v0.22.0: Chat completion, inference types and hub mixins!

Discuss about the release in our Community Tab. Feedback is welcome!! 🤗

✨ InferenceClient

Support for inference tools continues to improve in huggingface_hub. At the menu in this release? A new chat_completion API and fully typed inputs/outputs!

Chat-completion API!

A long-awaited API has just landed in huggingface_hub! InferenceClient.chat_completion follows most of OpenAI's API, making it much easier to integrate with existing tools.

Technically speaking it uses the same backend as the text-generation task but requires a preprocessing step to format the list of messages into a single text prompt. The chat template is rendered server-side when models are powered by TGI, which is the case for most LLMs: Llama, Zephyr, Mistral, Gemma, etc. Otherwise, the templating happens client-side which requires minijinja package to be installed. We are actively working on bridging this gap, aiming at rendering all templates server-side in the future.

>>> from huggingface_hub import InferenceClient
>>> messages = [{"role": "user", "content": "What is the capital of France?"}]
>>> client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Batch completion
>>> client.chat_completion(messages, max_tokens=100)
ChatCompletionOutput(
    choices=[
        ChatCompletionOutputChoice(
            finish_reason='eos_token',
            index=0,
            message=ChatCompletionOutputChoiceMessage(
                content='The capital of France is Paris. The official name of the city is "Ville de Paris" (City of Paris) and the name of the country\'s governing body, which is located in Paris, is "La République française" (The French Republic). \nI hope that helps! Let me know if you need any further information.'
            )
        )
    ],
    created=1710498360
)

# Stream new tokens one by one
>>> for token in client.chat_completion(messages, max_tokens=10, stream=True):
...     print(token)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content='The', role='assistant'), index=0, finish_reason=None)], created=1710498504)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' capital', role='assistant'), index=0, finish_reason=None)], created=1710498504)
(...)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' may', role='assistant'), index=0, finish_reason=None)], created=1710498504)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=None, role=None), index=0, finish_reason='length')], created=1710498504)

• Implement InferenceClient.chat_completion + use new types for text-generation by @Wauplin in #2094
• Fix InferenceClient.text_generation for non-tgi models by @Wauplin in #2136
• #2153 by @Wauplin in #2153

Inference types

We are currently working towards more consistency in tasks definitions across the Hugging Face ecosystem. This is no easy job but a major milestone has recently been achieved! All inputs and outputs of the main ML tasks are now fully specified as JSONschema objects. This is the first brick needed to have consistent expectations when running inference across our stack: transformers (Python), transformers.js (Typescript), Inference API (Python), Inference Endpoints (Python), Text Generation Inference (Rust), Text Embeddings Inference (Rust), InferenceClient (Python), Inference.js (Typescript), etc.

Integrating those definitions will require more work but huggingface_hub is one of the first tools to integrate them. As a start, all InferenceClient return values are now typed dataclasses. Furthermore, typed dataclasses have been generated for all tasks' inputs and outputs. This means you can now integrate them in your own library to ensure consistency with the Hugging Face ecosystem. Specifications are open-source (see here) meaning anyone can access and contribute to them. Python's generated classes are documented here.

Here is a short example showcasing the new output types:

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.object_detection("people.jpg"):
[
    ObjectDetectionOutputElement(
        score=0.9486683011054993,
        label='person',
        box=ObjectDetectionBoundingBox(xmin=59, ymin=39, xmax=420, ymax=510)
    ),
...
]

Note that those dataclasses are backward-compatible with the dict-based interface that was previously in use. In the example above, both ObjectDetectionBoundingBox(...).xmin and ObjectDetectionBoundingBox(...)["xmin"] are correct, even though the former should be the preferred solution from now on.

