move examples that use Secrets out of main (#1011)

* move examples that use Secrets out of main

* typo

04_secrets/db_to_sheet.py

@@ -1,6 +1,5 @@
 # ---
 # deploy: true
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Write to Google Sheets from Postgres
@@ -21,7 +20,7 @@
 
 # First we will enter our database credentials. The easiest way to do this is to click **New
 # secret** and select the **Postgres compatible** Secret preset and fill in the requested
-# information. Then we press **Next** and name our Secret `example-postgres-secret` and click **Create**.
+# information. Then we press **Next** and name our Secret `postgres-secret` and click **Create**.
 
 # ### Google Sheets/GCP
 
@@ -52,7 +51,7 @@
 # 8. A json key file should be downloaded to your computer at this point. Copy the contents of that
 #    file and use it as the value for the `SERVICE_ACCOUNT_JSON` field in your new secret.
 
-# We'll name this other Secret `"my-gsheets-secret"`.
+# We'll name this other Secret `"gsheets-secret"`.
 
 # Now you can access the values of your Secrets from Modal Functions that you annotate with the
 # corresponding `modal.Secret`s, e.g.:
@@ -64,7 +63,7 @@
 app = modal.App("example-db-to-sheet")
 
 
-@app.function(secrets=[modal.Secret.from_name("example-postgres-secret")])
+@app.function(secrets=[modal.Secret.from_name("postgres-secret")])
 def show_host():
     # automatically filled from the specified secret
     print("Host is " + os.environ["PGHOST"])
@@ -76,7 +75,7 @@ def show_host():
 # You can additionally specify
 
 pg_secret = modal.Secret.from_name(
-    "example-postgres-secret",
+    "postgres-secret",
     required_keys=["PGHOST", "PGPORT", "PGDATABASE", "PGUSER", "PGPASSWORD"],
 )
 
@@ -225,7 +224,7 @@ def main():
     image=pygsheets_image,
     secrets=[
         modal.Secret.from_name(
-            "my-gsheets-secret", required_keys=["SERVICE_ACCOUNT_JSON"]
+            "gsheets-secret", required_keys=["SERVICE_ACCOUNT_JSON"]
         )
     ],
 )

06_gpu_and_ml/dreambooth/diffusers_lora_finetune.py

@@ -1,6 +1,5 @@
 # ---
 # deploy: true
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Create a character LoRA for Flux with Hugging Face and Gradio
@@ -133,10 +132,10 @@ class SharedConfig:
 # [gated by a license agreement](https://huggingface.co/docs/hub/en/models-gated) which
 # you must agree to [here](https://huggingface.co/black-forest-labs/FLUX.1-dev).
 # After you have accepted the license, [create a Modal Secret](https://modal.com/secrets)
-# with the name `huggingface` following the instructions in the template.
+# with the name `huggingface-secret` following the instructions in the template.
 
 huggingface_secret = modal.Secret.from_name(
-    "huggingface", required_keys=["HF_TOKEN"]
+    "huggingface-secret", required_keys=["HF_TOKEN"]
 )
 
 image = image.env(
@@ -220,7 +219,7 @@ def load_images(image_urls: list[str]) -> Path:
 # This is especially important if you're fiddling around with the configuration parameters.
 
 # This example can optionally use [Weights & Biases](https://wandb.ai) to track all of this training information.
-# Just sign up for an account, switch the flag below, and add your API key as a [Modal secret](https://modal.com/docs/guide/secrets).
+# Just sign up for an account, switch the flag below, and add your API key as a [Modal Secret](https://modal.com/secrets).
 
 USE_WANDB = False
 
@@ -275,7 +274,7 @@ class TrainConfig(SharedConfig):
     + (
         [
             modal.Secret.from_name(
-                "my-wandb-secret", required_keys=["WANDB_API_KEY"]
+                "wandb-secret", required_keys=["WANDB_API_KEY"]
             )
         ]
         if USE_WANDB

06_gpu_and_ml/embeddings/text_embeddings_inference.py

@@ -1,6 +1,5 @@
 # ---
 # cmd: ["modal", "run", "06_gpu_and_ml/embeddings/text_embeddings_inference.py::embed_dataset"]
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Run TextEmbeddingsInference (TEI) on Modal
@@ -118,7 +117,7 @@ def download_data():
         "bigquery-public-data.hacker_news.full",
         max_results=100_000,
     )
-    df = iterator.to_dataframe(progress_bar_type="tqdm")
+    df = iterator.to_dataframe(progress_bar_type="tqdm").dropna()
 
     df["id"] = df["id"].astype(int)
     df["text"] = df["text"].apply(lambda x: x[:512])

06_gpu_and_ml/langchains/potus_speech_qanda.py

@@ -1,6 +1,5 @@
 # ---
 # args: ["--query", "How many oil barrels were released from reserves?"]
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Retrieval-augmented generation (RAG) for question-answering with LangChain

06_gpu_and_ml/llm-structured/instructor_generate.py

@@ -1,6 +1,5 @@
 # ---
 # output-directory: "/tmp/instructor_generate"
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Structured Data Extraction using `instructor`
@@ -46,7 +45,7 @@
     image=image,
     secrets=[
         modal.Secret.from_name(
-            "my-anthropic-secret", required_keys=["ANTHROPIC_API_KEY"]
+            "anthropic-secret", required_keys=["ANTHROPIC_API_KEY"]
         )
     ],
 )

06_gpu_and_ml/yolo/finetune_yolo.py

@@ -1,6 +1,5 @@
 # ---
 # args: ["--no-quick-check"]
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Fine-tune open source YOLO models for object detection

10_integrations/cloud_bucket_mount_loras.py

@@ -1,7 +1,6 @@
 # ---
 # output-directory: "/tmp/stable-diffusion-xl"
 # deploy: true
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # LoRAs Galore: Create a LoRA Playground with Modal, Gradio, and S3
@@ -17,7 +16,7 @@
 
 # By the end of this example, we've deployed a "playground" app where anyone with a browser can try
 # out these custom models. That's the power of Modal: custom, autoscaling AI applications, deployed in seconds.
-# You can try out our deployment [here](https://modal-labs--loras-galore-ui.modal.run).
+# You can try out our deployment [here](https://modal-labs-examples--loras-galore-ui.modal.run).
 
