NimbusImage
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Documentation
See this gitbook for documentation.
System requirements
General notes
You can run the server yourself on most reasonably new computers (Mac, Linux, PC). The GPU workers (e.g. Cellpose, Piscis) only work on Linux and possibly Windows, but will fall back to CPU if no GPU is (properly) installed.
The typical install time is probably around 1-2 hours.
Software requirements:
Docker (latest version) (be sure to follow the post install instructions for Linux, and
Node.js (latest version)
Optional (required for machine learning workers):
CUDA for machine learning workers,
NVIDIA docker toolkit
Supported browsers
Supports all major browsers, including Chrome, Firefox, and Safari. Note that the SAM ViT-B tool requires WebGPU and so is only available on Chrome.
Hardware requirements
Will run on most Mac, Linux, and PC computers. GPU workers requires a GPU with NVIDIA 535 drivers installed, but will fall back to CPU if no properly installed GPU is detected. Strongly recommend at least 16GB of RAM. To handle very large images, we recommend servers with at least 64GB of RAM.
Development environment
Install PNPM
npm i -g pnpm
Clone the repo and install node modules:
git clone https://github.com/arjunrajlaboratory/NimbusImage.git
cd NimbusImage
pnpm install
Compile C++ code to wasm with this command:
pnpm emscripten-build
This will run the command pnpm emscripten-build:release.
You can also run pnpm emscripten-build:debug to build with debug symbols.
The following will pull in the SAM models (from the UPennContrast directory):
mkdir -p public/onnx-models/sam/vit_b
cd public/onnx-models/sam/vit_b
wget "https://huggingface.co/rajlab/sam_vit_b/resolve/main/decoder.onnx" -O decoder.onnx
wget "https://huggingface.co/rajlab/sam_vit_b/resolve/main/encoder.onnx" -O encoder.onnx
Start docker images for the backend:
docker compose build
docker compose up -d
This will set up Girder (backend) running on http://localhost:8080
Then, to start the front end (development):
pnpm run dev
If you are on Linux, you may need to run the following:
cat /proc/sys/fs/inotify/max_user_watches
sudo sysctl fs.inotify.max_user_watches=1000000
sudo sysctl -p
You can now access NimbusImage by going to:
http://localhost:5173
To setup an environment for native C++ development for ITK, see itk/README.md.
For technical documentation about tools, see TOOLS.md.
To install the workers:
Go to a new directory (NOT the UPennContrast directory) and run
git clone https://github.com/arjunrajlab/ImageAnalysisProject
chmod +x build_machine_learning_workers.sh
chmod +x build_workers.sh
./build_machine_learning_workers.sh
./build_workers.sh
That will install all the workers. The machine learning workers will run on CPU on Linux if a GPU is not available, although will run much more slowly.
Login details
IMPORTANT: by default, a admin user will be created with the name admin and the password password. You can use that user to initially log into the system. For security, it is critical to add a new admin user in Girder and then remove the original admin user. To do this, go to localhost:8080, where you can sign into Girder, then go to the Users tab on the left.
Demo and test data
Quick start
Vignettes
Test dataset with RNA FISH images
Test N-dimensional dataset with GFP labeled nuclei
Girder Defaults
Girder will create an assetstore in which all the data is stored.
To change the default settings of the landing pange for unauthenticated users, create a .env file following this pattern:
VITE_GIRDER_URL=http://localhost:8080
VITE_DEFAULT_USER=User
VITE_DEFAULT_PASSWORD=Password
VITE_ZENODO_SAMPLES="nimbusimagesampledatasets"
The users that already opened the app once will have the field "Girder Domain" filled with the last domain they used. Otherwise, the VITE_GIRDER_URL variable will be used. If the default user and password are set, the app will try to log in with these credentials.
Compile and minify for production
To compile for production, run this command:
pnpm build
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