backup

server/gpu/modal/reflector_llm.py

@@ -38,7 +38,7 @@ def migrate_cache_llm():
     from transformers.utils.hub import move_cache
 
     print("Moving LLM cache")
-    move_cache()
+    move_cache(cache_dir=IMAGE_MODEL_DIR)
     print("LLM cache moved")
 
 

server/gpu/modal/reflector_transcriber.py

@@ -13,19 +13,40 @@ from pydantic import BaseModel
 WHISPER_MODEL: str = "large-v2"
 WHISPER_COMPUTE_TYPE: str = "float16"
 WHISPER_NUM_WORKERS: int = 1
-WHISPER_CACHE_DIR: str = "/cache/whisper"
+
+MODEL_DIR = "/model"
 
 # Translation Model
 TRANSLATION_MODEL = "facebook/m2m100_418M"
 
-stub = Stub(name="reflector-transcriber")
+stub = Stub(name="reflector-transtest")
 
 
-def download_whisper():
+def download_models():
     from faster_whisper.utils import download_model
+    from huggingface_hub import snapshot_download
 
-    download_model(WHISPER_MODEL, local_files_only=False)
+    print("Downloading Whisper model")
+    download_model(WHISPER_MODEL)
+    print("Whisper model downloaded")
 
+    print("Downloading Translation model")
+    ignore_patterns = ["*.ot"]
+    snapshot_download(TRANSLATION_MODEL, cache_dir=MODEL_DIR, ignore_patterns=ignore_patterns)
+    print("Translation model downloaded")
+
+def migrate_cache_llm():
+    """
+    XXX The cache for model files in Transformers v4.22.0 has been updated.
+    Migrating your old cache. This is a one-time only operation. You can
+    interrupt this and resume the migration later on by calling
+    `transformers.utils.move_cache()`.
+    """
+    from transformers.utils.hub import move_cache
+
+    print("Moving LLM cache")
+    move_cache()
+    print("LLM cache moved")
 
 whisper_image = (
     Image.debian_slim(python_version="3.10.8")
@@ -38,7 +59,8 @@ whisper_image = (
         "sentencepiece",
         "protobuf",
     )
-    .run_function(download_whisper)
+    .run_function(download_models)
+    .run_function(migrate_cache_llm)
     .env(
         {
             "LD_LIBRARY_PATH": (
@@ -69,8 +91,14 @@ class Whisper:
             compute_type=WHISPER_COMPUTE_TYPE,
             num_workers=WHISPER_NUM_WORKERS,
         )
-        self.translation_model = M2M100ForConditionalGeneration.from_pretrained(TRANSLATION_MODEL).to(self.device)
-        self.translation_tokenizer = M2M100Tokenizer.from_pretrained(TRANSLATION_MODEL)
+        self.translation_model = M2M100ForConditionalGeneration.from_pretrained(
+                TRANSLATION_MODEL,
+                cache_dir=TRANSCRIPTION_MODEL_DIR
+        ).to(self.device)
+        self.translation_tokenizer = M2M100Tokenizer.from_pretrained(
+                TRANSLATION_MODEL,
+                cache_dir=TRANSCRIPTION_MODEL_DIR
+        )
 
 
     @method()