 # ## Basic setup
 
@@ -86,7 +85,7 @@
 # We went with 800 MiB, but feel free to adapt to what works best for you.
 
 
-@app.function()
+@app.function(secrets=[bucket_secret])
 def search_loras(limit: int, max_model_size: int = 1024 * 1024 * 1024):
     api = huggingface_hub.HfApi()
 

10_integrations/dbt/dbt_duckdb.py

@@ -1,6 +1,5 @@
 # ---
 # deploy: true
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Build your own data warehouse with DuckDB, DBT, and Modal

10_integrations/s3_bucket_mount.py

@@ -1,6 +1,5 @@
 # ---
 # output-directory: "/tmp/s3_bucket_mount"
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Analyze NYC yellow taxi data with DuckDB on Parquet files from S3

10_integrations/webscraper.py

@@ -1,7 +1,3 @@
-# ---
-# env: {"MODAL_ENVIRONMENT": "main"}
-# ---
-
 # # Web Scraping on Modal
 
 # This example shows how you can scrape links from a website and post them to a Slack channel using Modal.

12_datasets/imagenet.py

@@ -1,7 +1,6 @@
 # ---
 # deploy: true
 # lambda-test: false
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 #
 # This scripts demonstrates how to ingest the famous ImageNet (https://www.image-net.org/)

13_sandboxes/codelangchain/README.md

@@ -3,8 +3,8 @@
 This example deploys a "code agent": a language model that can write and execute
 code in a flexible control flow aimed at completing a task or goal.
 
-It is implemented in LangChain, using the LangGraph library to structure the agent
-and the LangServe framework to turn it into a FastAPI app.
+It is implemented in LangChain, using the LangGraph library to structure the
+agent and the LangServe framework to turn it into a FastAPI app.
 
 We use Modal to turn that app into a web endpoint. We also use Modal to
 "sandbox" the agent's code execution, so that it can't accidentally (or when
@@ -19,14 +19,13 @@ the project's context and implementation. Check it out if you're curious!
 To run this app, you need to `pip install modal` and then create the following
 [secrets](https://modal.com/docs/guide/secrets):
 
-- `openai-secret` with an OpenAI API key, so that we can query OpenAI's
-  models to power the agent,
-- and `my-langsmith-secret` with a LangSmith API key, so that we can monitor the
+- `openai-secret` with an OpenAI API key, so that we can query OpenAI's models
+  to power the agent,
+- and `langsmith-secret` with a LangSmith API key, so that we can monitor the
   agent's behavior with LangSmith.
 
-Head to the
-[secret creation dashboard](https://modal.com/secrets/)
-and follow the instructions for each secret type.
+Head to the [secret creation dashboard](https://modal.com/secrets/) and follow
+the instructions for each secret type.
 
 Then, you can deploy the app with:
 
@@ -42,7 +41,8 @@ You can also navigate to the `/docs` path to see OpenAPI/Swagger docs, for
 everything you'd need to see how to incorporate the agent into your downstream
 applications via API requests.
 
-When developing the app, use `modal serve codelangchain.py` to get a hot-reloading server.
+When developing the app, use `modal serve codelangchain.py` to get a
+hot-reloading server.
 
 ## Repo structure
 

13_sandboxes/codelangchain/agent.py

@@ -1,7 +1,7 @@
 # ---
 # cmd: ["modal", "run", "13_sandboxes.codelangchain.agent", "--question", "Use gpt2 and transformers to generate text"]
 # pytest: false
-# env: {"MODAL_AUTOMOUNT": "True", "MODAL_ENVIRONMENT": "main"}
+# env: {"MODAL_AUTOMOUNT": "True"}
 # ---
 
 # # Build a coding agent with Modal Sandboxes and LangGraph
@@ -36,7 +36,7 @@
             "openai-secret", required_keys=["OPENAI_API_KEY"]
         ),
         modal.Secret.from_name(
-            "my-langsmith-secret", required_keys=["LANGCHAIN_API_KEY"]
+            "langsmith-secret", required_keys=["LANGCHAIN_API_KEY"]
         ),
     ],
 )

13_sandboxes/codelangchain/langserve.py

@@ -1,7 +1,6 @@
 # ---
 # pytest: false
 # cmd: ["modal", "serve", "13_sandboxes.codelangchain.langserve"]
-# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Deploy LangChain and LangGraph applications with LangServe
@@ -33,7 +32,7 @@
             "openai-secret", required_keys=["OPENAI_API_KEY"]
         ),
         modal.Secret.from_name(
-            "my-langsmith-secret", required_keys=["LANGCHAIN_API_KEY"]
+            "langsmith-secret", required_keys=["LANGCHAIN_API_KEY"]
         ),
     ],
 )