ci

.github/workflows/module-1.yaml

@@ -98,7 +98,7 @@ jobs:
 
       - name: Login 
         run: |
-          docker login ghcr.io -u truskovskiyk -p ${{ secrets.GITHUB_TOKEN }}
+          docker login ghcr.io -u truskovskiyk -p ${{ secrets.GH_TOKEN }}
 
       - name: Build 
         run: |
@@ -110,7 +110,7 @@ jobs:
 
       - name: Run ok
         run: |
-          docker run --rm --name app-ml-test-run kyrylprojector/app-ml:latest
+          docker run --rm --name app-ml-test-run ghcr.io/kyryl-opens-ml/app-ml:latest
 
         
   k8s-test-deployment-action:
@@ -124,7 +124,7 @@ jobs:
 
       - name: Deploy application
         run: |
-          kubectl create -f week-1/k8s-resources/deployment-app-web.yaml
+          kubectl create -f module-1/k8s-resources/deployment-app-web.yaml
 
       - name: Print pods
         run: |
@@ -141,6 +141,12 @@ jobs:
 
   modal-lab-example-run:
     runs-on: ubuntu-latest
+
+    env:
+      MODAL_TOKEN_ID: ${{ secrets.MODAL_MODAL_LABS_TOKEN_ID }}
+      MODAL_TOKEN_SECRET: ${{ secrets.MODAL_MODAL_LABS_TOKEN_SECRET }}
+      MODAL_ENVIRONMENT: main
+
     steps:
       - uses: actions/checkout@v4
 
@@ -151,3 +157,7 @@ jobs:
       - name: Install modal
         run: |
           pip install modal --upgrade
+
+      - name: Run function
+        run: |
+          modal run ./module-1/modal_hello_world.py

module-1/modal_hello_world.py

@@ -0,0 +1,81 @@
+
+import sys
+import modal
+
+stub = modal.Stub("example-hello-world")
+
+@stub.function()
+def f(i):
+    if i % 2 == 0:
+        print("hello", i)
+    else:
+        print("world", i, file=sys.stderr)
+
+    return i * i
+
+@stub.local_entrypoint()
+def main():
+    # run the function locally
+    print(f.local(1000))
+
+    # run the function remotely on Modal
+    print(f.remote(1000))
+
+    # run the function in parallel and remotely on Modal
+    total = 0
+    for ret in f.map(range(20)):
+        total += ret
+
+    print(total)
+
+
+# Enter `modal run hello_world.py` in a shell and you'll see
+# a Modal app initialize.
+# You'll then see the `print`ed logs of
+# the `main` function and, mixed in with them, all the logs of `f` as it is run
+# locally, then remotely, and then remotely and in parallel.
+#
+# That's all triggered by adding the [`@stub.local_entrypoint`](/docs/reference/modal.Stub#local_entrypoint) decorator on `main`,
+# which defines it as the function to start from locally when we invoke `modal run`.
+#
+# ## What just happened?
+#
+# When we called `.remote` on `f`, the function was executed
+# **in the cloud**, on Modal's infrastructure, not locally on our computer.
+#
+# In short, we took the function `f`, put it inside a container,
+# sent it the inputs, and streamed back the logs and outputs.
+#
+# ## But why does this matter?
+#
+# Try doing one of these things next to start seeing the full power of Modal!
+#
+# ### You can change the code and run it again
+#
+# For instance, change the `print` statement in the function `f`
+# to print `"spam"` and `"eggs"` instead and run the app again.
+# You'll see that that your new code is run with no extra work from you --
+# and it should even run faster!
+#
+# Modal's goal is to make running code in the cloud feel like you're
+# running code locally. That means no waiting for long image builds when you've just moved a comma,
+# no fiddling with container image pushes, and no context-switching to a web UI to inspect logs.
+#
+# ### You can map over more data
+#
+# Change the `map` range from `20` to some large number, like `1170`. You'll see
+# Modal create and run even more containers in parallel this time.
+#
+# And it'll happen lightning fast!
+#
+# ### You can run a more interesting function
+#
+# The function `f` is obviously silly and doesn't do much, but in its place
+# imagine something that matters to you, like:
+#
+# * Running [language model inference](/docs/examples/vllm_mixtral) or [fine-tuning](/docs/examples/slack-finetune)
+# * Manipulating [audio](/docs/examples/discord-musicgen) or [images](stable_diffusion_xl_turbo)
+# * [Collecting financial data](/docs/examples/fetch_stock_prices) to backtest a trading algorithm.
+#
+# Modal lets you parallelize that operation effortlessly by running hundreds or
+# thousands of containers in the cloud.