Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
Allow URLs
Browse files
app.py
CHANGED
@@ -7,6 +7,7 @@ from huggingface_hub import HfApi
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from huggingface_hub.utils import RepositoryNotFoundError, GatedRepoError
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from accelerate.commands.estimate import create_empty_model, check_has_model
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from accelerate.utils import convert_bytes, calculate_maximum_sizes
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# We need to store them as globals because gradio doesn't have a way for us to pass them in to the button
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HAS_DISCUSSION = True
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@@ -54,12 +55,20 @@ When training with `Adam`, you can expect roughly 4x the reported results to be
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discussion = api.create_discussion(MODEL_NAME, "[AUTOMATED] Model Memory Requirements", description=post)
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webbrowser.open_new_tab(discussion.url)
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def
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"
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def calculate_memory(model_name:str, library:str, options:list, access_token:str, raw=False):
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"Calculates the memory usage for a model"
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@@ -67,11 +76,7 @@ def calculate_memory(model_name:str, library:str, options:list, access_token:str
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model_name = translate_llama2(model_name)
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if library == "auto":
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library = None
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try:
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model_name = convert_url_to_name(model_name)
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except ValueError:
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raise gr.Error(f"URL `{model_name}` is not a valid model URL to the Hugging Face Hub")
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try:
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model = create_empty_model(model_name, library_name=library, trust_remote_code=True, access_token=access_token)
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except GatedRepoError:
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from huggingface_hub.utils import RepositoryNotFoundError, GatedRepoError
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from accelerate.commands.estimate import create_empty_model, check_has_model
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from accelerate.utils import convert_bytes, calculate_maximum_sizes
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from urllib.parse import urlparse
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# We need to store them as globals because gradio doesn't have a way for us to pass them in to the button
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HAS_DISCUSSION = True
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discussion = api.create_discussion(MODEL_NAME, "[AUTOMATED] Model Memory Requirements", description=post)
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webbrowser.open_new_tab(discussion.url)
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def extract_from_url(name:str):
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"Checks if `name` is a URL, and if so converts it to a model name"
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is_url = False
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try:
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result = urlparse(name)
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is_url = all([result.scheme, result.netloc])
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except:
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is_url = False
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# Pass through if not a URL
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if not is_url:
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return name
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else:
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path = result.path
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return path[1:]
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def calculate_memory(model_name:str, library:str, options:list, access_token:str, raw=False):
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"Calculates the memory usage for a model"
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model_name = translate_llama2(model_name)
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if library == "auto":
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library = None
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model_name = extract_from_url(model_name)
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try:
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model = create_empty_model(model_name, library_name=library, trust_remote_code=True, access_token=access_token)
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except GatedRepoError:
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