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chore: apply prettier formatting to docs

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Pepijn
2026-04-08 12:48:35 +02:00
parent 97972ae1de
commit 76129ab130
2 changed files with 32 additions and 31 deletions
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docs/source/env_processor.mdx
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@@ -126,7 +126,7 @@ class LiberoVelocityProcessorStep(ObservationProcessorStep):
state = torch.cat([eef_pos, eef_axisangle, eef_vel,
gripper_pos, gripper_vel], dim=-1) # 14D
return state
````
```
### 4. **Cleaner Environment Code**
docs/source/evaluation.mdx
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@@ -28,18 +28,18 @@ lerobot-eval \
## Key flags
| Flag | Default | Description |
|---|---|---|
| `--policy.path` | required | Hub repo ID or local path to a pretrained model |
| `--env.type` | required | Benchmark name (`pusht`, `libero`, `metaworld`, etc.) |
| `--env.task` | varies | Task or suite name (e.g. `libero_spatial`, `libero_10`) |
| `--eval.n_episodes` | `50` | Total episodes to run (across all tasks) |
| `--eval.batch_size` | `0` (auto) | Number of parallel environments. `0` = auto-tune from CPU cores |
| `--eval.use_async_envs` | `true` | Use `AsyncVectorEnv` (parallel stepping). Auto-downgrades to sync when `batch_size=1` |
| `--policy.device` | `cuda` | Inference device |
| `--policy.use_amp` | `false` | Mixed-precision inference (saves VRAM, faster on Ampere+) |
| `--seed` | `1000` | Random seed for reproducibility |
| `--output_dir` | auto-generated | Where to write results and videos |
| Flag | Default | Description |
| ----------------------- | -------------- | ------------------------------------------------------------------------------------- |
| `--policy.path` | required | Hub repo ID or local path to a pretrained model |
| `--env.type` | required | Benchmark name (`pusht`, `libero`, `metaworld`, etc.) |
| `--env.task` | varies | Task or suite name (e.g. `libero_spatial`, `libero_10`) |
| `--eval.n_episodes` | `50` | Total episodes to run (across all tasks) |
| `--eval.batch_size` | `0` (auto) | Number of parallel environments. `0` = auto-tune from CPU cores |
| `--eval.use_async_envs` | `true` | Use `AsyncVectorEnv` (parallel stepping). Auto-downgrades to sync when `batch_size=1` |
| `--policy.device` | `cuda` | Inference device |
| `--policy.use_amp` | `false` | Mixed-precision inference (saves VRAM, faster on Ampere+) |
| `--seed` | `1000` | Random seed for reproducibility |
| `--output_dir` | auto-generated | Where to write results and videos |
### Environment-specific flags
@@ -59,15 +59,16 @@ See each benchmark's documentation ([LIBERO](libero), [Meta-World](metaworld)) f
`batch_size` controls how many environments run in parallel within a single `VectorEnv`:
| `batch_size` | Behavior |
|---|---|
| `batch_size` | Behavior |
| ------------- | -------------------------------------------------------------------- |
| `0` (default) | Auto-tune: `floor(cpu_cores × 0.7)`, capped by `n_episodes` and `64` |
| `1` | Single environment, synchronous. Useful for debugging |
| `N` | N environments step in parallel via `AsyncVectorEnv` |
| `1` | Single environment, synchronous. Useful for debugging |
| `N` | N environments step in parallel via `AsyncVectorEnv` |
When `batch_size > 1` and `use_async_envs=true`, each environment runs in its own subprocess via Gymnasium's `AsyncVectorEnv`. This parallelizes the simulation stepping (the main bottleneck), while the policy runs a single batched forward pass on GPU.
**Example:** On a 16-core machine with `n_episodes=100`:
- Auto batch_size = `floor(16 × 0.7)` = `11`
- 11 environments step simultaneously → ~11× faster than sequential
@@ -91,12 +92,12 @@ For multi-task benchmarks (e.g. LIBERO with 10 tasks), environments are wrapped
### Tuning for speed
| Situation | Recommendation |
|---|---|
| Slow eval, low GPU utilization | Increase `batch_size` (or leave at auto) |
| Out of memory (system RAM) | Decrease `batch_size` |
| Out of GPU memory | Decrease `batch_size`, or use `--policy.use_amp=true` |
| Debugging / single-stepping | `--eval.batch_size=1 --eval.use_async_envs=false` |
| Situation | Recommendation |
| ------------------------------ | ----------------------------------------------------- |
| Slow eval, low GPU utilization | Increase `batch_size` (or leave at auto) |
| Out of memory (system RAM) | Decrease `batch_size` |
| Out of GPU memory | Decrease `batch_size`, or use `--policy.use_amp=true` |
| Debugging / single-stepping | `--eval.batch_size=1 --eval.use_async_envs=false` |
## Output
@@ -107,14 +108,14 @@ Results are written to `output_dir` (default: `outputs/eval/<date>/<time>_<job_n
### Metrics
| Metric | Description |
|---|---|
| `pc_success` | Success rate (%). Based on `info["is_success"]` from the environment |
| `avg_sum_reward` | Mean cumulative reward per episode |
| `avg_max_reward` | Mean peak reward per episode |
| `n_episodes` | Total episodes evaluated |
| `eval_s` | Total wall-clock time |
| `eval_ep_s` | Mean wall-clock time per episode |
| Metric | Description |
| ---------------- | -------------------------------------------------------------------- |
| `pc_success` | Success rate (%). Based on `info["is_success"]` from the environment |
| `avg_sum_reward` | Mean cumulative reward per episode |
| `avg_max_reward` | Mean peak reward per episode |
| `n_episodes` | Total episodes evaluated |
| `eval_s` | Total wall-clock time |
| `eval_ep_s` | Mean wall-clock time per episode |
## Multi-task evaluation