Upload files to the Hub

Sharing your files and work is an important aspect of the Hub. The huggingface_hub offers several options for uploading your files to the Hub. You can use these functions independently or integrate them into your library, making it more convenient for your users to interact with the Hub. This guide will show you how to push files:

• without using Git.
• that are very large with Git LFS.
• with the commit context manager.
• with the push_to_hub() function.

Whenever you want to upload files to the Hub, you need to log in to your Hugging Face account. For more details about authentication, check out this section.

Upload a file

Once you’ve created a repository with create_repo(), you can upload a file to your repository using upload_file().

Specify the path of the file to upload, where you want to upload the file to in the repository, and the name of the repository you want to add the file to. Depending on your repository type, you can optionally set the repository type as a dataset, model, or space.

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> api.upload_file(
...     path_or_fileobj="/path/to/local/folder/README.md",
...     path_in_repo="README.md",
...     repo_id="username/test-dataset",
...     repo_type="dataset",
... )

Upload a folder

Use the upload_folder() function to upload a local folder to an existing repository. Specify the path of the local folder to upload, where you want to upload the folder to in the repository, and the name of the repository you want to add the folder to. Depending on your repository type, you can optionally set the repository type as a dataset, model, or space.

>>> from huggingface_hub import HfApi
>>> api = HfApi()

# Upload all the content from the local folder to your remote Space.
# By default, files are uploaded at the root of the repo
>>> api.upload_folder(
...     folder_path="/path/to/local/space",
...     repo_id="username/my-cool-space",
...     repo_type="space",
... )

By default, the .gitignore file will be taken into account to know which files should be committed or not. By default we check if a .gitignore file is present in a commit, and if not, we check if it exists on the Hub. Please be aware that only a .gitignore file present at the root of the directory will be used. We do not check for .gitignore files in subdirectories.

If you don’t want to use an hardcoded .gitignore file, you can use the allow_patterns and ignore_patterns arguments to filter which files to upload. These parameters accept either a single pattern or a list of patterns. Patterns are Standard Wildcards (globbing patterns) as documented here. If both allow_patterns and ignore_patterns are provided, both constraints apply.

Beside the .gitignore file and allow/ignore patterns, any .git/ folder present in any subdirectory will be ignored.

>>> api.upload_folder(
...     folder_path="/path/to/local/folder",
...     path_in_repo="my-dataset/train", # Upload to a specific folder
...     repo_id="username/test-dataset",
...     repo_type="dataset",
...     ignore_patterns="**/logs/*.txt", # Ignore all text logs
... )

You can also use the delete_patterns argument to specify files you want to delete from the repo in the same commit. This can prove useful if you want to clean a remote folder before pushing files in it and you don’t know which files already exists.

The example below uploads the local ./logs folder to the remote /experiment/logs/ folder. Only txt files are uploaded but before that, all previous logs on the repo on deleted. All of this in a single commit.

>>> api.upload_folder(
...     folder_path="/path/to/local/folder/logs",
...     repo_id="username/trained-model",
...     path_in_repo="experiment/logs/",
...     allow_patterns="*.txt", # Upload all local text files
...     delete_patterns="*.txt", # Delete all remote text files before
... )

Upload from the CLI

You can use the huggingface-cli upload command from the terminal to directly upload files to the Hub. Internally it uses the same upload_file() and upload_folder() helpers described above.

You can either upload a single file or an entire folder:

# Usage:  huggingface-cli upload [repo_id] [local_path] [path_in_repo]
>>> huggingface-cli upload Wauplin/my-cool-model ./models/model.safetensors model.safetensors
https://huggingface.co./Wauplin/my-cool-model/blob/main/model.safetensors

>>> huggingface-cli upload Wauplin/my-cool-model ./models .
https://huggingface.co./Wauplin/my-cool-model/tree/main

local_path and path_in_repo are optional and can be implicitly inferred. If local_path is not set, the tool will check if a local folder or file has the same name as the repo_id. If that’s the case, its content will be uploaded. Otherwise, an exception is raised asking the user to explicitly set local_path. In any case, if path_in_repo is not set, files are uploaded at the root of the repo.

For more details about the CLI upload command, please refer to the CLI guide.

