Overview
Ubuntu 20.04 with Jupyter, JupyterLab, TensorBoard and preconfigured conda environments for TensorFlow 2 including the latest matching versions of CUDA 11.1 and cuDNN 8.1 for GPU-accelerated computing. There is no need for an SSH session to launch a notebook. All services, including TensorBoard and the Ubuntu MATE desktop, can be accessed through the browser. Check out the video to see how to start training your first model on a GPU in less than 5 minutes.
https://www.youtube.com/watch?v=NmdDMh1foSE
Additionally, this environment provides a full-featured Ubuntu MATE desktop environment and direct console access through the browser with the latest versions of Google Chrome, Visual Studio Code and Docker (including the NVIDIA Container Runtime).
The Machine Learning Workbench puts a focus on security. All communication to the instance is encrypted, launching and accessing the Jupyter Notebook requires username and password authentication.
Highlights
- ZERO CONFIGURATION: Immediately get started with developing and training your model
- SECURE: Encryption in transit by default and strong user authentication
- NO SSH NECESSARY: All features, including tensorboard, jupyter notebook and the graphical desktop environment, can be accessed through the browser
Details
Typical total price
$0.082/hour
Pricing
Free trial
- ...
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
t3.small | $0.03 | $0.021 | $0.051 |
t3.medium Recommended | $0.04 | $0.042 | $0.082 |
t3.large | $0.05 | $0.083 | $0.133 |
t3.xlarge | $0.06 | $0.166 | $0.226 |
t3.2xlarge | $0.07 | $0.333 | $0.403 |
t3a.small | $0.03 | $0.019 | $0.049 |
t3a.medium | $0.04 | $0.038 | $0.078 |
t3a.large | $0.05 | $0.075 | $0.125 |
t3a.xlarge | $0.06 | $0.15 | $0.21 |
t3a.2xlarge | $0.07 | $0.301 | $0.371 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp3) volumes | $0.08/per GB/month of provisioned storage |
Vendor refund policy
We do not currently support refunds, but you can cancel at any time.
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Updated Ubuntu 20.04 to include latest security updates, updated TensorFlow to 2.8.0.
Additional details
Usage instructions
Launch the image on any of the available instance types. Consider changing the size of the root EBS volume. Make sure that the attached security group allows traffic on port 80 and 443 if you plan to access the server via the browser interface, and port 3389 if you plan to use the Remote Desktop Protocol. Allow a couple of minutes for the instance to boot.
The web interface can be accessed through your browser on the public IP (or private IP for enterprise VPCs) of the EC2 instance (e.g. https://18.245.21.43 where 18.245.21.43 is the public IP address of the instance).
Most browsers will display a certificate warning. This warning is letting you know that the certificate was self-signed instead of signed by a trusted Certificate Authority. You can safely ignore the warning as it doesn't impact the security of the connection by clicking on "Continue to this webpage" (Internet Explorer) or "Advanced" and then "Proceed to website" (Chrome).
The default user is ubuntu and the default password is the instance ID.
Alternatively, you can reach your desktop environment through a Remote Desktop Client (such as the Microsoft Remote Desktop Application, preinstalled on Windows and available on the Apple App Store for MacOS). The hostname is simply the public IP (or private IP for enterprise VPCs). Make sure you enter the username and password before you connect. On Windows, click "Show Options" and fill in the username and password.
Resources
Vendor resources
Support
Vendor support
For paid support, email sales@netcubed.de for further information. Free support is provided via support@netcubed.de . For free support, we do not provide a guaranteed response time, however we do our best to respond to questions within 24 hours Monday through Friday.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
It's a snap ...
This really is a zero-admin solution. I had to develop a way to give a bunch of architecture and design students a hands-on demo of how to use AI., and have them run the lab in under an hour.
The basic idea is that creating something like a rudimentary image classifier is so conventionalized that they can do it, first time, in under an hour .. even if their technical skills are pretty modest.
Of course, this only holds true if there are no hitches setting up the machine and installing the requisite software etc. In fact, setup would probably take well over an hour for someone who is highly skilled.
Then I foresaw issues having the students see what is going on .. even if I set up VNC or some such, some of them were apt to struggle .. basically, I was teaching Tensorflow for poets to folks who are almost literally poets. I needed something that was ready to rock, and simple.
Enter this netcubed deeplearning AMI. It's perfect. I bought a subscription, created a development machine to test my approach, and I'm planning to mint a bunch of machines for the class. Then I will just hand out URLs and passwords, and they'll be off and running.
I did need to one thing to get tensorboard to run. Apparently the machine wants the log files to go in a predetermined folder in order to have the tensorboard client work properly. The tech support folks had me do the following in the terminal:
# move to the home directory
cd ~
# remove tensorlogs folder
rm -rf ~/tensorlogs
# make tensorlogs folder a symlink where tf for poets log files actually go
ln -s ~/tensorflow-for-poets-2/tf_files/training_summaries tensorlogs
In any case, great product, great service. The email tech support was extremely responsive. The template works beautifully when instantiated using a t3.medium instance .. "TF for Poets" ran right out of the box..