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improve hf jobs docs

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Nikodem Bartnik
2026-07-02 11:20:08 +02:00
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@@ -532,84 +532,7 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
Hugging Face jobs let's you easily select hardware and run the training in the cloud. So if you don't have a powerful GPU or you need more VRAM or just want to train a model much faster use HF Jobs! It's pay as you go and you simply pay for each second of use, you can see the pricing and additional information [here](https://huggingface.co/docs/hub/jobs).
> **Tip:** if you just want to launch a standard training run, you can skip building the command below and use the integrated **Train on HF Jobs via `--job.target`** flow described further down — `lerobot-train` then submits the job, uploads a local-only dataset for you, and streams the logs.
To run the training manually use this command:
<hfoptions id="train_with_hf_jobs">
<hfoption id="Command">
```bash
hf jobs run \
--flavor a10g-small \
--timeout 4h \
--secrets HF_TOKEN \
huggingface/lerobot-gpu:latest \
-- \
python -m lerobot.scripts.lerobot_train \
--dataset.repo_id=username/dataset \
--policy.type=act \
--steps=5000 \
--batch_size=16 \
--policy.device=cuda \
--policy.repo_id=username/your_policy \
--log_freq=100
```
</hfoption>
<hfoption id="API example">
<!-- prettier-ignore-start -->
```python
from huggingface_hub import run_job, get_token
run_name = "act_so101_hf_jobs"
dataset_id = "username/dataset"
user_hub_id = "username"
command_args = [
"python", "-m", "lerobot.scripts.lerobot_train",
"--dataset.repo_id", dataset_id,
"--policy.type", "act",
"--steps", "5000",
"--batch_size", "16",
"--num_workers", "4",
"--policy.device", "cuda",
"--log_freq", "100",
"--save_freq", "1000",
"--save_checkpoint", "true",
"--wandb.enable", "false",
"--policy.repo_id", f"{user_hub_id}/{run_name}"
]
print(f"Submitting job '{run_name}' to Hugging Face Infrastructure...")
job_info = run_job(
image="huggingface/lerobot-gpu:latest",
command=command_args,
flavor="a10g-small",
timeout="4h",
secrets={"HF_TOKEN": get_token()}
)
print("\n🚀 Job successfully launched!")
print(f"🔹 Job ID: {job_info.id}")
print(f"🔗 Live UI Dashboard & Logs: {job_info.url}")
```
<!-- prettier-ignore-end -->
</hfoption>
</hfoptions>
You can modify the `--flavor` to use different hardware, for example: `t4-small`, `a100-large`, `h200`. Use `hf jobs hardware` to see the full list with pricing.
Depending on the model you want to train and the hardware you selected you can also modify the `--batch_size` and `--number_of_workers`.
For longer training sessions increase the timeout.
Once the training is started you can go to [Jobs](https://huggingface.co/settings/jobs) and see if your jobs is running as well as all the outputs. Sometimes it takes a few minutes to schedule your job so be patient.
After training the model will be pushed to hub and you can use it as any other model with LeRobot.
#### Train on HF Jobs via `--job.target` (integrated CLI)
`lerobot-train` runs locally by default. To run on a HuggingFace GPU without constructing the Docker command yourself, pass `--job.target` with a hardware flavor name:
`lerobot-train` runs locally by default. To run on a HuggingFace GPU, pass `--job.target` with a hardware flavor name:
```bash
lerobot-train \