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Remove all local telemetry #1702

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Merged
merged 1 commit into from
Dec 14, 2022
Merged

Remove all local telemetry #1702

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3 changes: 2 additions & 1 deletion docs/source/installation.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -127,7 +127,8 @@ Our library gathers telemetry information during `from_pretrained()` requests.
This data includes the version of Diffusers and PyTorch/Flax, the requested model or pipeline class,
and the path to a pretrained checkpoint if it is hosted on the Hub.
This usage data helps us debug issues and prioritize new features.
No private data, such as paths to models saved locally on disk, is ever collected.
Telemetry is only sent when loading models and pipelines from the HuggingFace Hub,
and is not collected during local usage.

We understand that not everyone wants to share additional information, and we respect your privacy,
so you can disable telemetry collection by setting the `DISABLE_TELEMETRY` environment variable from your terminal:
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24 changes: 1 addition & 23 deletions src/diffusers/hub_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,6 @@
from typing import Dict, Optional, Union
from uuid import uuid4

import requests
from huggingface_hub import HfFolder, whoami

from . import __version__
Expand Down Expand Up @@ -56,7 +55,7 @@ def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
Formats a user-agent string with basic info about a request.
"""
ua = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if DISABLE_TELEMETRY:
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"; torch/{_torch_version}"
Expand All @@ -75,27 +74,6 @@ def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
return ua


def send_telemetry(data: Dict, name: str):
"""
Sends logs to the Hub telemetry endpoint.

Args:
data: the fields to track, e.g. {"example_name": "dreambooth"}
name: a unique name to differentiate the telemetry logs, e.g. "diffusers_examples" or "diffusers_notebooks"
"""
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return

headers = {"user-agent": http_user_agent(data)}
endpoint = HUGGINGFACE_CO_TELEMETRY + name
try:
r = requests.head(endpoint, headers=headers)
r.raise_for_status()
except Exception:
# We don't want to error in case of connection errors of any kind.
pass


def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
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9 changes: 0 additions & 9 deletions src/diffusers/modeling_flax_utils.py
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Expand Up @@ -28,7 +28,6 @@
from requests import HTTPError

from . import __version__, is_torch_available
from .hub_utils import send_telemetry
from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax
from .utils import (
CONFIG_NAME,
Expand Down Expand Up @@ -340,10 +339,6 @@ def from_pretrained(
f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
f"{pretrained_path_with_subfolder}."
)
send_telemetry(
{"model_class": cls.__name__, "model_path": "local", "framework": "flax"},
name="diffusers_from_pretrained",
)
else:
try:
model_file = hf_hub_download(
Expand All @@ -359,10 +354,6 @@ def from_pretrained(
subfolder=subfolder,
revision=revision,
)
send_telemetry(
{"model_class": cls.__name__, "model_path": "hub", "framework": "flax"},
name="diffusers_from_pretrained",
)

except RepositoryNotFoundError:
raise EnvironmentError(
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9 changes: 0 additions & 9 deletions src/diffusers/modeling_utils.py
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Expand Up @@ -26,7 +26,6 @@
from requests import HTTPError

from . import __version__
from .hub_utils import send_telemetry
from .utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
Expand Down Expand Up @@ -594,10 +593,6 @@ def _get_model_file(
raise EnvironmentError(
f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
)
send_telemetry(
{"model_class": cls.__name__, "model_path": "local", "framework": "pytorch"},
name="diffusers_from_pretrained",
)
return model_file
else:
try:
Expand All @@ -615,10 +610,6 @@ def _get_model_file(
subfolder=subfolder,
revision=revision,
)
send_telemetry(
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@patrickvonplaten patrickvonplaten Dec 14, 2022

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This we can and should leave IMO because it's when people download from the Hub so IP is exposed anyways

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@anton-l anton-l Dec 14, 2022

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The pipeline class is already sent via the hub request, so this call was redundant.

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@patrickvonplaten patrickvonplaten Dec 14, 2022

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Ok all good then!

{"model_class": cls.__name__, "model_path": "hub", "framework": "pytorch"},
name="diffusers_from_pretrained",
)
return model_file

except RepositoryNotFoundError:
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10 changes: 1 addition & 9 deletions src/diffusers/pipeline_flax_utils.py
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Expand Up @@ -29,7 +29,7 @@
from tqdm.auto import tqdm

from .configuration_utils import ConfigMixin
from .hub_utils import http_user_agent, send_telemetry
from .hub_utils import http_user_agent
from .modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
from .schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
from .utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, is_transformers_available, logging
Expand Down Expand Up @@ -346,16 +346,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
ignore_patterns=ignore_patterns,
user_agent=user_agent,
)
send_telemetry(
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@patrickvonplaten patrickvonplaten Dec 14, 2022

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this should stay because we're not in a local case here

{"pipeline_class": requested_pipeline_class, "pipeline_path": "hub", "framework": "flax"},
name="diffusers_from_pretrained",
)
else:
cached_folder = pretrained_model_name_or_path
send_telemetry(
{"pipeline_class": cls.__name__, "pipeline_path": "local", "framework": "flax"},
name="diffusers_from_pretrained",
)

config_dict = cls.load_config(cached_folder)

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10 changes: 1 addition & 9 deletions src/diffusers/pipeline_utils.py
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Expand Up @@ -33,7 +33,7 @@

from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import http_user_agent, send_telemetry
from .hub_utils import http_user_agent
from .modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from .utils import (
Expand Down Expand Up @@ -509,16 +509,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
ignore_patterns=ignore_patterns,
user_agent=user_agent,
)
send_telemetry(
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@patrickvonplaten patrickvonplaten Dec 14, 2022

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This should stay as snapshot download is called anyways already

{"pipeline_class": requested_pipeline_class, "pipeline_path": "hub", "framework": "pytorch"},
name="diffusers_from_pretrained",
)
else:
cached_folder = pretrained_model_name_or_path
send_telemetry(
{"pipeline_class": cls.__name__, "pipeline_path": "local", "framework": "pytorch"},
name="diffusers_from_pretrained",
)

config_dict = cls.load_config(cached_folder)

Expand Down