Upload a large folder

In most cases, the upload_folder() method and huggingface-cli upload command should be the go-to solutions to upload files to the Hub. They ensure a single commit will be made, handle a lot of use cases, and fail explicitly when something wrong happens. However, when dealing with a large amount of data, you will usually prefer a resilient process even if it leads to more commits or requires more CPU usage. The upload_large_folder() method has been implemented in that spirit:

• it is resumable: the upload process is split into many small tasks (hashing files, pre-uploading them, and committing them). Each time a task is completed, the result is cached locally in a ./cache/huggingface folder inside the folder you are trying to upload. By doing so, restarting the process after an interruption will resume all completed tasks.
• it is multi-threaded: hashing large files and pre-uploading them benefits a lot from multithreading if your machine allows it.
• it is resilient to errors: a high-level retry-mechanism has been added to retry each independent task indefinitely until it passes (no matter if it’s a OSError, ConnectionError, PermissionError, etc.). This mechanism is double-edged. If transient errors happen, the process will continue and retry. If permanent errors happen (e.g. permission denied), it will retry indefinitely without solving the root cause.

If you want more technical details about how upload_large_folder is implemented under the hood, please have a look to the upload_large_folder() package reference.

Here is how to use upload_large_folder() in a script. The method signature is very similar to upload_folder():

>>> api.upload_large_folder(
...     repo_id="HuggingFaceM4/Docmatix",
...     repo_type="dataset",
...     folder_path="/path/to/local/docmatix",
... )

You will see the following output in your terminal:

Repo created: https://huggingface.co./datasets/HuggingFaceM4/Docmatix
Found 5 candidate files to upload
Recovering from metadata files: 100%|█████████████████████████████████████| 5/5 [00:00<00:00, 542.66it/s]

---------- 2024-07-22 17:23:17 (0:00:00) ----------
Files:   hashed 5/5 (5.0G/5.0G) | pre-uploaded: 0/5 (0.0/5.0G) | committed: 0/5 (0.0/5.0G) | ignored: 0
Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 5 | committing: 0 | waiting: 11
---------------------------------------------------

First, the repo is created if it didn’t exist before. Then, the local folder is scanned for files to upload. For each file, we try to recover metadata information (from a previously interrupted upload). From there, it is able to launch workers and print an update status every 1 minute. Here, we can see that 5 files have already been hashed but not pre-uploaded. 5 workers are pre-uploading files while the 11 others are waiting for a task.

A command line is also provided. You can define the number of workers and the level of verbosity in the terminal:

huggingface-cli upload-large-folder HuggingFaceM4/Docmatix --repo-type=dataset /path/to/local/docmatix --num-workers=16

Tips and tricks for large uploads

There are some limitations to be aware of when dealing with a large amount of data in your repo. Given the time it takes to stream the data, getting an upload/push to fail at the end of the process or encountering a degraded experience, be it on hf.co or when working locally, can be very annoying.

Check out our Repository limitations and recommendations guide for best practices on how to structure your repositories on the Hub. Let’s move on with some practical tips to make your upload process as smooth as possible.

• Start small: We recommend starting with a small amount of data to test your upload script. It’s easier to iterate on a script when failing takes only a little time.
• Expect failures: Streaming large amounts of data is challenging. You don’t know what can happen, but it’s always best to consider that something will fail at least once -no matter if it’s due to your machine, your connection, or our servers. For example, if you plan to upload a large number of files, it’s best to keep track locally of which files you already uploaded before uploading the next batch. You are ensured that an LFS file that is already committed will never be re-uploaded twice but checking it client-side can still save some time. This is what upload_large_folder() does for you.
• Use hf_transfer: this is a Rust-based library meant to speed up uploads on machines with very high bandwidth. To use hf_transfer:
  1. Specify the hf_transfer extra when installing huggingface_hub (i.e., pip install huggingface_hub[hf_transfer]).
  2. Set HF_HUB_ENABLE_HF_TRANSFER=1 as an environment variable.

Advanced features

In most cases, you won’t need more than upload_file() and upload_folder() to upload your files to the Hub. However, huggingface_hub has more advanced features to make things easier. Let’s have a look at them!

Non-blocking uploads

In some cases, you want to push data without blocking your main thread. This is particularly useful to upload logs and artifacts while continuing a training. To do so, you can use the run_as_future argument in both upload_file() and upload_folder(). This will return a concurrent.futures.Future object that you can use to check the status of the upload.

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> future = api.upload_folder( # Upload in the background (non-blocking action)
...     repo_id="username/my-model",
...     folder_path="checkpoints-001",
...     run_as_future=True,
... )
>>> future
Future(...)
>>> future.done()
False
>>> future.result() # Wait for the upload to complete (blocking action)
...

