These instructions will guide you through the process of setting up Efemarai. To setup and successfully run Efemarai you need a *nix based operating system, Python 3, and Chromium or Chrome.
1. Create Account
Go to our sign up page and create an account.
2. Set License Token
Copy your license token from the License section and set it as an environment variable. You might want to put this line in your shell config file in order to have the token permanently available (do not forget to source the file), not just for the current terminal session .
export EFEMARAI_LICENSE_TOKEN=<your-license-token>
3. Install Python Package
Install the Efemarai Python package.
pip install efemarai \ --extra-index-url https://${EFEMARAI_LICENSE_TOKEN}@pypi.efemarai.com
4. Start Efemarai Daemon
To use Efemarai you need to first run the daemon. Running the daemon locally ensures that none of your data, code or models leave your computer. Make sure that the EFEMARAI_LICENSE_TOKEN
environment variable is available and set correctly. Once the daemon is running you can run your code (which uses the Efemarai Python package) from any other terminal and you will be able to explore the corresponding visualization from the Efemarai web platform.
efemarai
5. Launch Efemarai
Go to the Efemarai launch page and click the LAUNCH button. Make sure to use Chromium or Google Chrome as they are the only supported browsers due to their advanced WebGL capabilities required by Efemarai.
Inspect a Tensor
As a quick example, run the following Python script.
import numpy as np import efemarai as ef tensor = np.random.rand(3, 4, 5) ef.inspect(tensor)
You should be able to see your first visualized tensor in the browser! Simply press v
and you should see something similar to the image below.
You can navigate within the 3D view with Shift + LeftMouse
to rotate, Shift + RightMouse
to translate and Scroll
to zoom in or out. If what you see after running the example script is not similar to the image below make sure to consult the Troubleshoot section.
Visualize a Computational Graph
As another example, you can explore the computational graph of a CNN being trained on the CIFAR 10 dataset. Just run the demo that comes with the Efemarai Python package
efemarai_example_train_cifar
With a few clicks on the function nodes (blue cubes) you should be able to see something like this.
Next Steps
Now that you are all set to use Efemarai here are a few resources that provide a deeper dive into Efemarai’s features and functionality.
Troubleshoot
I get a black background when I launch Efemarai.
If you get a black background when you launch Efemarai then your browser is not properly configured to use WebGL2.
- Enable WebGL2 & hardware acceleration:
- Go to
chrome://settings
- Scroll to the bottom of the page and open the Advanced settings
- Find the System section
- Ensure that Use hardware acceleration when available is checked (you need to relaunch the browser for any changes to take effect)
2. Verify that WebGL2 is working correctly:
- Go to
chrome://gpu
- Inspect the WebGL2 item in the Graphics Feature Status list
- It should say Hardware accelerated
3. If there is another message for the WebGL2 item in the Graphics Feature Status then you might want to inspect the rest of the information on the chrome://gpu
page and see if there are any errors reported. As a last resort you can try one of the experimental features of your browser and force hardware acceleration to be enabled.
- Go to
chrome://flags
- Find and enable the Override software rendering list flag
Check the WebGL2 status at chrome://gpu
and if it does not say Hardware accelerated then you might have some problems with your graphics card drivers or graphics card.
Installing with pip fails.
If installing Efemarai with pip fails then there are a couple of things you can check.
- Make sure you use the right
pip
version. On some platforms you may need to usepip3
instead of justpip
. - Make sure that when you run
echo $EFEMARAI_LICENSE_TOKEN
in the terminal it prints out the correct license token.