Manage your Space

In this guide, we will see how to manage your Space runtime (secrets and hardware) using huggingface_hub.

A simple example: configure secrets and hardware.

Here is an end-to-end example to create and setup a Space on the Hub.

1. Create a Space on the Hub.

>>> from huggingface_hub import HfApi
>>> repo_id = "Wauplin/my-cool-training-space"
>>> api = HfApi()

# For example with a Gradio SDK
>>> api.create_repo(repo_id=repo_id, repo_type="space", space_sdk="gradio")

1. (bis) Duplicate a Space.

This can prove useful if you want to build up from an existing Space instead of starting from scratch. It is also useful is you want control over the configuration/settings of a public Space. See duplicate_space() for more details.

>>> duplicate_space("multimodalart/dreambooth-training")

2. Upload your code using your preferred solution.

Here is an example to upload the local folder src/ from your machine to your Space:

>>> api.upload_folder(repo_id=repo_id, repo_type="space", folder_path="src/")

At this step, your app should already be running on the Hub for free ! However, you might want to configure it further with secrets and upgraded hardware.

3. Configure secrets

Your Space might require some secret keys or token to work. See docs for more details. For example, an HF token to upload an image dataset to the Hub once generated from your Space.

>>> api.add_space_secret(repo_id=repo_id, key="HF_TOKEN", value="hf_api_***")

Secrets can be deleted as well:

>>> api.delete_space_secret(repo_id=repo_id, key="HF_TOKEN")

From within your Space, secrets are available as environment variables (or Streamlit Secrets Management if using Streamlit). No need to fetch them via the API!

Any change in your Space configuration (secrets or hardware) will trigger a restart of your app.

4. Configure the hardware

By default, your Space will run on a CPU environment for free. You can upgrade the hardware to run it on GPUs. A payment card or a community grant is required to access upgrade your Space. See docs for more details.

# Use `SpaceHardware` enum
>>> from huggingface_hub import SpaceHardware
>>> api.request_space_hardware(repo_id=repo_id, hardware=SpaceHardware.T4_MEDIUM)

# Or simply pass a string value
>>> api.request_space_hardware(repo_id=repo_id, hardware="t4-medium")

Hardware updates are not done immediately as your Space has to be reloaded on our servers. At any time, you can check on which hardware your Space is running to see if your request has been met.

>>> runtime = api.get_space_runtime(repo_id=repo_id)
>>> runtime.stage
"RUNNING_BUILDING"
>>> runtime.hardware
"cpu-basic"
>>> runtime.requested_hardware
"t4-medium"

You now have a Space fully configured. Make sure to downgrade your Space back to “cpu-classic” when you are done using it.

Bonus: request hardware when creating the Space!

Upgraded hardware will be automatically assigned to your Space once it’s built.

>>> api.create_repo(
...     repo_id=repo_id,
...     repo_type="space",
...     space_sdk="gradio"
...     space_hardware="cpu-upgrade",
... )

More advanced: temporarily upgrade your Space !

Spaces allow for a lot of different use cases. Sometimes, you might want to temporarily run a Space on a specific hardware, do something and then shut it down. In this section, we will explore how to benefit from Spaces to finetune a model on demand. This is only one way of solving this particular problem. It has to be taken as a suggestion and adapted to your use case.

Let’s assume we have a Space to finetune a model. It is a Gradio app that takes as input a model id and a dataset id. The workflow is as follows:

(Prompt the user for a model and a dataset)
Load the model from the Hub.
Load the dataset from the Hub.
Finetune the model on the dataset.
Upload the new model to the Hub.

Step 3. requires a custom hardware but you don’t want your Space to be running all the time on a paid GPU. A solution is to dynamically request hardware for the training and shut it down afterwards. Since requesting hardware restarts your Space, your app must somehow “remember” the current task it is performing. There are multiple ways of doing this. In this guide we will see one solution using a Dataset as “task scheduler”.

App skeleton

Here is what your app would look like. On startup, check if a task is scheduled and if yes, run it on the correct hardware. Once done, set back hardware to the free-plan CPU and prompt the user for a new task.

Such a workflow does not support concurrent access as normal demos. In particular, the interface will be disabled when training occurs. It is preferable to set your repo as private to ensure you are the only user.

# Space will need your token to request hardware: set it as a Secret !
HF_TOKEN = os.environ.get("HF_TOKEN")

# Space own repo_id
TRAINING_SPACE_ID = "Wauplin/dreambooth-training"

from huggingface_hub import HfApi, SpaceHardware
api = HfApi(token=HF_TOKEN)

# On Space startup, check if a task is scheduled. If yes, finetune the model. If not,
# display an interface to request a new task.
task = get_task()
if task is None:
    # Start Gradio app
    def gradio_fn(task):
        # On user request, add task and request hardware
        add_task(task)
        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)

    gr.Interface(fn=gradio_fn, ...).launch()
else:
    runtime = api.get_space_runtime(repo_id=TRAINING_SPACE_ID)
    # Check if Space is loaded with a GPU.
    if runtime.hardware == SpaceHardware.T4_MEDIUM:
        # If yes, finetune base model on dataset !
        train_and_upload(task)

        # Then, mark the task as "DONE"
        mark_as_done(task)

        # DO NOT FORGET: set back CPU hardware
        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.CPU_BASIC)
    else:
        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)

Task scheduler

Scheduling tasks can be done in many ways. Here is an example how it could be done using a simple CSV stored as a Dataset.

# Dataset ID in which a `tasks.csv` file contains the tasks to perform.
# Here is a basic example for `tasks.csv` containing inputs (base model and dataset)
# and status (PENDING or DONE).
#     multimodalart/sd-fine-tunable,Wauplin/concept-1,DONE
#     multimodalart/sd-fine-tunable,Wauplin/concept-2,PENDING
TASK_DATASET_ID = "Wauplin/dreambooth-task-scheduler"

def _get_csv_file():
    return hf_hub_download(repo_id=TASK_DATASET_ID, filename="tasks.csv", repo_type="dataset", token=HF_TOKEN)

def get_task():
    with open(_get_csv_file()) as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        for row in csv_reader:
            if row[2] == "PENDING":
                return row[0], row[1] # model_id, dataset_id

def add_task(task):
    model_id, dataset_id = task
    with open(_get_csv_file()) as csv_file:
        with open(csv_file, "r") as f:
            tasks = f.read()

    api.upload_file(
        repo_id=repo_id,
        repo_type=repo_type,
        path_in_repo="tasks.csv",
        # Quick and dirty way to add a task
        path_or_fileobj=(tasks + f"\n{model_id},{dataset_id},PENDING").encode()
    )

def mark_as_done(task):
    model_id, dataset_id = task
    with open(_get_csv_file()) as csv_file:
        with open(csv_file, "r") as f:
            tasks = f.read()

    api.upload_file(
        repo_id=repo_id,
        repo_type=repo_type,
        path_in_repo="tasks.csv",
        # Quick and dirty way to set the task as DONE
        path_or_fileobj=tasks.replace(
            f"{model_id},{dataset_id},PENDING",
            f"{model_id},{dataset_id},DONE"
        ).encode()
    )

Hub Python Library

Manage your Space

A simple example: configure secrets and hardware.

More advanced: temporarily upgrade your Space !

App skeleton

Task scheduler