Even though background jobs are mostly useful to upload data/create commits, you can queue any method you like using run_as_future(). For instance, you can use it to create a repo and then upload data to it in the background. The built-in run_as_future argument in upload methods is just an alias around it.

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> api.run_as_future(api.create_repo, "username/my-model", exists_ok=True)
Future(...)
>>> api.upload_file(
...     repo_id="username/my-model",
...     path_in_repo="file.txt",
...     path_or_fileobj=b"file content",
...     run_as_future=True,
... )
Future(...)

Upload a folder by chunks

upload_folder() makes it easy to upload an entire folder to the Hub. However, for large folders (thousands of files or hundreds of GB), we recommend using upload_large_folder(), which splits the upload into multiple commits. See the Upload a large folder section for more details.

Scheduled uploads

The Hugging Face Hub makes it easy to save and version data. However, there are some limitations when updating the same file thousands of times. For instance, you might want to save logs of a training process or user feedback on a deployed Space. In these cases, uploading the data as a dataset on the Hub makes sense, but it can be hard to do properly. The main reason is that you don’t want to version every update of your data because it’ll make the git repository unusable. The CommitScheduler class offers a solution to this problem.

The idea is to run a background job that regularly pushes a local folder to the Hub. Let’s assume you have a Gradio Space that takes as input some text and generates two translations of it. Then, the user can select their preferred translation. For each run, you want to save the input, output, and user preference to analyze the results. This is a perfect use case for CommitScheduler; you want to save data to the Hub (potentially millions of user feedback), but you don’t need to save in real-time each user’s input. Instead, you can save the data locally in a JSON file and upload it every 10 minutes. For example:

>>> import json
>>> import uuid
>>> from pathlib import Path
>>> import gradio as gr
>>> from huggingface_hub import CommitScheduler

# Define the file where to save the data. Use UUID to make sure not to overwrite existing data from a previous run.
>>> feedback_file = Path("user_feedback/") / f"data_{uuid.uuid4()}.json"
>>> feedback_folder = feedback_file.parent

# Schedule regular uploads. Remote repo and local folder are created if they don't already exist.
>>> scheduler = CommitScheduler(
...     repo_id="report-translation-feedback",
...     repo_type="dataset",
...     folder_path=feedback_folder,
...     path_in_repo="data",
...     every=10,
... )

# Define the function that will be called when the user submits its feedback (to be called in Gradio)
>>> def save_feedback(input_text:str, output_1: str, output_2:str, user_choice: int) -> None:
...     """
...     Append input/outputs and user feedback to a JSON Lines file using a thread lock to avoid concurrent writes from different users.
...     """
...     with scheduler.lock:
...         with feedback_file.open("a") as f:
...             f.write(json.dumps({"input": input_text, "output_1": output_1, "output_2": output_2, "user_choice": user_choice}))
...             f.write("\n")

# Start Gradio
>>> with gr.Blocks() as demo:
>>>     ... # define Gradio demo + use `save_feedback`
>>> demo.launch()

And that’s it! User input/outputs and feedback will be available as a dataset on the Hub. By using a unique JSON file name, you are guaranteed you won’t overwrite data from a previous run or data from another Spaces/replicas pushing concurrently to the same repository.

For more details about the CommitScheduler, here is what you need to know:

• append-only: It is assumed that you will only add content to the folder. You must only append data to existing files or create new files. Deleting or overwriting a file might corrupt your repository.
• git history: The scheduler will commit the folder every every minutes. To avoid polluting the git repository too much, it is recommended to set a minimal value of 5 minutes. Besides, the scheduler is designed to avoid empty commits. If no new content is detected in the folder, the scheduled commit is dropped.
• errors: The scheduler run as background thread. It is started when you instantiate the class and never stops. In particular, if an error occurs during the upload (example: connection issue), the scheduler will silently ignore it and retry at the next scheduled commit.
• thread-safety: In most cases it is safe to assume that you can write to a file without having to worry about a lock file. The scheduler will not crash or be corrupted if you write content to the folder while it’s uploading. In practice, it is possible that concurrency issues happen for heavy-loaded apps. In this case, we advice to use the scheduler.lock lock to ensure thread-safety. The lock is blocked only when the scheduler scans the folder for changes, not when it uploads data. You can safely assume that it will not affect the user experience on your Space.

