A system for managing (distributed) reconstruction of neurons from electron microscopic image stacks.
The easiest way to try out SEURON is to deploy it locally using docker compose. It sets up a full-fledged system with workers for all type of tasks SEURON can perform, and a ready-to-use JupyterLab interface. Keep in mind all computation and IO happens on a single computer, so make sure you have the necessary resources for the tasks.
- Docker 19.03 or higher
- Install docker compose plugin if you do not have it
- Optional NVidia GPU support
- NVidia kernel driver 450.80.02 or higher
- Install nvidia-container-toolkit
- Optional Setup a slack RTM bot account
- Create a notification channel for SEURON runtime messages
- Invite the bot to channels you want to interact with it
- Recommended if you plan to use SEURON on GCP
# Clone SEURON repo
git clone https://github.com/seung-lab/seuron.git seuron
# Change to the top directory of the local repo
cd seuron
# Run local deployment script
./start_seuronbot.localFollow the instruction of the local deployment script, in the end it produces a link of the JupyterLab instance hosted by SEURON. You can open the link and try the local_pipeline_tutorial.ipynb in it.
Run the following command from you local seuron folder
docker compose --env-file .env.local -f deploy/docker-compose-CeleryExecutor.yml -f deploy/docker-compose-local.yml down
The command above will keep all the docker volume created by the deployment, so you can recover all the data when you deploy again. If you want to have a fresh deployment next time, use the following command instead
docker compose --env-file .env.local -f deploy/docker-compose-CeleryExecutor.yml -f deploy/docker-compose-local.yml down -v
Deploying to Google Cloud is recommended when the dataset is large and/or you want to use multiple computers to accelerate the process. The main system of SEURON runs on a manager instance, while the tasks are done by instances managed by google instance groups. You can specify different types of instance for different types of work to optimize for efficiency or speed. The instance groups are automatically adjusted by SEURON to fit the workload.
- Google Cloud SDK
- Recommended Setup slack RTM bot account
- Create a notification channel for SEURON runtime messages
- Invite the bot to channels (not necessarily the notification channel) in which you plan to interact with it
There is no helper script to create a Google Cloud deployment. Please review and edit cloud/google/cloud-deployment.yaml and supply the entries needed by the deployment command. Once you do that use the following command to create a deployment (replacing DEPLOYMENT_NAME with your own choice):
gcloud deployment-manager deployments create DEPLOYMENT_NAME --config cloud/google/cloud-deployment.yaml
gcloud deployment-manager deployments delete DEPLOYMENT_NAME
The command may fail if you made any manual modifications to the resources created by the deployment, in which case you will need to clean up the offending items according to the error messages before trying the delete command again
SEURON deployed to Google Cloud are created using Google Cloud Compute Engine default service account. Because SEURON automatically creates instances to work on tasks, you have to make sure the default service account have enough permission to create and delete instance. It is also recommended to give it permission to read and write Google Cloud Storage, so you can access the cloud storage without tokens/credentials
If you need to write to cloud storages outside of your Google Cloud project, most likely you will need to provide a token/credential. SEURON stores them using airflow variables. Then you can mount these secrets using MOUNT_SECRETS key in the parameters for inference and segmentation.