# LAMBDA Deep Learning Blade Server and Quad Workstation with 4-10 GPUs

#### Up to 10 GPUs. Fully Customizable. Ubuntu, TensorFlow, PyTorch, Keras, CUDA, and cuDNN pre-installed.

Operating System: Ubuntu 18.04 + Lambda Stack

Software: TensorFlow, PyTorch, Caffe, Keras, CUDA, cuDNN

Processor: 2x Intel Xeon Gold 6230 (20 Cores, 2.10 GHz)

GPUs: 8x RTX 8000 + NVLink

Memory: 768 GB

Operating System Drive: 3.84 TB NVMe

Extra Storage: 4 TB SSD

Warranty & Support: Three years of hardware coverage, plus technical support from a Lambda engineer

$64,790.00 EMAIL FOR MORE INFO Premium Lambda Blade 8x RTX 6000 2x Xeon Gold 5218 (16 Cores) 8x RTX 6000 GPUs 512 GB of Memory 1.92 TB NVMe SSD 4 TB SATA SSD Starting at$ 41,458.00

8x RTX 8000

2x Xeon Gold 5218 (16 Cores)

8x RTX 8000 GPUs

768 GB of Memory

3.84 TB NVMe SSD

4 TB SATA SSD

Starting at

$59,753.00 EMAIL FOR MORE INFO #### Trusted by thousands of customers worldwide #### Researchers and engineers at universities, start-ups, Fortune 500s, public agencies, and national labs use Lambda to power their artificial intelligence workloads. #### Trusted by thousands of customers worldwide #### Researchers and engineers at universities, start-ups, Fortune 500s, public agencies, and national labs use Lambda to power their artificial intelligence workloads. ### Deep Learning Workstation with 4 GPUs ###### GPU workstation with RTX 2080 Ti, RTX 6000, RTX 8000, or Titan V. Ubuntu, TensorFlow, PyTorch, Keras, CUDA, and cuDNN pre-installed. Trusted by Top A.I. Research Groups Lambda Stack comes free with your computer. Machine Learning libraries work out-of-the box and can be updated automatically. Explore Lambda's Research Our research papers have been accepted into the top machine learning and graphics conferences, including ICCV, SIGGRAPH Asia, NeurIPS, and ACM Transactions on Graphics (TOG). Basic Workstation 4x RTX 2080 Ti In Stock (Ships in 2-3 Days) Intel i9-9820X CPU (10 Cores) RTX 2080 Ti (11 GB VRAM) 64 GB Memory 2 TB NVMe (3,500 MB/s Read) Starting at$ 9,139

In Stock (Ships in 2-3 Days)

Intel i9-9920X CPU (12 Cores)

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2 TB NVMe (3,500 MB/s Read)

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$21,563 Max Workstation 4x Quadro RTX 8000 In Stock (Ships in 2-3 Days) Intel W-2195 CPU (18 Cores) RTX 8000 (48 GB VRAM) 256 GB Memory 2 TB NVMe (3,500 MB/s Read) Starting at$ 33,579

RenderNet: A Deep Conv. Network for Differentiable Rendering from 3D Shapes

### HoloGAN: Unsupervised learning of 3D representations from natural images

(Submitted on 2 Apr 2019 (v1), last revised 1 Oct 2019 (this version, v2))

We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Most generative models rely on 2D kernels to generate images and make few assumptions about the 3D world. These models therefore tend to create blurry images or artefacts in tasks that require a strong 3D understanding, such as novel-view synthesis. HoloGAN instead learns a 3D representation of the world, and to render this representation in a realistic manner. Unlike other GANs, HoloGAN provides explicit control over the pose of generated objects through rigid-body transformations of the learnt 3D features. Our experiments show that using explicit 3D features enables HoloGAN to disentangle 3D pose and identity, which is further decomposed into shape and appearance, while still being able to generate images with similar or higher visual quality than other generative models. HoloGAN can be trained end-to-end from unlabelled 2D images only. Particularly, we do not require pose labels, 3D shapes, or multiple views of the same objects. This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
 Comments: International Conference on Computer Vision ICCV 2019. For project page, see this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:1904.01326 [cs.CV] (or arXiv:1904.01326v2 [cs.CV] for this version)

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## Submission history

From: Thu Nguyen-Phuoc [view email][v1] Tue, 2 Apr 2019 10:36:01 UTC (4,497 KB)
[v2] Tue, 1 Oct 2019 10:41:28 UTC (3,832 KB)