|
| 1 | +--- |
| 2 | +title: Core Concepts |
| 3 | +sidebar_position: 1 |
| 4 | +--- |
| 5 | + |
| 6 | +# Core Concepts |
| 7 | + |
| 8 | +This page defines the key terms used throughout Transformer Lab and explains how they relate to each other. |
| 9 | + |
| 10 | +## Teams |
| 11 | + |
| 12 | +A team is a shared workspace for multiple users. Teams provide: |
| 13 | + |
| 14 | +- **Shared experiments** — collaborate on the same research |
| 15 | +- **Shared compute providers** — configured once, available to all members |
| 16 | +- **Quotas** — team-level and per-user compute time limits |
| 17 | +- **Access control** — role-based permissions (owner, member) with per-resource read/write rules |
| 18 | + |
| 19 | +## Experiments |
| 20 | + |
| 21 | +An experiment is the top-level container for organizing your work. It holds tasks, jobs, notes, and documents in one place. Think of it as a project folder for a line of research. |
| 22 | + |
| 23 | +When you first use Transformer Lab, default experiments named `alpha`, `beta`, and `gamma` are created for you. You can create your own and name them however you like. |
| 24 | + |
| 25 | +Everything you do — launching training runs, running evaluations, chatting with a model — happens inside an experiment. |
| 26 | + |
| 27 | +## Tasks |
| 28 | + |
| 29 | +A task is a **specification** for a piece of work. It defines what to run, what resources it needs, and where to run it. |
| 30 | + |
| 31 | +Tasks are defined with a `task.yaml` file. Here's a minimal example: |
| 32 | + |
| 33 | +```yaml |
| 34 | +name: fine-tune-llama |
| 35 | +resources: |
| 36 | + compute_provider: my-slurm-cluster |
| 37 | + accelerators: 'NVIDIA' |
| 38 | + num_nodes: 1 |
| 39 | +envs: |
| 40 | + LEARNING_RATE: '0.001' |
| 41 | +setup: | |
| 42 | + pip install -r requirements.txt |
| 43 | +run: | |
| 44 | + python train.py |
| 45 | +``` |
| 46 | +
|
| 47 | +This is just a sample — task.yaml supports many more fields including GitHub repo integration, hyperparameter sweeps, and resource constraints. See the [Task YAML Structure](../running-a-task/task-yaml-structure.md) page for the full reference. |
| 48 | +
|
| 49 | +You can create tasks by uploading a YAML file, importing from a task gallery, or writing one from scratch in the built-in editor. |
| 50 | +
|
| 51 | +A task is a reusable template — each time you launch it, it creates a new **job**. |
| 52 | +
|
| 53 | +## Jobs |
| 54 | +
|
| 55 | +A job is a **running instance** of a task. When you launch a task, Transformer Lab creates a job, routes it to a compute provider, and tracks it through its lifecycle: |
| 56 | +
|
| 57 | +```mermaid |
| 58 | +stateDiagram-v2 |
| 59 | + [*] --> QUEUED |
| 60 | + QUEUED --> LAUNCHING |
| 61 | + QUEUED --> FAILED |
| 62 | + LAUNCHING --> RUNNING |
| 63 | + LAUNCHING --> FAILED |
| 64 | + RUNNING --> COMPLETE |
| 65 | + RUNNING --> FAILED |
| 66 | + RUNNING --> STOPPED |
| 67 | +``` |
| 68 | + |
| 69 | +- **QUEUED** — submitted, waiting for the provider |
| 70 | +- **LAUNCHING** — setup script running, dependencies installing |
| 71 | +- **RUNNING** — main command executing |
| 72 | +- **COMPLETE** — finished successfully |
| 73 | +- **FAILED** — exited with an error |
| 74 | +- **STOPPED** — stopped by the user |
| 75 | + |
| 76 | +Each job produces logs and may produce artifacts like trained model weights, evaluation results, or exported files. |
| 77 | + |
| 78 | +## Compute Providers |
| 79 | + |
| 80 | +A compute provider is the backend that actually runs your jobs. Transformer Lab is provider-agnostic — you can configure one or more of the following: |
| 81 | + |
| 82 | +| Provider | Use Case | |
| 83 | +| --------------------- | ------------------------------------------ | |
| 84 | +| **Local** | Run on the machine hosting Transformer Lab | |
| 85 | +| **Slurm** | Submit to an on-premise HPC cluster | |
| 86 | +| **SkyPilot** | Orchestrate across multiple clouds | |
| 87 | +| **Runpod** | Serverless GPU cloud | |
| 88 | +| **dstack** | Open-source distributed compute | |
| 89 | +| **Nebius** | Cloud GPU provider | |
| 90 | +| **Vast.ai** | GPU cloud marketplace | |
| 91 | +| **AWS / GCP / Azure** | Direct cloud VM provisioning | |
| 92 | + |
| 93 | +When you launch a task, Transformer Lab translates your resource requirements into the provider's native format (e.g., an `sbatch` command for Slurm, a VM launch for cloud providers) and handles monitoring and log retrieval. |
| 94 | + |
| 95 | +## How It All Fits Together |
| 96 | + |
| 97 | +```mermaid |
| 98 | +graph TD |
| 99 | + T[Team] --> CP[Compute Providers] |
| 100 | + T --> Q[Quotas] |
| 101 | + T --> U[Users] |
| 102 | + U --> E[Experiments] |
| 103 | + E --> TK[Tasks] |
| 104 | + E --> N[Notes & Documents] |
| 105 | + TK -->|launch| J[Jobs] |
| 106 | + J -->|run on| CP |
| 107 | + J -->|produce| A[Artifacts] |
| 108 | +``` |
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