Principle:Allenai Open instruct Beaker Experiment Launch
| Knowledge Sources | |
|---|---|
| Domains | MLOps, Distributed Training, Experiment Management, Containerization |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Beaker Experiment Launch is the practice of submitting containerized, reproducible machine learning training and evaluation jobs to a managed compute cluster, ensuring consistent environments, automated resource allocation, and traceable experiment provenance.
Description
Large language model post-training workflows such as supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning from human feedback (RLHF/GRPO) require substantial GPU resources, multi-node coordination, and deterministic execution environments. The Beaker Experiment Launch principle addresses these requirements by encapsulating training scripts inside Docker containers and submitting them as structured experiment specifications to the Beaker platform, AI2's managed compute infrastructure.
This approach solves several critical problems in ML experiment management:
- Reproducibility: Every experiment runs inside a pinned Docker image, guaranteeing that the software environment (CUDA, Python packages, system libraries) is identical across runs.
- Resource Abstraction: Researchers specify GPU counts, node counts, and cluster preferences declaratively. The platform handles scheduling, inter-node networking, and fault recovery.
- Provenance Tracking: Each experiment receives a unique identifier and records its full specification, making it possible to audit and reproduce any historical training run.
- Secret Management: API keys for Weights & Biases, HuggingFace Hub, and other services are injected securely via Beaker's secret store, never embedded in code or images.
Usage
Use this principle whenever you need to:
- Launch a training run (SFT, DPO, GRPO) that requires one or more GPUs on a managed cluster.
- Execute multi-node distributed training across 2 or more machines with high-bandwidth interconnect.
- Ensure that a training environment is identical between development iteration and final production runs.
- Submit batch experiments with automatic retry logic and preemption handling.
- Automatically cache datasets locally before remote execution to avoid redundant preprocessing on GPU nodes.
Theoretical Basis
Containerized Reproducibility
The core idea behind containerized experiment execution is that an ML training run is fully determined by three inputs: the code (pinned via Git commit), the data (referenced by immutable dataset identifiers), and the environment (captured in a Docker image). By fixing all three, any experiment can be reproduced exactly.
In the Tulu 3 post-training pipeline, the Docker image is built from the current Git working tree at launch time. This means the image contains the exact state of the codebase, including any uncommitted local changes. The image is tagged and pushed to a container registry, creating a permanent artifact linked to the experiment.
Declarative Experiment Specifications
Rather than manually SSH-ing into GPU nodes and running scripts, the Beaker paradigm uses declarative experiment specifications. An experiment spec defines:
- Tasks: One or more task definitions, each with a command to execute, resource requirements, and environment variables.
- Constraints: Which clusters or specific hostnames the task may run on.
- Resources: GPU count, shared memory allocation, and replica count for multi-node jobs.
- Context: Priority level (low, normal, high, urgent) and preemptibility settings.
- Budget: Organizational billing account for resource consumption tracking.
- Retry Policy: How many times a failed task may be automatically retried.
This declarative approach enables the platform to make intelligent scheduling decisions, such as bin-packing jobs onto available nodes or migrating preemptible workloads when higher-priority jobs arrive.
Multi-Node Coordination
For distributed training across multiple nodes, the platform provides built-in leader election. One replica is designated the leader, and its hostname is exposed to all replicas via the BEAKER_LEADER_REPLICA_HOSTNAME environment variable. The launch tooling automatically rewrites Accelerate configuration to set the correct --num_machines, --machine_rank, and --main_process_ip parameters, abstracting away the complexity of multi-node setup.
Failure propagation ensures that if any node in a multi-node job fails, all nodes are terminated and the experiment is marked as failed, preventing silent partial failures that waste compute.
Dataset Caching Strategy
A notable aspect of this principle is the local dataset caching step that occurs before the Beaker experiment is submitted. For Open Instruct training commands, the launcher runs the training script with a --cache_dataset_only flag on the local machine. This preprocesses and tokenizes the dataset, storing it at a deterministic path derived from the configuration hash. The cached dataset is then available on shared storage (Weka or Google Cloud Storage) when the GPU job starts, eliminating the need for expensive preprocessing on GPU nodes.
Automatic Output Directory Management
To support automatic post-training evaluation, the launcher rewrites --output_dir arguments to point to a shared filesystem path (e.g., Weka). This ensures that model checkpoints are accessible to downstream evaluation jobs without manual file transfers.
Practical Guide
Step 1: Prepare Your Training Script
Write your training script using one of the supported Open Instruct entry points:
open_instruct/finetune.pyfor supervised fine-tuningopen_instruct/dpo.pyoropen_instruct/dpo_tune_cache.pyfor DPOopen_instruct/grpo_fast.pyfor GRPO reinforcement learningopen_instruct/reward_modeling.pyfor reward model training
Step 2: Commit Your Changes
The launch tooling builds a Docker image from the current working tree. You must commit your changes before launching:
git add -A && git commit -m "Prepare training run"
Step 3: Configure Cluster and Resources
Select your target cluster(s) based on your requirements:
- Weka clusters (e.g.,
ai2/jupiter,ai2/saturn): Provide high-speed shared storage for dataset caching and checkpoint sharing. - GCP clusters (e.g.,
ai2/augusta): Provide Google Cloud GPU instances with GCS-based model storage. - Interconnect clusters: Required for multi-node jobs with NCCL-based communication.
Step 4: Launch the Experiment
Use the Mason CLI tool or the build_image_and_launch.sh wrapper script:
# Direct mason invocation
python mason.py --cluster ai2/jupiter --budget ai2/oe-adapt --gpus 8 \
-- python open_instruct/grpo_fast.py --model_name_or_path allenai/Llama-3.1-Tulu-3-8B ...
# Or via the wrapper script for pre-configured debug experiments
./scripts/train/build_image_and_launch.sh scripts/train/debug/single_gpu_on_beaker.sh
Step 5: Monitor and Iterate
Each launched experiment returns a Beaker URL for monitoring. The experiment records all inputs, environment variables, and outputs, enabling full traceability of the training run.