Change HUGGINGFACE_TOKEN to HF_TOKEN (#539)

modal-labs · Jan 3, 2024 · c00633e · c00633e
1 parent 8db92de
commit c00633e
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Showing 8 changed files with 19 additions and 19 deletions.
diff --git a/06_gpu_and_ml/diffusers/train_and_serve_diffusers_script.py b/06_gpu_and_ml/diffusers/train_and_serve_diffusers_script.py
@@ -110,7 +110,7 @@
 # import huggingface_hub
 
 # # login to huggingface
-# hf_key = os.environ["HUGGINGFACE_TOKEN"]
+# hf_key = os.environ["HF_TOKEN"]
 # huggingface_hub.login(hf_key)
 
 # dataset = load_dataset("imagefolder", data_dir="/lg_white_bg_heroicon_png_img", split="train")
@@ -285,7 +285,7 @@ def train():
     write_basic_config(mixed_precision="fp16")
 
     # authenticate to hugging face so we can download the model weights
-    hf_key = os.environ["HUGGINGFACE_TOKEN"]
+    hf_key = os.environ["HF_TOKEN"]
     huggingface_hub.login(hf_key)
 
     # check whether we can access the model repo

diff --git a/06_gpu_and_ml/embeddings/text_embeddings_inference.py b/06_gpu_and_ml/embeddings/text_embeddings_inference.py
@@ -33,7 +33,7 @@ def spawn_server() -> subprocess.Popen:
         ["text-embeddings-router"] + LAUNCH_FLAGS,
         env={
             **os.environ,
-            "HUGGING_FACE_HUB_TOKEN": os.environ["HUGGINGFACE_TOKEN"],
+            "HUGGING_FACE_HUB_TOKEN": os.environ["HF_TOKEN"],
         },
     )
 

diff --git a/06_gpu_and_ml/openai_whisper/finetuning/train/__main__.py b/06_gpu_and_ml/openai_whisper/finetuning/train/__main__.py
@@ -171,13 +171,13 @@ def __call__(
         "mozilla-foundation/common_voice_11_0",
         "hi",
         split="train+validation",
-        use_auth_token=os.environ["HUGGINGFACE_TOKEN"],
+        use_auth_token=os.environ["HF_TOKEN"],
     )
     raw_datasets["eval"] = load_dataset(
         "mozilla-foundation/common_voice_11_0",
         "hi",
         split="test",
-        use_auth_token=os.environ["HUGGINGFACE_TOKEN"],
+        use_auth_token=os.environ["HF_TOKEN"],
     )
 
     # Most ASR datasets only provide input audio samples (audio) and
@@ -211,7 +211,7 @@ def __call__(
         else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=os.environ["HUGGINGFACE_TOKEN"],
+        use_auth_token=os.environ["HF_TOKEN"],
     )
 
     config.update(

diff --git a/06_gpu_and_ml/stable_diffusion/stable_diffusion_aitemplate.py b/06_gpu_and_ml/stable_diffusion/stable_diffusion_aitemplate.py
@@ -58,7 +58,7 @@ def download_and_compile():
         MODEL_ID,
         revision="fp16",
         torch_dtype=torch.float16,
-        use_auth_token=os.environ["HUGGINGFACE_TOKEN"],
+        use_auth_token=os.environ["HF_TOKEN"],
     ).save_pretrained(MODEL_PATH, safe_serialization=True)
 
