Use this file to discover all available pages before exploring further.
Lyceum Cloud accepts three execution types: Python, Docker image, and Docker Compose. Pick the one that matches how your code is packaged.
You have…
Use
A .py script and a requirements.txt
Python
A container image (public or private)
Docker image
A multi-service stack (docker-compose.yml)
Docker Compose
All three submission paths return an execution_id plus a streaming URL. From that point on you read logs, fetch results, and abort through the same set of endpoints — see Runs.
Every run targets a hardware profile (e.g. cpu, gpu.a100, gpu.h100). You can only launch on profiles your account is authorised for. Three endpoints help you check what’s available:
Python is the most common entry point. The CLI handles packaging your script, bundling local imports, and installing pip requirements before the code runs. The .lyceum/config.json workspace file lets you persist requirements and import paths so you don’t need to repeat them on every invocation.
CLI
REST API
lyceum python run script.py --machine gpu.a100 --requirements requirements.txt
Flag
Description
-m, --machine
Hardware profile (cpu, gpu.a100, gpu.h100, …)
-r, --requirements
Path to a requirements.txt
-f, --file-name
Display name for the run
--import
Local imports to bundle
--use-config / --no-config
Toggle reading .lyceum/config.json
-d, --debug
Enable debug logging
Manage the workspace config (.lyceum/config.json) for shared dependencies and import paths:
lyceum python config init # createlyceum python config show # print current configlyceum python config refresh # regenerate
POST /api/v2/external/execution/streaming/start
Body fields include code (the Python source as a string), machine_type, optional requirements, imports, and other execution options. Returns execution_id and stream_url.
Use Docker when your environment is already containerised — for example a CUDA image with preinstalled dependencies, or a job that doesn’t fit the Python entrypoint cleanly. The platform pulls the image, runs the command you specify, and streams stdout/stderr back.For private registries, the request supports two credential modes:
Basic auth — username and password for any registry
AWS — access key, secret key, session token, and region for Amazon ECR
CLI
REST API
lyceum docker run python:3.11-slim -c "python -c 'print(1)'" -m cpu
Flag
Default
Description
-c, --command
—
Command to run inside the container
-e, --env
—
Environment variable, e.g. KEY=value (repeatable)
-m, --machine
cpu
Machine type
--s3 / --no-s3
on
Mount your storage bucket inside the container
--s3-mount-path
/mnt/s3
Where to mount the bucket inside the container
--callback
—
Webhook URL for completion notification
--registry-creds
—
Registry credentials as a JSON string
--registry-type
—
Registry credential type (basic, aws)
By default the bucket is mounted at /mnt/s3 inside the container.
POST /api/v2/external/execution/image/start
Body: docker_image_ref, docker_run_cmd, docker_run_env, machine_type, and docker_registry_credential_type (basic or aws) plus the matching credential fields when needed. For ECR pass aws_access_key_id, aws_secret_access_key, aws_session_token, aws_region. For other registries pass docker_username and docker_password. Set enable_s3_mount: true to mount your storage bucket (path defaults to s3_mount_path: "/mnt/s3").
For multi-service stacks (e.g. an app talking to a database) you can submit a whole docker-compose.yml. The platform brings up all services on the same machine and tears them down when the entrypoint service exits.
CLI
REST API
lyceum compose run docker-compose.yml --machine gpu.a100
Flag
Description
-m, --machine
Hardware profile
--env-file
Env file passed to the stack
POST /api/v2/external/execution/compose/start
Body: the compose_file_code as a string and a machine_type.
Each execution type has its own abort endpoint. Use abort to immediately kill a run; for graceful stop (notebooks, interactive sessions), use POST /workloads/stop/{execution_id} instead.
POST /execution/streaming/abort/{execution_id}POST /execution/image/abort/{execution_id}POST /execution/compose/abort/{execution_id}