• Generate inference types + start using output types by @Wauplin in #2036
• Add = None at optional parameters by @LysandreJik in #2095
• Fix inference types shared between tasks by @Wauplin in #2125

🧩 ModelHubMixin

ModelHubMixin is an object that can be used as a parent class for the objects in your library in order to provide built-in serialization methods to upload and download pretrained models from the Hub. This mixin is adapted into a PyTorchHubMixin that can serialize and deserialize any Pytorch model. The 0.22 release brings its share of improvements to these classes:

1. Better support of init values. If you instantiate a model with some custom arguments, the values will be automatically stored in a config.json file and restored when reloading the model from pretrained weights. This should unlock integrations with external libraries in a much smoother way.
2. Library authors integrating the hub mixin can now define custom metadata for their library: library name, tags, document url and repo url. These are to be defined only once when integrating the library. Any model pushed to the Hub using the library will then be easily discoverable thanks to those tags.
3. A base modelcard is generated for each saved model. This modelcard includes default tags (e.g. model_hub_mixin) and custom tags from the library (see 2.). You can extend/modify this modelcard by overwriting the generate_model_card method.

>>> import torch
>>> import torch.nn as nn
>>> from huggingface_hub import PyTorchModelHubMixin


# Define your Pytorch model exactly the same way you are used to
>>> class MyModel(
...         nn.Module,
...         PyTorchModelHubMixin, # multiple inheritance
...         library_name="keras-nlp",
...         tags=["keras"],
...         repo_url="https://github.com/keras-team/keras-nlp",
...         docs_url="https://keras.io/keras_nlp/",
...         # ^ optional metadata to generate model card
...     ):
...     def __init__(self, hidden_size: int = 512, vocab_size: int = 30000, output_size: int = 4):
...         super().__init__()
...         self.param = nn.Parameter(torch.rand(hidden_size, vocab_size))
...         self.linear = nn.Linear(output_size, vocab_size)

...     def forward(self, x):
...         return self.linear(x + self.param)

# 1. Create model
>>> model = MyModel(hidden_size=128)

# Config is automatically created based on input + default values
>>> model._hub_mixin_config
{"hidden_size": 128, "vocab_size": 30000, "output_size": 4}

# 2. (optional) Save model to local directory
>>> model.save_pretrained("path/to/my-awesome-model")

# 3. Push model weights to the Hub
>>> model.push_to_hub("my-awesome-model")

# 4. Initialize model from the Hub => config has been preserved
>>> model = MyModel.from_pretrained("username/my-awesome-model")
>>> model._hub_mixin_config
{"hidden_size": 128, "vocab_size": 30000, "output_size": 4}

# Model card has been correctly populated
>>> from huggingface_hub import ModelCard
>>> card = ModelCard.load("username/my-awesome-model")
>>> card.data.tags
["keras", "pytorch_model_hub_mixin", "model_hub_mixin"]
>>> card.data.library_name
"keras-nlp"

For more details on how to integrate these classes, check out the integration guide.

• Fix ModelHubMixin: pass config when __init__ accepts **kwargs by @Wauplin in #2058
• [PyTorchModelHubMixin] Fix saving model with shared tensors by @NielsRogge in #2086
• Correctly inject config in PytorchModelHubMixin by @Wauplin in #2079
• Fix passing kwargs in PytorchHubMixin by @Wauplin in #2093
• Generate modelcard in ModelHubMixin by @Wauplin in #2080
• Fix ModelHubMixin: save config only if doesn't exist by @Wauplin in [#2105...

[v0.21.4] Hot-fix: Fix saving model with shared tensors

Release v0.21 introduced a breaking change make it impossible to save a PytorchModelHubMixin-based model that has shared tensors. This has been fixed in #2086.

Full Changelog: v0.21.3...v0.21.4

[v0.21.3] Hot-fix: ModelHubMixin pass config when `__init__` accepts `**kwargs`

More details in #2058.

Full Changelog: v0.21.2...v0.21.3

v0.21.2: hot-fix: [HfFileSystem] Fix glob with pattern without wildcards

See #2056. (+#2050 shipped as v0.21.1).

Full Changelog: v0.21.0...v0.21.2

v0.21.0: dataclasses everywhere, file-system, PyTorchModelHubMixin, serialization and more.

Discuss about the release in our Community Tab. Feedback welcome!! 🤗

🖇️ Dataclasses everywhere!

All objects returned by the HfApi client are now dataclasses!

