OPENGIT/Retrieval-based-Voice-Conversion-WebUI
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Uncomment nvidia deps by deafult, update the script

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VSlobolinskyi 2025-03-18 18:19:24 +02:00
parent fea77c0b96
commit b598767553
2 changed files with 43 additions and 26 deletions
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51
configure_gpu_deps.py
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@ -4,20 +4,20 @@ import re
def process_lines(lines, target_gpu): def process_lines(lines, target_gpu):
""" """
Process the lines of a file and uncomment the configuration block corresponding Process lines from the pyproject.toml file.
to target_gpu ('nvidia' or 'amd') while leaving the other block commented. In the GPU configuration blocks, uncomment lines if they belong to the target block,
and ensure lines in the non-target block are commented.
""" """
output_lines = [] output_lines = []
current_block = None # None, "nvidia", or "amd" current_block = None # None, "nvidia", or "amd"
# Define regexes to detect the markers # Regex patterns for block markers and separator lines.
nvidia_marker = re.compile(r'---\s*NVIDIA GPU configuration\s*---', re.IGNORECASE) nvidia_marker = re.compile(r'---\s*NVIDIA GPU configuration\s*---', re.IGNORECASE)
amd_marker = re.compile(r'---\s*AMD GPU configuration\s*---', re.IGNORECASE) amd_marker = re.compile(r'---\s*AMD GPU configuration\s*---', re.IGNORECASE)
separator = re.compile(r'^#\s*-{5,}') # a commented separator line (at least 5 dashes) separator = re.compile(r'^#\s*-{5,}') # a commented separator line
for line in lines: for line in lines:
stripped = line.lstrip() # Check if this line marks the beginning of a GPU config block.
# Check for block start markers (they remain unchanged)
if nvidia_marker.search(line): if nvidia_marker.search(line):
current_block = "nvidia" current_block = "nvidia"
output_lines.append(line) output_lines.append(line)
@ -26,24 +26,41 @@ def process_lines(lines, target_gpu):
current_block = "amd" current_block = "amd"
output_lines.append(line) output_lines.append(line)
continue continue
# End of block when encountering a separator line
# If we hit a separator line, then end the current block.
if separator.match(line): if separator.match(line):
current_block = None current_block = None
output_lines.append(line) output_lines.append(line)
continue continue
# If we're in a GPU configuration block and the line is commented, process it # Process lines within a GPU configuration block.
if current_block is not None and stripped.startswith("#"): if current_block is not None:
# Remove the first '#' and any following space if we are in the target block. # Remove newline to check content.
if current_block == target_gpu: stripped = line.rstrip("\n")
# Uncomment by removing the first '#' (preserve indentation) if stripped.strip() == "":
# Using regex to remove a leading '#' with possible spaces
uncommented = re.sub(r'^(?P<indent>\s*)#\s?', r'\g<indent>', line)
output_lines.append(uncommented)
else:
# Leave the line commented for the non-target block
output_lines.append(line) output_lines.append(line)
continue
# If this block is the target, ensure the line is uncommented.
if current_block == target_gpu:
# If the line is already uncommented, leave it.
if not stripped.lstrip().startswith("#"):
output_lines.append(line)
else:
# Remove the first occurrence of '#' with following space.
uncommented = re.sub(r'^(\s*)#\s?', r'\1', line)
output_lines.append(uncommented)
else:
# For the non-target block, ensure the line is commented.
if stripped.lstrip().startswith("#"):
# Already commented, leave as-is.
output_lines.append(line)
else:
# Add a '#' preserving the original indentation.
leading_space = re.match(r'^(\s*)', line).group(1)
output_lines.append(f"{leading_space}# {line.lstrip()}")
else: else:
# Outside of any GPU config block, just add the line.
output_lines.append(line) output_lines.append(line)
return output_lines return output_lines

16
pyproject.toml
View File

@ -60,14 +60,14 @@ av = "*"
# --------------------------------------------------------------------------- # ---------------------------------------------------------------------------
# --- NVIDIA GPU configuration --- # --- NVIDIA GPU configuration ---
# torch = [ torch = [
# { url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=d99be44487d3ed0f7e6ef5d6689a37fb4a2f2821a9e7b59e7e04002a876a667a", markers = "sys_platform == 'win32'" }, { url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=d99be44487d3ed0f7e6ef5d6689a37fb4a2f2821a9e7b59e7e04002a876a667a", markers = "sys_platform == 'win32'" },
# { url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=f3c0ba02b50d0021ff26f030e22d4c45965537cf91f322e52a65b8c58396f81c", markers = "sys_platform == 'linux'" } { url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=f3c0ba02b50d0021ff26f030e22d4c45965537cf91f322e52a65b8c58396f81c", markers = "sys_platform == 'linux'" }
# ] ]
# torchaudio = [ torchaudio = [
# { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=79b5afa556063be18de4a1964339242301fe04e782e1030a22695257dd9afbd2", markers = "sys_platform == 'win32'" }, { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=79b5afa556063be18de4a1964339242301fe04e782e1030a22695257dd9afbd2", markers = "sys_platform == 'win32'" },
# { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=2b077639f240176bb27e964e2e9b3a5c2a8d560a3a7bc1ffd0a024e81f2e10b4", markers = "sys_platform == 'linux'" } { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=2b077639f240176bb27e964e2e9b3a5c2a8d560a3a7bc1ffd0a024e81f2e10b4", markers = "sys_platform == 'linux'" }
# ] ]
# --- AMD GPU configuration --- # --- AMD GPU configuration ---
# torch = "2.4.1" # torch = "2.4.1"
# torchaudio = "2.4.1" # torchaudio = "2.4.1"