Documentation Index
Fetch the complete documentation index at: https://docs.telescope.training/llms.txt
Use this file to discover all available pages before exploring further.
Telescope can be installed via Docker (recommended), which comes with everything ready including performance libraries (like Transformer Engine and NVIDIA Apex), or from source, which is simpler but lacks some performance optimizations.
Docker (recommended)
From source
Prerequisites: NVIDIA GPU(s), Docker with the NVIDIA Container ToolkitOn GPU cloud platforms like Vast.ai and RunPod, you just need to create a custom template with the image ghcr.io/eduardoslonski/telescope:latest — they handle the rest. On Lambda, CoreWeave, and similar VM-based platforms, Docker comes preinstalled so you can pull and run the image directly.
Pull the image
docker pull ghcr.io/eduardoslonski/telescope:latest
Start the container
docker run --rm --gpus all --ipc=host --shm-size=16g --network=host \
--ulimit memlock=-1 --ulimit stack=67108864 --ulimit nofile=65536:65536 \
-it ghcr.io/eduardoslonski/telescope:latest /bin/bash
--ipc=host and --shm-size=16g are required for NCCL shared memory across GPUs. --ulimit memlock=-1 unlocks GPU memory pinning for efficient transfers. Platforms like Vast.ai and RunPod handle these flags automatically when using their template system.
/root/telescope inside the container.Set up Weights & Biases
Telescope logs training data to Weights & Biases, which the UI Visualization uses to display metrics and rollouts. Log in before starting training:Run training
Inside the container, run training with any of the example configs:uv run train.py --config configs/examples/example_countdown.yaml
Prerequisites: NVIDIA GPU(s), Python 3.11+, uvInstall uv (if not already installed):curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/eduardoslonski/telescope.git
cd telescope
uv venv --python 3.11
source .venv/bin/activate
uv sync
Set up Weights & Biases
Telescope logs training data to Weights & Biases, which the UI Visualization uses to display metrics and rollouts. Log in before starting training:Run training
Run training with any of the example configs:uv run train.py --config configs/examples/example_countdown.yaml
Installing from source does not include some performance libraries (Transformer Engine, NVIDIA Apex) that are pre-built in the Docker image and require complex compilation from source. Training will still work fine, just without the accelerated performance those provide.
uv run train.py --config configs/examples/example_countdown.yaml \
--learning_rate 5e-7 \
--number_of_steps 500