In the past, objects were either dataclasses, typed dictionaries, non-typed dictionaries and even basic classes. This is now all harmonized with the goal of improving developer experience.

Kudos goes to the community for the implementation and testing of all the harmonization process. Thanks again for the contributions!

• Use dataclasses for all objects returned by HfApi #1911 by @Ahmedniz1 in #1974
• Updating HfApi objects to use dataclass by @Ahmedniz1 in #1988
• Dataclasses for objects returned hf api by @NouamaneELGueddarii in #1993

💾 FileSystem

The HfFileSystem class implements the fsspec interface to allow loading and writing files with a filesystem-like interface. The interface is highly used by the datasets library and this release will improve further the efficiency and robustness of the integration.

• Pass revision in path to AbstractBufferedFile init by @albertvillanova in #1948
• [HfFileSystem] Fix rm on branch by @lhoestq in #1957
• Retry fetching data on 502 error in HfFileSystem by @mariosasko in #1981
• Add HfFileSystemStreamFile by @lhoestq in #1967
• [HfFileSystem] Copy non lfs files by @lhoestq in #1996
• Add HfFileSystem.url method by @mariosasko in #2027

🧩 Pytorch Hub Mixin

The PyTorchModelHubMixin class let's you upload ANY pytorch model to the Hub in a few lines of code. More precisely, it is a class that can be inherited in any nn.Module class to add the from_pretrained, save_pretrained and push_to_hub helpers to your class. It handles serialization and deserialization of weights and configs for you and enables download counts on the Hub.

With this release, we've fixed 2 pain points holding back users from using this lib:

1. Configs are now better handled. The mixin automatically detects if the base class defines a config, saves it on the Hub and then injects it at load time, either as a dictionary or a dataclass depending on the base class's expectations.
2. Weights are now saved as .safetensors files instead of pytorch pickles for safety reasons. Loading from previous pytorch pickles is still supported but we are moving toward completely deprecating them (in a mid to long term plan).

• Better config support in ModelHubMixin by @Wauplin in #2001
• Use safetensors by default for PyTorchModelHubMixin by @bmuskalla in #2033

✨ InferenceClient improvements

Audio-to-audio task is now supported by both by the InferenceClient!

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> audio_output = client.audio_to_audio("audio.flac")
>>> for i, item in enumerate(audio_output):
>>>     with open(f"output_{i}.flac", "wb") as f:
            f.write(item["blob"])

• Added audio to audio in inference client by @Ahmedniz1 in #2020

Also fixed a few things:

• Fix intolerance for new field in TGI stream response: 'index' by @danielpcox in #2006
• Fix optional model in tabular tasks by @Wauplin in #2018
• Added best_of to non-TGI ignored parameters by @dopc in #1949

📤 Model serialization

With the aim of harmonizing repo structures and file serialization on the Hub, we added a new module serialization with a first helper split_state_dict_into_shards that takes a state dict and split it into shards. Code implementation is mostly taken from transformers and aims to be reused by other libraries in the ecosystem. It seamlessly supports torch, tensorflow and numpy weights, and can be easily extended to other frameworks.

This is a first step in the harmonization process and more loading/saving helpers will be added soon.

• Framework-agnostic split_state_dict_into_shards helper by @Wauplin in #1938

📚 Documentation

🌐 Translations

Community is actively getting the job done to translate the huggingface_hub to other languages. We now have docs available in Simplified Chinese (here) and in French (here) to help democratize good machine learning!

• [i18n-CN] Translated some files to simplified Chinese #1915 by @2404589803 in #1916
• Update .github workflow to build cn docs on PRs by @Wauplin in #1931
• [i18n-FR] Translated files in french and reviewed them by @JibrilEl in #2024

Docs misc

• Document base_model in modelcard metadata by @Wauplin in #1936
• Update the documentation of add_collection_item by @FremyCompany in #1958
• Docs[i18n-en]: added pkgx as an installation method to the docs by @michaelessiet in #1955
• Added hf_transfer extra into setup.py and docs/ by @jamesbraza in #1970
• Documenting CLI default for download --repo-type by @jamesbraza in #1986
• Update repository.md by @xmichaelmason in #2010

Docs fixes

• Fix URL in get_safetensors_metadata docstring by @Wauplin in #1951
• Fix grammar by @Anthonyg5005 in #2003
• Fix doc by @jordane95 in #2013
• typo fix by @Decryptu in #2035

🛠️ Misc improvements

Creating a commit with an invalid README will fail early instead of uploading all LFS files before failing to commit.

