# Index

# Workspace Setup

Once you're done with the framing phase of the project, as explored in the previous guides of this section, you can start setting up your workspace in order to proceed to the next steps of the data science process, where you will explore the data, train Machine Learning models on them, and deploy those models in order to make useful predictions.

There are two ways to set up your workspace: either in your local machine or in the cloud.

To set up a local workspace is a must-have skill for a data science practitioner, so we recommend you to try to go through a Local Setup at least once (unless you have a very old/weak computer).

Probably in the future, you'll need to train bigger models that require more memory, costly CPU operations on raw data, and GPU acceleration for the training phase. In the Cloud Computing section we give you an overview of the several options you can consider to overcome the limitations of your local machine.

# Local Setup

After you've installed Python (opens new window) on your machine, in the guide Installing Packages with Pip (opens new window) you understand how to use the default Package Installation manager (Pip) to download the modules that you will need. However, even if you feel that more control over the environment could be good, you should consider using Anaconda as a preferred package manager for Data Science.

# Anaconda

The recommended way to set up your local workspace is through the use of Anaconda (opens new window).

Conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads, and switches between environments on your local computer. It was created for Python programs, but it can package and distribute software for any language.

Use the following tutorial to setup Anaconda on your local machine:

How to Set Up Your Data Science Environment with Anaconda (opens new window)

Anaconda brings with it the most used Python modules when working with data, and you can install more by simply using the " conda install " command, or using pip.

# Cloud Computing

One day you will find that in order to train that model on 50 GB of images it takes too long for your computer to compute, so you start wondering if you should use Cloud Computing to host your calculations somewhere else!

Ever had a thought like "I should buy a more powerful pc"? Forget about it and use Cloud GPUs to train your model faster and cheaper!

Last updated : 20 April 2019

# Cloud vendor Website Pricing Free Trial / Free Credits
1 Google Colaboratory https://colab.research.google.com FREE FREE FOREVER*
2 Kaggle Kernels https://www.kaggle.com FREE FREE FOREVER*
3 Tensorpad https://www.tensorpad.com 1080ti at $0.49/hour 5 free GPU hours
4 FloydHub https://www.floydhub.com https://www.floydhub.com/pricing free 2 GPU powerups in 14 days trial plan (opens new window)
5 Onepanel https://www.onepanel.io https://www.onepanel.io/pricing -
6 Nimblebox https://nimblebox.ai https://nimblebox.ai/plans free $5 worth of cloud credits
7 paperspace https://www.paperspace.com https://www.paperspace.com/pricing $10 credits
8 Overture https://www.overture.ai - free credits on signup
9 Dataiku https://www.dataiku.com - Free Plans (opens new window)
10 Cloudalize https://www.cloudalize.com https://www.cloudalize.com/pricing/ -
11 Deepcognition https://deepcognition.ai https://deepcognition.ai/products Desktop version free (opens new window) to use
12 GPUeater https://gpueater.com https://gpueater.com/#pricing -
13 Vast.ai https://vast.ai https://vast.ai/console/create/ -
14 Clusterone https://clusterone.com https://clusterone.com/pricing $25 Sign up credit
15 Snark https://snark.ai https://scale.snark.ai/pricing -
16 Crestle https://crestle.ai https://crestle.ai/#pricing 1 hour of free GPU usage on sign up fast.ai (opens new window)
17 Vector Dash(gaming) https://vectordash.com - free 7 day plan
18 Spell https://spell.run/developers https://spell.run/pricing $10 GPU credit on signup
19 Rapid Switch https://www.rapidswitch.com pricing (opens new window) -
20 Salamander https://salamander.ai https://salamander.ai -
21 Leadergpu https://www.leadergpu.com https://www.leadergpu.com -
22 vscaler https://www.vscaler.com on request (opens new window) -
23 AWS Sagemaker https://aws.amazon.com/sagemaker/ pricing (opens new window) Free plans (opens new window)
24 Exoscale https://www.exoscale.com/gpu/ pricing (opens new window) -
25 Cirrascale http://www.cirrascale.com Work station (opens new window) -
26 Alibaba cloud https://alibabacloud.com pay as you go (opens new window) $300 credits (opens new window)
27 IBM Cloud https://www.ibm.com/cloud/gpu pay as you go (opens new window) $200 credits (opens new window)
28 Google Cloud Platform https://cloud.google.com/gpu/ https://cloud.google.com/pricing/ $300 credits (opens new window)
29 Valohai https://valohai.com https://valohai.com/pricing/ free trial avaliable
30 Nvidia cloud https://www.nvidia.com/en-us/data-center/gpu-cloud-computing/ - -
31 One stop System https://www.onestopsystems.com - -
32 Azure https://azure.microsoft.com/en-in/services/machine-learning-studio/ pricing (opens new window) $200 credits (opens new window)
33 Omnisci https://www.omnisci.com https://www.omnisci.com/cloud 14 day free trial
34 Rendsolve https://rendsolve.com https://rendsolve.com/pricing -
35 Golem https://golem.network - -

# Deploy your model as a Web app

Have an idea and want to serve to world 🌎 , create a Webapp and deploy it as a flask , Django etc

# Vendor Website Pricing Free Trial / Free Credits
1 Render https://render.com https://render.com/pricing -
2 Heroku https://www.heroku.com https://www.heroku.com/pricing Free plan (opens new window) (model<500MB)
3 Digtal Ocean https://www.digitalocean.com pay as you go (opens new window) free $100 credits with github student pack (opens new window)
4 Glitch https://glitch.com - -
5 Zeit https://zeit.co https://zeit.co/pricing Free plan available

# Perks and offers

If you are a student or researcher you can get extra credts , contact the provider

# * Notes

  • Google colab and Kaggle kernels have limited session time
  • Most of the gpu providers run on top of AWS , GCP etc so may have more or less same pricing as the latter
  • Information given above is best to my searching ability , you may recheck with the provider for pricing and other info
  • license (opens new window)

# Conclusions

You have been given a panoramic of options to set up your workspace, either on your local machine or in the cloud. Often, to start practicing and experimenting you don't need a powerful GPU machine, and you can try things locally.

Then, when you need to experiment with the training of several Machine Learning models and pick the best one, you can choose a cloud provider like the ones listed here to massively scale!