Space persistence demo

Persisting data from a Space to a Dataset on the Hub is the main use case for CommitScheduler. Depending on the use case, you might want to structure your data differently. The structure has to be robust to concurrent users and restarts which often implies generating UUIDs. Besides robustness, you should upload data in a format readable by the 🤗 Datasets library for later reuse. We created a Space that demonstrates how to save several different data formats (you may need to adapt it for your own specific needs).

Custom uploads

CommitScheduler assumes your data is append-only and should be uploading “as is”. However, you might want to customize the way data is uploaded. You can do that by creating a class inheriting from CommitScheduler and overwrite the push_to_hub method (feel free to overwrite it any way you want). You are guaranteed it will be called every every minutes in a background thread. You don’t have to worry about concurrency and errors but you must be careful about other aspects, such as pushing empty commits or duplicated data.

In the (simplified) example below, we overwrite push_to_hub to zip all PNG files in a single archive to avoid overloading the repo on the Hub:

class ZipScheduler(CommitScheduler):
    def push_to_hub(self):
        # 1. List PNG files
          png_files = list(self.folder_path.glob("*.png"))
          if len(png_files) == 0:
              return None  # return early if nothing to commit

        # 2. Zip png files in a single archive
        with tempfile.TemporaryDirectory() as tmpdir:
            archive_path = Path(tmpdir) / "train.zip"
            with zipfile.ZipFile(archive_path, "w", zipfile.ZIP_DEFLATED) as zip:
                for png_file in png_files:
                    zip.write(filename=png_file, arcname=png_file.name)

            # 3. Upload archive
            self.api.upload_file(..., path_or_fileobj=archive_path)

        # 4. Delete local png files to avoid re-uploading them later
        for png_file in png_files:
            png_file.unlink()

When you overwrite push_to_hub, you have access to the attributes of CommitScheduler and especially:

• HfApi client: api
• Folder parameters: folder_path and path_in_repo
• Repo parameters: repo_id, repo_type, revision
• The thread lock: lock

create_commit

The upload_file() and upload_folder() functions are high-level APIs that are generally convenient to use. We recommend trying these functions first if you don’t need to work at a lower level. However, if you want to work at a commit-level, you can use the create_commit() function directly.

There are three types of operations supported by create_commit():

• CommitOperationAdd uploads a file to the Hub. If the file already exists, the file contents are overwritten. This operation accepts two arguments:

  • path_in_repo: the repository path to upload a file to.
  • path_or_fileobj: either a path to a file on your filesystem or a file-like object. This is the content of the file to upload to the Hub.

• CommitOperationDelete removes a file or a folder from a repository. This operation accepts path_in_repo as an argument.

• CommitOperationCopy copies a file within a repository. This operation accepts three arguments:

  • src_path_in_repo: the repository path of the file to copy.
  • path_in_repo: the repository path where the file should be copied.
  • src_revision: optional - the revision of the file to copy if your want to copy a file from a different branch/revision.

For example, if you want to upload two files and delete a file in a Hub repository:

1. Use the appropriate CommitOperation to add or delete a file and to delete a folder:

>>> from huggingface_hub import HfApi, CommitOperationAdd, CommitOperationDelete
>>> api = HfApi()
>>> operations = [
...     CommitOperationAdd(path_in_repo="LICENSE.md", path_or_fileobj="~/repo/LICENSE.md"),
...     CommitOperationAdd(path_in_repo="weights.h5", path_or_fileobj="~/repo/weights-final.h5"),
...     CommitOperationDelete(path_in_repo="old-weights.h5"),
...     CommitOperationDelete(path_in_repo="logs/"),
...     CommitOperationCopy(src_path_in_repo="image.png", path_in_repo="duplicate_image.png"),
... ]

2. Pass your operations to create_commit():

>>> api.create_commit(
...     repo_id="lysandre/test-model",
...     operations=operations,
...     commit_message="Upload my model weights and license",
... )

In addition to upload_file() and upload_folder(), the following functions also use create_commit() under the hood:

• delete_file() deletes a single file from a repository on the Hub.
• delete_folder() deletes an entire folder from a repository on the Hub.
• metadata_update() updates a repository’s metadata.

For more detailed information, take a look at the HfApi reference.