• Fail early on invalid metadata by @Wauplin in #1934

Added a revision_exists helper, working similarly to repo_exists and file_exists:

>>> from huggingface_hub import revision_exists
>>> revision_exists("google/gemma-7b", "float16")
True
>>> revision_exists("google/gemma-7b", "not-a-revision")
False

• Add revision_exists helper by @Wauplin in #2042

InferenceClient.wait(...) now raises an error if the endpoint is in a failed state.

• raise on failed inference endpoint by @Wauplin in #1935

Improved progress bar when downloading a file

• improve http_get by @Wauplin in #1954

Other stuff:

• added will not echo message to the login token message by @vtrenton in #1925
• Raise if repo is disabled by @Wauplin in #1965
• Fix timezone in datetime parsing by @Wauplin in #1982
• retry on any 5xx on upload by @Wauplin in #2026

💔 Breaking changes

• Classes ModelFilter and DatasetFilter are deprecated when listing models and datasets in favor of a simpler API that lets you pass the parameters directly to list_models and list_datasets.

>>> from huggingface_hub import list_models, ModelFilter

# use
>>> list_models(language="zh")
# instead of 
>>> list_models(filter=ModelFilter(language="zh"))

Cleaner, right? ModelFilter and DatasetFilter will still be supported until v0.24 release.

• Deprecate ModelFilter/DatasetFilter by @druvdub in #2028
• List models tweaks by @julien-c in #2044

• In the inference client, ModelStatus.compute_type is not a string anymore but a dictionary with more detailed info...

0.20.3 hot-fix: Fix HfFolder login when env variable not set

This patch release fixes an issue when retrieving the locally saved token using huggingface_hub.HfFolder.get_token. For the record, this is a "planned to be deprecated" method, in favor of huggingface_hub.get_token which is more robust and versatile. The issue came from a breaking change introduced in #1895 meaning only 0.20.x is affected.

For more details, please refer to #1966.

Full Changelog: v0.20.2...v0.20.3

0.20.2 hot-fix: Fix concurrency issues in google colab login

A concurrency issue when using userdata.get to retrieve HF_TOKEN token led to deadlocks when downloading files in parallel. This hot-fix release fixes this issue by using a global lock before trying to get the token from the secrets vault. More details in #1953.

Full Changelog: v0.20.1...v0.20.2

0.20.1: hot-fix Fix circular import

This hot-fix release fixes a circular import error happening when import login or logout helpers from huggingface_hub.

Related PR: #1930

Full Changelog: v0.20.0...v0.20.1

v0.20.0: Authentication, speed, safetensors metadata, access requests and more.

(Discuss about the release in our Community Tab. Feedback welcome!! 🤗)

🔐 Authentication

Authentication has been greatly improved in Google Colab. The best way to authenticate in a Colab notebook is to define a HF_TOKEN secret in your personal secrets. When a notebook tries to reach the Hub, a pop-up will ask you if you want to share the HF_TOKEN secret with this notebook -as an opt-in mechanism. This way, no need to call huggingface_hub.login and copy-paste your token anymore! 🔥🔥🔥

In addition to the Google Colab integration, the login guide has been revisited to focus on security. It is recommended to authenticate either using huggingface_hub.login or the HF_TOKEN environment variable, rather than passing a hardcoded token in your scripts. Check out the new guide here.

• Login/authentication enhancements by @Wauplin in #1895
• Catch SecretNotFoundError in google colab login by @Wauplin in #1912

🏎️ Faster HfFileSystem

HfFileSystem is a pythonic fsspec-compatible file interface to the Hugging Face Hub. Implementation has been greatly improved to optimize fs.find performances.

