hugging-face-jobs
Execute ML workloads on Hugging Face cloud
Run GPU/TPU workloads without local hardware setup. Submit Python scripts to managed Hugging Face Jobs infrastructure with secure token authentication.
์คํฌ ZIP ๋ค์ด๋ก๋
Claude์์ ์ ๋ก๋
์ค์ โ ๊ธฐ๋ฅ โ ์คํฌ โ ์คํฌ ์ ๋ก๋๋ก ์ด๋
ํ ๊ธ์ ์ผ๊ณ ์ฌ์ฉ ์์
ํ ์คํธํด ๋ณด๊ธฐ
"hugging-face-jobs" ์ฌ์ฉ ์ค์ ๋๋ค. Submit UV script for sentiment analysis
์์ ๊ฒฐ๊ณผ:
Job submitted successfully. Job ID: job_abc123. Monitor at https://huggingface.co/jobs/username/job_abc123. Estimated completion: 5 minutes.
"hugging-face-jobs" ์ฌ์ฉ ์ค์ ๋๋ค. Check status of running jobs
์์ ๊ฒฐ๊ณผ:
Found 2 running jobs: job_abc123 (RUNNING, 45% complete), job_def456 (QUEUED). Use hf_jobs('logs', {'job_id': 'job_abc123'}) to view logs.
๋ณด์ ๊ฐ์ฌ
์์ All 284 static analysis findings are false positives. The 'Ruby/shell backtick execution' detections are actually Python code examples in markdown documentation. 'Hardcoded URLs' are legitimate documentation links to Hugging Face resources. Environment variable access (HF_TOKEN) is documented authentication behavior for Hub operations. No malicious patterns detected.
ํ์ง ์ ์
๋ง๋ค ์ ์๋ ๊ฒ
ML Engineer running batch inference
Process thousands of samples through a model using cloud GPUs without local hardware investment. Submit UV scripts with vLLM for high-throughput generation.
Data Scientist transforming datasets
Execute data processing pipelines on Hugging Face datasets using Polars or Pandas. Push transformed results back to Hub securely.
Researcher running experiments
Execute reproducible ML experiments with scheduled jobs. Monitor progress via job URLs and retrieve results from Hub repositories.
์ด ํ๋กฌํํธ๋ฅผ ์ฌ์ฉํด ๋ณด์ธ์
Run this Python script on Hugging Face Jobs: [paste script]. Use CPU and 30 minute timeout.
Execute this inference script on an A10G GPU. Push results to my Hub repo username/results. Include HF_TOKEN authentication.
Create a scheduled job that runs this data transformation script daily at 9 AM. Use cpu-upgrade hardware and persist output to Hub.
Run this command using the pytorch/pytorch CUDA image on an A100 GPU. Set timeout to 4 hours and monitor for completion.
๋ชจ๋ฒ ์ฌ๋ก
- Always use secrets={'HF_TOKEN': '$HF_TOKEN'} for Hub authentication - never hardcode tokens
- Set appropriate timeouts with 20-30% buffer for your workload type
- Persist results to Hub or external storage before job completion - environment is ephemeral
ํผํ๊ธฐ
- Using local file paths with hf_jobs() MCP tool - scripts must be inline code or URLs
- Polling job status repeatedly - wait for user to request status checks
- Using env instead of secrets for tokens - env variables are visible in logs
์์ฃผ ๋ฌป๋ ์ง๋ฌธ
Do I need a paid Hugging Face plan to use Jobs?
How do I authenticate my jobs with the Hub?
What happens to my results after the job finishes?
Can I use local script files with hf_jobs()?
How do I choose the right GPU for my workload?
Can I schedule jobs to run automatically?
๊ฐ๋ฐ์ ์ธ๋ถ ์ ๋ณด
์์ฑ์
sickn33๋ผ์ด์ ์ค
Complete terms in LICENSE.txt
๋ฆฌํฌ์งํ ๋ฆฌ
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/hugging-face-jobs์ฐธ์กฐ
main
ํ์ผ ๊ตฌ์กฐ
๐ SKILL.md