     diffusers.EulerDiscreteScheduler.from_pretrained(

diff --git a/06_gpu_and_ml/text_generation_inference.py b/06_gpu_and_ml/text_generation_inference.py
@@ -61,7 +61,7 @@ def download_model():
         ],
         env={
             **os.environ,
-            "HUGGING_FACE_HUB_TOKEN": os.environ["HUGGINGFACE_TOKEN"],
+            "HUGGING_FACE_HUB_TOKEN": os.environ["HF_TOKEN"],
         },
         check=True,
     )
@@ -73,7 +73,7 @@ def download_model():
 #
 # Next we run the download step to pre-populate the image with our model weights.
 #
-# For this step to work on a gated model such as LLaMA 2, the HUGGINGFACE_TOKEN environment
+# For this step to work on a gated model such as LLaMA 2, the HF_TOKEN environment
 # variable must be set ([reference](https://github.com/huggingface/text-generation-inference#using-a-private-or-gated-model)).
 #
 # After [creating a HuggingFace access token](https://huggingface.co/settings/tokens),
@@ -130,7 +130,7 @@ def __enter__(self):
             ["text-generation-launcher"] + LAUNCH_FLAGS,
             env={
                 **os.environ,
-                "HUGGING_FACE_HUB_TOKEN": os.environ["HUGGINGFACE_TOKEN"],
+                "HUGGING_FACE_HUB_TOKEN": os.environ["HF_TOKEN"],
             },
         )
         self.client = AsyncClient("http://127.0.0.1:8000", timeout=60)

diff --git a/06_gpu_and_ml/vllm_inference.py b/06_gpu_and_ml/vllm_inference.py
@@ -35,7 +35,7 @@
 # ### Download the weights
 # Make sure you have created a [HuggingFace access token](https://huggingface.co/settings/tokens).
 # To access the token in a Modal function, we can create a secret on the [secrets page](https://modal.com/secrets).
-# Now the token will be available via the environment variable named `HUGGINGFACE_TOKEN`. Functions that inject this secret will have access to the environment variable.
+# Now the token will be available via the environment variable named `HF_TOKEN`. Functions that inject this secret will have access to the environment variable.
 #
 # We can download the model to a particular directory using the HuggingFace utility function `snapshot_download`.
 #
@@ -49,7 +49,7 @@ def download_model_to_folder():
     snapshot_download(
         BASE_MODEL,
         local_dir=MODEL_DIR,
-        token=os.environ["HUGGINGFACE_TOKEN"],
+        token=os.environ["HF_TOKEN"],
     )
     move_cache()
 

diff --git a/10_integrations/pyjulia.py b/10_integrations/pyjulia.py
@@ -3,13 +3,13 @@
 stub = modal.Stub("example-pyjulia")
 stub.image = (
     modal.Image.debian_slim()
-    # Install Julia 1.7
+    # Install Julia 1.10
     .apt_install("wget", "ca-certificates")
     .run_commands(
-        "wget -nv https://julialang-s3.julialang.org/bin/linux/x64/1.7/julia-1.7.2-linux-x86_64.tar.gz",
-        "tar -xf julia-1.7.2-linux-x86_64.tar.gz",
-        "cp -r julia-1.7.2 /opt/",
-        "ln -s /opt/julia-1.7.2/bin/julia /usr/local/bin/julia",
+        "wget -nv https://julialang-s3.julialang.org/bin/linux/x64/1.10/julia-1.10.0-linux-x86_64.tar.gz",
+        "tar -xf julia-1.10.0-linux-x86_64.tar.gz",
+        "cp -r julia-1.10.0 /opt/",
+        "ln -s /opt/julia-1.10.0/bin/julia /usr/local/bin/julia",
     )
     # Install PyJulia bindings
     .pip_install("julia")

diff --git a/10_integrations/stable_diffusion_slackbot.py b/10_integrations/stable_diffusion_slackbot.py
@@ -35,7 +35,7 @@
 # Next, [create a HuggingFace access token](https://huggingface.co/settings/tokens).
 # To access the token in a Modal function, we can create a secret on the
 # [secrets page](https://modal.com/secrets). Let's use the environment variable
-# named `HUGGINGFACE_TOKEN`. Functions that inject this secret will have access
+# named `HF_TOKEN`. Functions that inject this secret will have access
 # to the environment variable.
 #
 # ![create a huggingface token](./huggingface_token.png)
@@ -57,7 +57,7 @@ def fetch_model(local_files_only: bool = False):
 
     return StableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5",
-        use_auth_token=os.environ["HUGGINGFACE_TOKEN"],
+        use_auth_token=os.environ["HF_TOKEN"],
         variant="fp16",
         torch_dtype=float16,
         device_map="auto",