Release: v0.35.0.rc1

Author: Wauplin

.github/workflows/python-quality.yml

@@ -44,10 +44,10 @@ jobs:
       - run: .venv/bin/python utils/generate_async_inference_client.py
       - run: .venv/bin/python utils/generate_inference_types.py
       - run: .venv/bin/python utils/check_task_parameters.py
-
+      - run: uvx ty check src
       # Run type checking at least on huggingface_hub root file to check all modules
       # that can be lazy-loaded actually exist.
       - run: .venv/bin/mypy src/huggingface_hub/__init__.py --follow-imports=silent --show-traceback
 
       # Run mypy on full package
-      - run: .venv/bin/mypy src
+      - run: .venv/bin/mypy src

.gitignore

@@ -139,4 +139,7 @@ dmypy.json
 # Spell checker config
 cspell.json
 
-tmp*
+tmp*
+
+# Claude Code
+CLAUDE.md

CONTRIBUTING.md

@@ -164,7 +164,7 @@ Follow these steps to start contributing:
    ```
 
    Compared to `make style`, `make quality` will never update your code. In addition to the previous code formatter, it
-   also runs [`mypy`](https://github.com/python/mypy) to check for static typing issues. All those tests will also run
+   also runs [`ty`](https://docs.astral.sh/ty/) type checker to check for static typing issues. All those tests will also run
    in the CI once you open your PR but it is recommended to run them locally in order to iterate faster.
 
    > For the commands leveraging the `make` utility, we recommend using the WSL system when running on

Makefile

@@ -13,7 +13,7 @@ quality:
 	python utils/check_all_variable.py
 	python utils/generate_async_inference_client.py
 
-	mypy src
+	ty check src
 
 style:
 	ruff format $(check_dirs) # formatter

docs/source/en/_toctree.yml

@@ -26,6 +26,8 @@
       title: Inference
     - local: guides/inference_endpoints
       title: Inference Endpoints
+    - local: guides/jobs
+      title: Jobs
     - local: guides/community
       title: Community Tab
     - local: guides/collections
@@ -40,8 +42,6 @@
       title: Integrate a library
     - local: guides/webhooks
       title: Webhooks
-    - local: guides/jobs
-      title: Jobs
 - title: 'Conceptual guides'
   sections:
     - local: concepts/git_vs_http

docs/source/en/guides/cli.md

@@ -611,7 +611,7 @@ Run compute jobs on Hugging Face infrastructure with a familiar Docker-like inte
 
 ```bash
 # Directly run Python code
->>> hf jobs run python:3.12 python -c "print('Hello from the cloud!')"
+>>> hf jobs run python:3.12 python -c 'print("Hello from the cloud!")'
 
 # Use GPUs without any setup
 >>> hf jobs run --flavor a10g-small pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel \
@@ -636,6 +636,12 @@ Run compute jobs on Hugging Face infrastructure with a familiar Docker-like inte
 - 📊 **Live Monitoring**: Stream logs in real-time, just like running locally
 - 💰 **Pay-as-you-go**: Only pay for the seconds you use
 
+<Tip>
+
+**Hugging Face Jobs** are available only to [Pro users](https://huggingface.co/pro) and [Team or Enterprise organizations](https://huggingface.co/enterprise). Upgrade your plan to get started!
+
+</Tip>
+
 ### Quick Start
 
 #### 1. Run your first job
@@ -714,6 +720,14 @@ You can pass environment variables to your job using
 >>> hf jobs run --secrets-file .env.secrets python:3.12 python -c "import os; print(os.environ['MY_SECRET'])"
 ```
 
+<Tip>
+
+Use `--secrets HF_TOKEN` to pass your local Hugging Face token implicitly.
+With this syntax, the secret is retrieved from the environment variable.
+For `HF_TOKEN`, it may read the token file located in the Hugging Face home folder if the environment variable is unset.
+
+</Tip>
+
 ### Hardware
 
 Available `--flavor` options:
@@ -739,10 +753,58 @@ Run UV scripts (Python scripts with inline dependencies) on HF infrastructure:
 >>> hf jobs uv run ml_training.py --flavor gpu-t4-small
 
 # Pass arguments to script
->>> hf jobs uv run process.py input.csv output.parquet --repo data-scripts
+>>> hf jobs uv run process.py input.csv output.parquet
+
+# Add dependencies
+>>> hf jobs uv run --with transformers --with torch train.py
 
 # Run a script directly from a URL
 >>> hf jobs uv run https://huggingface.co/datasets/username/scripts/resolve/main/example.py
+
+# Run a command
+>>> hf jobs uv run --with lighteval python -c "import lighteval"
 ```
 
 UV scripts are Python scripts that include their dependencies directly in the file using a special comment syntax. This makes them perfect for self-contained tasks that don't require complex project setups. Learn more about UV scripts in the [UV documentation](https://docs.astral.sh/uv/guides/scripts/).
+
+### Scheduled Jobs
+
+Schedule and manage jobs that will run on HF infrastructure.
+
+The schedule should be one of `@annually`, `@yearly`, `@monthly`, `@weekly`, `@daily`, `@hourly`, or a CRON schedule expression (e.g., `"0 9 * * 1"` for 9 AM every Monday).
+
+```bash
+# Schedule a job that runs every hour
+>>> hf jobs scheduled run @hourly python:3.12 python -c 'print("This runs every hour!")'
+
+# Use the CRON syntax
+>>> hf jobs scheduled run "*/5 * * * *" python:3.12 python -c 'print("This runs every 5 minutes!")'
+
+# Schedule with GPU
+>>> hf jobs scheduled run @hourly --flavor a10g-small pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel \
+... python -c "import torch; print(f"This code ran with the following GPU: {torch.cuda.get_device_name()}")"
+
+# Schedule a UV script
+>>> hf jobs scheduled uv run @hourly my_script.py
+```
+
+Use the same parameters as `hf jobs run` to pass environment variables, secrets, timeout, etc.
+
+Manage scheduled jobs using
+
+```bash
+# List your active scheduled jobs
+>>> hf jobs scheduled ps
+
+# Inspect the status of a job
+>>> hf jobs scheduled inspect <scheduled_job_id>
+
+# Suspend (pause) a scheduled job
+>>> hf jobs scheduled suspend <scheduled_job_id>
+
+# Resume a scheduled job
+>>> hf jobs scheduled resume <scheduled_job_id>
+
+# Delete a scheduled job
+>>> hf jobs scheduled delete <scheduled_job_id>
+```

docs/source/en/guides/inference.md

@@ -219,6 +219,7 @@ For more details, refer to the [Inference Providers pricing documentation](https
 | [`~InferenceClient.image_classification`]           | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.image_segmentation`]             | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.image_to_image`]                 | ❌                 | ❌        | ❌      | ✅      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ✅         | ❌         | ❌        |
+| [`~InferenceClient.image_to_video`]                 | ❌                 | ❌        | ❌      | ✅      | ❌              | ❌            | ❌    | ❌            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.image_to_text`]                  | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.object_detection`]               | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |  | ❌ |
 | [`~InferenceClient.question_answering`]             | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |