switch default environment for examples in CI to 'examples'

.github/workflows/run-examples.yml

@@ -23,7 +23,7 @@ env:
   TERMINFO: /etc/terminfo
   MODAL_TOKEN_ID: ${{ secrets.MODAL_MODAL_LABS_TOKEN_ID }}
   MODAL_TOKEN_SECRET: ${{ secrets.MODAL_MODAL_LABS_TOKEN_SECRET }}
-  MODAL_ENVIRONMENT: main
+  MODAL_ENVIRONMENT: examples
 
 jobs:
   # Output all changed files in a JSON format compatible with GitHub Actions job matrices

04_secrets/db_to_sheet.py

@@ -1,5 +1,6 @@
 # ---
 # deploy: true
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Write to Google Sheets from Postgres

06_gpu_and_ml/dreambooth/diffusers_lora_finetune.py

@@ -1,5 +1,6 @@
 # ---
 # deploy: true
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Create a character LoRA for Flux with Hugging Face and Gradio

06_gpu_and_ml/embeddings/text_embeddings_inference.py

@@ -1,5 +1,6 @@
 # ---
 # cmd: ["modal", "run", "06_gpu_and_ml/embeddings/text_embeddings_inference.py::embed_dataset"]
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Run TextEmbeddingsInference (TEI) on Modal

06_gpu_and_ml/langchains/potus_speech_qanda.py

@@ -1,6 +1,7 @@
 # ---
-# deploy: true
 # args: ["--query", "How many oil barrels were released from reserves?"]
+# deploy: true
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Retrieval-augmented generation (RAG) for question-answering with LangChain

06_gpu_and_ml/llm-serving/download_llama.py

@@ -1,6 +1,8 @@
 # ---
 # args: ["--force-download"]
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
+
 import modal
 
 MODELS_DIR = "/llamas"

06_gpu_and_ml/llm-serving/vllm_inference.py

@@ -1,6 +1,7 @@
 # ---
 # cmd: ["modal", "serve", "06_gpu_and_ml/llm-serving/vllm_inference.py"]
 # pytest: false
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Run OpenAI-compatible LLM inference with LLaMA 3.1-8B and vLLM

06_gpu_and_ml/llm-structured/instructor_generate.py

@@ -1,5 +1,6 @@
 # ---
 # output-directory: "/tmp/instructor_generate"
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Structured Data Extraction using `instructor`

06_gpu_and_ml/yolo/finetune_yolo.py

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

10_integrations/algolia_indexer.py

@@ -1,5 +1,6 @@
 # ---
 # deploy: true
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 # # Algolia docsearch crawler
 #

10_integrations/cloud_bucket_mount_loras.py

@@ -1,6 +1,7 @@
 # ---
 # output-directory: "/tmp/stable-diffusion-xl"
 # deploy: true
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # LoRAs Galore: Create a LoRA Playground with Modal, Gradio, and S3

10_integrations/dbt/dbt_duckdb.py

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

10_integrations/s3_bucket_mount.py

@@ -1,5 +1,6 @@
 # ---
 # 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

@@ -1,3 +1,7 @@
+# ---
+# 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/laion400.py

@@ -33,17 +33,21 @@
 bucket_creds = modal.Secret.from_name(
     "aws-s3-modal-examples-datasets", environment_name="main"
 )
+
 bucket_name = "modal-examples-datasets"
+
 volume = modal.CloudBucketMount(
     bucket_name,
     secret=bucket_creds,
 )
+
 image = (
     modal.Image.debian_slim()
     .apt_install("wget")
     .pip_install("img2dataset~=1.45.0")
 )
-app = modal.App("example-laoin400-dataset-import", image=image)
+
+app = modal.App("example-laion400-dataset-import", image=image)
 
 
 def start_monitoring_disk_space(interval: int = 30) -> None:

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"}
+# env: {"MODAL_AUTOMOUNT": "True", "MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Build a coding agent with Modal Sandboxes and LangGraph

13_sandboxes/codelangchain/langserve.py

@@ -1,6 +1,7 @@
 # ---
 # pytest: false
 # cmd: ["modal", "serve", "13_sandboxes.codelangchain.langserve"]
+# env: {"MODAL_ENVIRONMENT": "main"}
 # ---
 
 # # Deploy LangChain and LangGraph applications with LangServe