Here is a quick benchmark with the bigcode/the-stack-dedup dataset:

	v0.19.4	v0.20.0
hffs.find("datasets/bigcode/the-stack-dedup", detail=False)	46.2s	1.63s
hffs.find("datasets/bigcode/the-stack-dedup", detail=True)	47.3s	24.2s

• Faster HfFileSystem.find by @mariosasko in #1809
• Faster HfFileSystem.glob by @lhoestq in #1815
• Fix common path in _ ls_tree by @lhoestq in #1850
• Remove maxdepth param from HfFileSystem.glob by @mariosasko in #1875
• [HfFileSystem] Support quoted revisions in path by @lhoestq in #1888
• Deprecate HfApi.list_files_info by @mariosasko in #1910

🚪 Access requests API (gated repos)

Models and datasets can be gated to monitor who's accessing the data you are sharing. You can also filter access with a manual approval of the requests. Access requests can now be managed programmatically using HfApi. This can be useful for example if you have advanced user request screening requirements (for advanced compliance requirements, etc) or if you want to condition access to a model based on completing a payment flow.

Check out this guide to learn more about gated repos.

>>> from huggingface_hub import list_pending_access_requests, accept_access_request

# List pending requests
>>> requests = list_pending_access_requests("meta-llama/Llama-2-7b")
>>> requests[0]
[
    AccessRequest(
        username='clem',
        fullname='Clem 🤗',
        email='***',
        timestamp=datetime.datetime(2023, 11, 23, 18, 4, 53, 828000, tzinfo=datetime.timezone.utc),
        status='pending',
        fields=None,
    ),
    ...
]

# Accept Clem's request
>>> accept_access_request("meta-llama/Llama-2-7b", "clem")

• Manage access requests programmatically by @Wauplin in #1905

🔍 Parse Safetensors metadata

Safetensors is a simple, fast and secured format to save tensors in a file. Its advantages makes it the preferred format to host weights on the Hub. Thanks to its specification, it is possible to parse the file metadata on-the-fly. HfApi now provides get_safetensors_metadata, an helper to get safetensors metadata from a repo.

# Parse repo with single weights file
>>> metadata = get_safetensors_metadata("bigscience/bloomz-560m")
>>> metadata
SafetensorsRepoMetadata(
    metadata=None,
    sharded=False,
    weight_map={'h.0.input_layernorm.bias': 'model.safetensors', ...},
    files_metadata={'model.safetensors': SafetensorsFileMetadata(...)}
)
>>> metadata.files_metadata["model.safetensors"].metadata
{'format': 'pt'}

• Parse safetensors metadata by @Wauplin in #1855

Other improvements

List and filter collections

You can now list collections on the Hub. You can filter them to return only collection containing a given item, or created by a given author.

>>> collections = list_collections(item="models/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF", sort="trending", limit=5):
>>> for collection in collections:
...   print(collection.slug)
teknium/quantized-models-6544690bb978e0b0f7328748
AmeerH/function-calling-65560a2565d7a6ef568527af
PostArchitekt/7bz-65479bb8c194936469697d8c
gnomealone/need-to-test-652007226c6ce4cdacf9c233
Crataco/favorite-7b-models-651944072b4fffcb41f8b568

• add list_collections endpoint, solves #1835 by @ceferisbarov in #1856
• fix list collections sort values by @Wauplin in #1867
• Warn about truncation when listing collections by @Wauplin in #1873

Respect .gitignore

upload_folder now respect gitignore files!

Previously it was possible to filter which files should be uploaded from a folder using the allow_patterns and ignore_patterns parameters. This can now automatically be done by simply creating a .gitignore file in your repo.

• Respect .gitignore file in commits by @Wauplin in #1868
• Remove respect_gitignore parameter by @Wauplin in #1876

Robust uploads

Uploading LFS files has also gotten more robust with a retry mechanism if a transient error happen while uploading to S3.

• More robust uploads by @Wauplin in #1827

Target language in InferenceClient.translation

InferenceClient.translation now supports src_lang/tgt_lang for applicable models.

