Spaces:
Running
Running
File size: 3,314 Bytes
e390ccc 4bba8df e390ccc 4bba8df e390ccc c554973 4bba8df e390ccc 4bba8df 17ff73c e390ccc 4bba8df e390ccc 44d3c68 e390ccc c554973 4bba8df 6d39e54 c554973 4bba8df 2926563 4bba8df c554973 e390ccc 41bc8d2 4bba8df e390ccc 41bc8d2 4bba8df af77a1c 44d3c68 af77a1c 44d3c68 4bba8df e390ccc 4bba8df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
import os
import shutil
import subprocess
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from interfaces.cap import languages as languages_cap
from interfaces.cap import domains as domains_cap
from interfaces.emotion9 import languages as languages_emotion9
from interfaces.illframes import domains as domains_illframes
from interfaces.cap import build_huggingface_path as hf_cap_path
from interfaces.cap_minor import build_huggingface_path as hf_cap_minor_path
from interfaces.cap_media_demo import build_huggingface_path as hf_cap_media_path # why... just follow the name template the next time pls
from interfaces.manifesto import build_huggingface_path as hf_manifesto_path
from interfaces.sentiment import build_huggingface_path as hf_sentiment_path
from interfaces.emotion import build_huggingface_path as hf_emotion_path
from interfaces.emotion9 import build_huggingface_path as hf_emotion9_path
from interfaces.ontolisst import build_huggingface_path as hf_ontlisst_path
from interfaces.illframes import build_huggingface_path as hf_illframes_path
from interfaces.ontolisst import build_huggingface_path as hf_ontolisst_path
from huggingface_hub import scan_cache_dir
HF_TOKEN = os.environ["hf_read"]
# should be a temporary solution
models = [hf_manifesto_path(""), hf_sentiment_path(""), hf_emotion_path(""), hf_cap_minor_path("", ""), hf_ontolisst_path("")]
# it gets more difficult with cap
domains_cap = list(domains_cap.values())
for language in languages_cap:
for domain in domains_cap:
models.append(hf_cap_path(language, domain))
# cap media
models.append(hf_cap_media_path("", ""))
# emotion9
for language in languages_emotion9:
models.append(hf_emotion9_path(language))
# illframes (domains is a dict for some reason?)
for domain in domains_illframes.values():
models.append(hf_illframes_path(domain))
tokenizers = ["xlm-roberta-large"]
def download_hf_models():
for model_id in models:
AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", offload_folder="offload",
token=HF_TOKEN)
for tokenizer_id in tokenizers:
AutoTokenizer.from_pretrained(tokenizer_id)
def df_h():
result = subprocess.run(["df", "-H"], capture_output=True, text=True)
print(result.stdout)
def scan_cache():
cache_dir = os.environ.get("TRANSFORMERS_CACHE", os.path.expanduser("~/.cache/huggingface/transformers"))
scan_result = scan_cache_dir(cache_dir)
print("=== Model Cache Report ===")
print(f"Cache size: {scan_result.size_on_disk / 1e6:.2f} MB")
print(f"Number of repos: {len(scan_result.repos)}")
for repo in scan_result.repos:
print(f"- {repo.repo_id} ({repo.repo_type}) — {repo.size_on_disk / 1e6:.2f} MB")
def set_hf_cache_dir(path:str):
os.environ['TRANSFORMERS_CACHE'] = path
os.environ['HF_HOME'] = path
os.environ['HF_DATASETS_CACHE'] = path
os.environ['TORCH_HOME'] = path
def is_disk_full(min_free_space_in_GB=10):
total, used, free = shutil.disk_usage("/")
free_gb = free / (1024 ** 3)
if free_gb >= min_free_space_in_GB:
return False
else:
return True
|