Preupload LFS files before commit

In some cases, you might want to upload huge files to S3 before making the commit call. For example, if you are committing a dataset in several shards that are generated in-memory, you would need to upload the shards one by one to avoid an out-of-memory issue. A solution is to upload each shard as a separate commit on the repo. While being perfectly valid, this solution has the drawback of potentially messing the git history by generating tens of commits. To overcome this issue, you can upload your files one by one to S3 and then create a single commit at the end. This is possible using preupload_lfs_files() in combination with create_commit().

Here is a simple example illustrating how to pre-upload files:

>>> from huggingface_hub import CommitOperationAdd, preupload_lfs_files, create_commit, create_repo

>>> repo_id = create_repo("test_preupload").repo_id

>>> operations = [] # List of all `CommitOperationAdd` objects that will be generated
>>> for i in range(5):
...     content = ... # generate binary content
...     addition = CommitOperationAdd(path_in_repo=f"shard_{i}_of_5.bin", path_or_fileobj=content)
...     preupload_lfs_files(repo_id, additions=[addition])
...     operations.append(addition)

>>> # Create commit
>>> create_commit(repo_id, operations=operations, commit_message="Commit all shards")

First, we create the CommitOperationAdd objects one by one. In a real-world example, those would contain the generated shards. Each file is uploaded before generating the next one. During the preupload_lfs_files() step, the CommitOperationAdd object is mutated. You should only use it to pass it directly to create_commit(). The main update of the object is that the binary content is removed from it, meaning that it will be garbage-collected if you don’t store another reference to it. This is expected as we don’t want to keep in memory the content that is already uploaded. Finally we create the commit by passing all the operations to create_commit(). You can pass additional operations (add, delete or copy) that have not been processed yet and they will be handled correctly.

(legacy) Upload files with Git LFS

All the methods described above use the Hub’s API to upload files. This is the recommended way to upload files to the Hub. However, we also provide Repository, a wrapper around the git tool to manage a local repository.

Git LFS automatically handles files larger than 10MB. But for very large files (>5GB), you need to install a custom transfer agent for Git LFS:

huggingface-cli lfs-enable-largefiles

You should install this for each repository that has a very large file. Once installed, you’ll be able to push files larger than 5GB.

commit context manager

The commit context manager handles four of the most common Git commands: pull, add, commit, and push. git-lfs automatically tracks any file larger than 10MB. In the following example, the commit context manager:

1. Pulls from the text-files repository.
2. Adds a change made to file.txt.
3. Commits the change.
4. Pushes the change to the text-files repository.

>>> from huggingface_hub import Repository
>>> with Repository(local_dir="text-files", clone_from="<user>/text-files").commit(commit_message="My first file :)"):
...     with open("file.txt", "w+") as f:
...         f.write(json.dumps({"hey": 8}))

Here is another example of how to use the commit context manager to save and upload a file to a repository:

>>> import torch
>>> model = torch.nn.Transformer()
>>> with Repository("torch-model", clone_from="<user>/torch-model", token=True).commit(commit_message="My cool model :)"):
...     torch.save(model.state_dict(), "model.pt")

Set blocking=False if you would like to push your commits asynchronously. Non-blocking behavior is helpful when you want to continue running your script while your commits are being pushed.

>>> with repo.commit(commit_message="My cool model :)", blocking=False)

You can check the status of your push with the command_queue method:

>>> last_command = repo.command_queue[-1]
>>> last_command.status

Refer to the table below for the possible statuses:

When blocking=False, commands are tracked, and your script will only exit when all pushes are completed, even if other errors occur in your script. Some additional useful commands for checking the status of a push include:

# Inspect an error.
>>> last_command.stderr

# Check whether a push is completed or ongoing.
>>> last_command.is_done

# Check whether a push command has errored.
>>> last_command.failed

push_to_hub

The Repository class has a push_to_hub() function to add files, make a commit, and push them to a repository. Unlike the commit context manager, you’ll need to pull from a repository first before calling push_to_hub().

For example, if you’ve already cloned a repository from the Hub, then you can initialize the repo from the local directory:

>>> from huggingface_hub import Repository
>>> repo = Repository(local_dir="path/to/local/repo")

Update your local clone with git_pull() and then push your file to the Hub:

>>> repo.git_pull()
>>> repo.push_to_hub(commit_message="Commit my-awesome-file to the Hub")

However, if you aren’t ready to push a file yet, you can use git_add() and git_commit() to only add and commit your file:

>>> repo.git_add("path/to/file")
>>> repo.git_commit(commit_message="add my first model config file :)")

When you’re ready, push the file to your repository with git_push():

>>> repo.git_push()