later date using the optional Z640 processor riser. The Z640 can support up to 4 hard drives or 6 SSDs.
The Z440 is recommended if you require a single processor with a low core count or mid-range graphics card. The Z440 can support up to 4 hard drives or 6 SSDs.
For massive computes, we carry NVIDIA Tesla K80 and P100 accelerators installed in worlds leading Cubix Xpander Rackmount series expansion chassis. The top selling, Xpander Rackmount 2, Gen 3 expands one PCIe 3.0 x16 slot in the HP Z Worskation into two dual-slot PCIe 3.0 x16 slots for passively cooled cards. Add 2 4 8 or 16 Tesla compute processors to for extreme compute power to take on any advanced product. Shave hours, days, or weeks off your estimated completion time to maximize your productivity.
For rigorous rendering and advanced deep learning solutions, we carry the Cubix Xpander Desktop Elite series expansion chassis ideal for NVIDIA GTX 1080Ti 11GB or Titan Xp 12GB GPUs. The most popular in the series, Xpander Desktop Elite, Gen 3 - 1500w, expands one PCIe 3.0 x16 slot in the HP Z Worskation into four dual-slot PCIe 3.0 x16 slots for actively cooled cards. The 1200w model also available for a mixture of GPUs and specialty hardware cards, and the 8 and 16 GPU model for a grid computing solution.