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DO NOT DISTRIBUTE
Paperspace
www.paperspace.com
Paperspace
Serverless AI for the future of intelligence.
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Introduction
Deep Learning platform built for developers.
Infrastructure automation and software layer to build
intelligent applications.
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A new generation of AI
developers require a rethinking
of tooling and workflows.
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Why this matters
Developers spend 75% of their time
managing infrastructure.
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So what’s the underlying problem?
The cloud was built for a different use-
case (web servers) and a different
audience (DevOps).
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The DevOps ecosystem is rich
Traditional Web Services Deep Learning
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?
?
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+ 100s more
?
storage, CDN, deploy, monitor, VPC, data, notebooks, train, visualize,
load balance, IPsec, CI/CD, DNS ... collaborate, version, hyperparameters ...
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The key to solving this problem
is finding the right layer of
abstraction.
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Put Uber/Facebook-grade
AI platform in the hands of every developer
There is a huge disconnect between modern business
objectives and the DL tools that can fulfill them.
Business Objective Infrastructure
• BI • GPUs
• Prediction Closing the Gap • Datastore
• Optimization • Algorithms (CNNs, RNNs, ...)
• Recommender systems • Frameworks (Pytorch,
• Any heuristic TensorFlow, etc)
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Paperspace abstracts powerful
infrastructure behind a simple
software layer making cloud
ML as easy as modern web
services.
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A complete platform for modern deep learning
Ingest → Train →
Analyze → Deploy
+
Manage, collaborate, share
• Fully-managed GPU infrastructure
• Unified dev experience
• 1 click Jupyter Notebooks/Lab
• API & language integrations
• ACL/team controls
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GRAI° Model building AI orchestration fabric
• Job queuing / management
Frameworks Tooling Data
• Cloud agnostic
• Accelerator architecture native
(GPU, FPGA, ASIC, TPU, etc)
Keras Pytorch TensorFlow Jupyter Python Quilt
• Unified compute
• Extensible GRAI° AI orchestration fabric
• Built on best practices
Job queuing Unified compute Container Data Encryption /
(containers, kubernetes, and data deployment management key managment
policies)
Bare-metal VPS Private cloud (VPC) Public Cloud
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Cloudscale with a single line of code
Gradient Toolstack
> import paperspace as ps
# Run job on GPU cluster
> ps({‘Type’: ‘TPU’, ‘container’: ‘TensorFlow’ ... }) GRAI Framework
Cloud Orchestration & Automation
Connecting modern ML and the cloud
Cloud Infra
by converting infrastructure into
code.
Raw compute is not sufficient. Network . Storage . Compute
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The Pipeline
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Remarks from the trenches
Trends:
1. Chip renaissance
2. Evolution of ML/AI in practice
3. Consolidation around best practices
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Chip renaissance
• Graphcore
• Cerebras
• Nervana
• Wave
• Google TPU
...
The big question today is whether
accelerator architectures will follow
commodity CPU x86 or lead to a
golden era for high-end, use-specific
hardware.
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The evolution of ML/AI in practice
2016 2018
Consumable API → Refit the Model → Model as core IP
• Clarifai • Paperspace
• AWS Rekognition • Algorithmia
• Google Cloud Vision • FloydHub
• MS cognitive services • ClusterOne
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Consolidation
around best
practices
• Containerization
• Jupyter
• Job runner architecture
• Pipeline
• etc.
18 .Paperspace hello@paperspace.com
20 Jay St. Suite 312 www.paperspace.com
Brooklyn, NY 11201 (718) 619 4325
Thank you.
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