[v0.34.0] Announcing Jobs: a new way to run compute on Hugging Face!
We're thrilled to introduce a powerful new command-line interface for running and managing compute jobs on Hugging Face infrastructure! With the new hf jobs command, you can now seamlessly launch, monitor, and manage jobs using a familiar Docker-like experience. Run any command in Docker images (from Docker Hub, Hugging Face Spaces, or your own custom images) on a variety of hardware including CPUs, GPUs, and TPUs - all with simple, intuitive commands.
Key features:
run, ps, logs, inspect, cancel) to run and manage jobsuv (experimental)All features are available both from Python (run_job, list_jobs, etc.) and the CLI (hf jobs).
Example usage:
# Run a Python script on the cloud
hf jobs run python:3.12 python -c "print('Hello from the cloud!')"
# Use a GPU
hf jobs run --flavor=t4-small --namespace=huggingface ubuntu nvidia-smi
# List your jobs
hf jobs ps
# Stream logs from a job
hf jobs logs <job-id>
# Inspect job details
hf jobs inspect <job-id>
# Cancel a running job
hf jobs cancel <job-id>
# Run a UV script (experimental)
hf jobs uv run my_script.py --flavor=a10g-small --with=trlYou can also pass environment variables and secrets, select hardware flavors, run jobs in organizations, and use the experimental uv runner for Python scripts with inline dependencies.
Check out the Jobs guide for more examples and details.
hf! (formerly huggingface-cli)Glad to announce a long awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf! The legacy huggingface-cli remains available without any breaking change, but is officially deprecated. We took the opportunity update the syntax to a more modern command format hf <resource> <action> [options] (e.g. hf auth login, hf repo create, hf jobs run).
Run hf --help to know more about the CLI options.
✗ hf --help
usage: hf <command> [<args>]
positional arguments:
{auth,cache,download,jobs,repo,repo-files,upload,upload-large-folder,env,version,lfs-enable-largefiles,lfs-multipart-upload}
hf command helpers
auth Manage authentication (login, logout, etc.).
cache Manage local cache directory.
download Download files from the Hub
jobs Run and manage Jobs on the Hub.
repo Manage repos on the Hub.
repo-files Manage files in a repo on the Hub.
upload Upload a file or a folder to the Hub. Recommended for single-commit uploads.
upload-large-folder
Upload a large folder to the Hub. Recommended for resumable uploads.
env Print information about the environment.
version Print information about the hf version.
options:
-h, --help show this help message and exitAdded support for image-to-image task in the InferenceClient for Replicate and fal.ai providers, allowing quick image generation using FLUX.1-Kontext-dev:
from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai")
client = InferenceClient(provider="replicate")
with open("cat.png", "rb") as image_file:
input_image = image_file.read()
# output is a PIL.Image object
image = client.image_to_image(
input_image,
prompt="Turn the cat into a tiger.",
model="black-forest-labs/FLUX.1-Kontext-dev",
)
image-to-image support for Replicate provider by @hanouticelina in #3188image-to-image support for fal.ai provider by @hanouticelina in #3187In addition to this, it is now possible to directly pass a PIL.Image as input to the InferenceClient.
tiny-agents got a nice update to deal with environment variables and secrets. We've also changed its input format to follow more closely the config format from VSCode. Here is an up to date config to run Github MCP Server with a token:
{
"model": "Qwen/Qwen2.5-72B-Instruct",
"provider": "nebius",
"inputs": [
{
"type": "promptString",
"id": "github-personal-access-token",
"description": "Github Personal Access Token (read-only)",
"password": true
}
],
"servers": [
{
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GITHUB_PERSONAL_ACCESS_TOKEN",
"-e",
"GITHUB_TOOLSETS=repos,issues,pull_requests",
"ghcr.io/github/github-mcp-server"
],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "${input:github-personal-access-token}"
}
}
]
}InferenceClient and tiny-agents got a few quality of life improvements and bug fixes:
Integration of Xet is now stable and production-ready. A majority of file transfer are now handled using this protocol on new repos. A few improvements have been shipped to ease developer experience during uploads:
Documentation has already been written to explain better the protocol and its options:
healthRoute instead of GET / to check status by @mfuntowicz in #3165expand argument when listing files in repos by @lhoestq in #3195libcst incompatibility with Python 3.13 by @hanouticelina in #3251Fetched April 7, 2026