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="fr_XX")
"Mon nom est Sarah Jessica Parker mais vous pouvez m'appeler Jessica"
>>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="es_XX")
'Mi nombre es Sarah Jessica Parker pero puedes llamarme Jessica'

• add language support to translation client, solves #1763 by @ceferisbarov in #1869

Support source in reported EvalResult

EvalResult now support source_name and source_link to provide a custom source for a reported result.

• Support source in EvalResult for model cards by @Wauplin in #1874

🛠️ Misc

Fetch all pull requests refs with list_repo_refs.

• Add include_pull_requests to list_repo_refs by @Wauplin in #1822

Filter discussion when listing them with get_repo_discussions.

# List opened PR from "sanchit-gandhi" on model repo "openai/whisper-large-v3"
>>> from huggingface_hub import get_repo_discussions
>>> discussions = get_repo_discussions(
...     repo_id="openai/whisper-large-v3",
...     author="sanchit-gandhi",
...     discussion_type="pull_request",
...     discussion_status="open",
... )

• ✨ Add filters to HfApi.get_repo_discussions by @SBrandeis in #1845

New field createdAt for ModelInfo, DatasetInfo and SpaceInfo.

• Add support for createdAt field by @Wauplin in #1816

It's now possible to create an inference endpoint running on a custom docker image (typically: a TGI container).

# Start an Inference Endpoint running Zephyr-7b-beta on TGI
>>> from huggingface_hub import create_inference_endpoint
>>> endpoint = create_inference_endpoint(
...     "aws-zephyr-7b-beta-0486",
...     repository="HuggingFaceH4/zephyr-7b-beta",
...     framework="pytorch",
...     task="text-generation",
...     accelerator="gpu",
...     vendor="aws",
...     region="us-east-1",
...     type="protected",
...     instance_size="medium",
...     instance_type="g5.2xlarge",
...     custom_image={
...         "health_route": "/health",
...         "env": {
...             "MAX_BATCH_PREFILL_TOKENS": "2048",
...             "MAX_INPUT_LENGTH": "1024",
...             "MAX_TOTAL_TOKENS": "1512",
...             "MODEL_ID": "/repository"
...         },
...         "url": "ghcr.io/huggingface/text-generation-inference:1.1.0",
...     },
... )

• Allow create inference endpoint from docker image by @Wauplin in #1861

Upload CLI: create branch when revision does not exist

• Create branch if missing in hugginface-cli upload by @Wauplin in #1857

🖥️ Environment variables

huggingface_hub.constants.HF_HOME has been made a public constant (see reference).

• Expose HF_HOME in constants by @Wauplin in #1825

Offline mode has gotten more consistent. If HF_HUB_OFFLINE is set, any http call to the Hub will fail. The fallback mechanism is snapshot_download has been refactored to be aligned with the hf_hub_download workflow. If offline mode is activated (or a connection error happens) and the files are already in the cache, snapshot_download returns the corresponding snapshot directory.

• Respect HF_HUB_OFFLINE for every http call by @Wauplin in #1899
• Improve snapshot_download offline mode by @Wauplin in #1913

DO_NOT_TRACK environment variable is now respected to deactivate telemetry calls. This is similar to HF_HUB_DISABLE_TELEMETRY but not specific to Hugging Face.

• Support DO_NOT_TRACK env variable by @Wauplin in #1920

📚 Documentation

• Document more list repos behavior by @Wauplin in #1823
• [i18n-KO] 🌐 Translated git_vs_http.md to Korean by @heuristicwave in #1862

Doc fixes

• Fixing gated attribute type in docs by @ademait in #1848
• Update modelcard_templa...

v0.19.4 - Hot-fix: do not fail if pydantic install is corrupted

On Python3.8, it is fairly easy to get a corrupted install of pydantic (more specificially, pydantic 2.x cannot run if tensorflow is installed because of an incompatible requirement on typing_extensions). Since pydantic is an optional dependency of huggingface_hub, we do not want to crash at huggingface_hub import time if pydantic install is corrupted. However this was the case because of how imports are made in huggingface_hub. This hot-fix releases fixes this bug. If pydantic is not correctly installed, we only raise a warning and continue as if it was not installed at all.

Related PR: #1829

Full Changelog: v0.19.3...v0.19.4