Tutorial (#1599)

Author: ospillinger

examples/pytorch/text-generator/README.md

@@ -4,9 +4,7 @@ _WARNING: you are on the master branch; please refer to examples on the branch c
 
 This example shows how to deploy a realtime text generation API using a GPT-2 model from Hugging Face's transformers library.
 
-<br>
-
-## Implement your predictor
+## Implement your Predictor
 
 1. Create a Python file named `predictor.py`.
 2. Define a Predictor class with a constructor that loads and initializes the model.
@@ -22,7 +20,6 @@ from transformers import GPT2Tokenizer, GPT2LMHeadModel
 class PythonPredictor:
     def __init__(self, config):
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"using device: {self.device}")
         self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
         self.model = GPT2LMHeadModel.from_pretrained("gpt2").to(self.device)
 
@@ -33,11 +30,7 @@ class PythonPredictor:
         return self.tokenizer.decode(prediction[0])
 ```
 
-Here are the complete [Predictor docs](../../../docs/deployments/realtime-api/predictors.md).
-
-<br>
-
-## Specify your Python dependencies
+## Specify Python dependencies
 
 Create a `requirements.txt` file to specify the dependencies needed by `predictor.py`. Cortex will automatically install them into your runtime once you deploy:
 
@@ -48,36 +41,39 @@ torch
 transformers==3.0.*
 ```
 
-<br>
-
-## Configure your API
-
-Create a `cortex.yaml` file and add the configuration below. A `RealtimeAPI` provides a runtime for inference and makes your `predictor.py` implementation available as a web service that can serve realtime predictions:
+## Deploy your model locally
 
-```yaml
-# cortex.yaml
+You can create APIs from any Python runtime that has access to Docker (e.g. the Python shell or a Jupyter notebook):
 
-- name: text-generator
-  kind: RealtimeAPI
-  predictor:
-    type: python
-    path: predictor.py
-```
+```python
+import cortex
 
-Here are the complete [API configuration docs](../../../docs/deployments/realtime-api/api-configuration.md).
+cx_local = cortex.client("local")
 
-<br>
+api_spec = {
+  "name": "text-generator",
+  "kind": "RealtimeAPI",
+  "predictor": {
+    "type": "python",
+    "path": "predictor.py"
+  }
+}
 
-## Deploy your model locally
+cx_local.deploy(api_spec, project_dir=".", wait=True)
+```
 
-`cortex deploy` takes your Predictor implementation along with the configuration from `cortex.yaml` and creates a web API:
+## Consume your API
 
-```bash
-$ cortex deploy
+```python
+import requests
 
-creating text-generator (RealtimeAPI)
+endpoint = cx_local.get_api("text-generator")["endpoint"]
+payload = {"text": "hello world"}
+print(requests.post(endpoint, payload).text)
 ```
 
+## Manage your APIs using the CLI
+
 Monitor the status of your API using `cortex get`:
 
 ```bash
@@ -91,11 +87,11 @@ Show additional information for your API (e.g. its endpoint) using `cortex get <
 
 ```bash
 $ cortex get text-generator
+
 status   last update   avg request   2XX
 live     1m            -             -
 
 endpoint: http://localhost:8889
-...
 ```
 
 You can also stream logs from your API:
@@ -106,18 +102,6 @@ $ cortex logs text-generator
 ...
 ```
 
-Once your API is live, use `curl` to test your API (it will take a few seconds to generate the text):
-
-```bash
-$ curl http://localhost:8889 \
-    -X POST -H "Content-Type: application/json" \
-    -d '{"text": "machine learning is"}'
-
-"machine learning is ..."
-```
-
-<br>
-
 ## Deploy your model to AWS
 
 Cortex can automatically provision infrastructure on your AWS account and deploy your models as production-ready web services:
@@ -126,19 +110,26 @@ Cortex can automatically provision infrastructure on your AWS account and deploy
 $ cortex cluster up
 ```
 
-This creates a Cortex cluster in your AWS account, which will take approximately 15 minutes.
+This creates a Cortex cluster in your AWS account, which will take approximately 15 minutes. After your cluster is created, you can deploy to your cluster by using the same code and configuration as before:
 
-After your cluster is created, you can deploy your model to your cluster by using the same code and configuration as before:
+```python
+import cortex
 
-```bash
-$ cortex deploy --env aws
+cx_aws = cortex.client("aws")
 
-creating text-generator (RealtimeAPI)
-```
+api_spec = {
+  "name": "text-generator",
+  "kind": "RealtimeAPI",
+  "predictor": {
+    "type": "python",
+    "path": "predictor.py"
+  }
+}
 
-_Note that the `--env` flag specifies the name of the CLI environment to use. [CLI environments](../../../docs/miscellaneous/environments.md) contain the information necessary to connect to your cluster. The default environment is `local`, and when the cluster was created, a new environment named `aws` was created to point to the cluster. You can change the default environment with `cortex env default <env_name`)._
+cx_aws.deploy(api_spec, project_dir=".")
+```
 
-Monitor the status of your APIs using `cortex get`:
+Monitor the status of your APIs using `cortex get` using your CLI:
 
 ```bash
 $ cortex get --watch
@@ -156,62 +147,32 @@ Show additional information for your API (e.g. its endpoint) using `cortex get <
 $ cortex get text-generator --env aws
 
 status   up-to-date   requested   last update   avg request   2XX
-live     1            1           17m           -             -
+live     1            1           1m            -             -
 
-metrics dashboard: https://us-west-2.console.aws.amazon.com/cloudwatch/home#dashboards:name=cortex
 endpoint: https://***.execute-api.us-west-2.amazonaws.com/text-generator
-...
 ```
 
-Use your new endpoint to make requests to your API on AWS:
-
-```bash
-$ curl https://***.execute-api.us-west-2.amazonaws.com/text-generator \
-    -X POST -H "Content-Type: application/json" \
-    -d '{"text": "machine learning is"}'
-
-"machine learning is ..."
-```
-
-<br>
-
-## Perform a rolling update
-
-When you make a change to your `predictor.py` or your `cortex.yaml`, you can update your api by re-running `cortex deploy`.
+## Run on GPUs
 
-Let's modify `predictor.py` to set the length of the generated text based on a query parameter:
+If your cortex cluster is using GPU instances (configured during cluster creation) or if you are running locally with an nvidia GPU, you can run your text generator API on GPUs. Add the `compute` field to your API configuration and re-deploy:
 
 ```python
-# predictor.py
-
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
+api_spec = {
+  "name": "text-generator",
+  "kind": "RealtimeAPI",
+  "predictor": {
+    "type": "python",
+    "path": "predictor.py"
+  },
+  "compute": {
+    "gpu": 1
+  }
+}
 
-class PythonPredictor:
-    def __init__(self, config):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"using device: {self.device}")
-        self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-        self.model = GPT2LMHeadModel.from_pretrained("gpt2").to(self.device)
-
-    def predict(self, payload, query_params):  # this line is updated
-        input_length = len(payload["text"].split())
-        output_length = int(query_params.get("length", 20))  # this line is added
-        tokens = self.tokenizer.encode(payload["text"], return_tensors="pt").to(self.device)
-        prediction = self.model.generate(tokens, max_length=input_length + output_length, do_sample=True)  # this line is updated
-        return self.tokenizer.decode(prediction[0])
-```
-
-Run `cortex deploy` to perform a rolling update of your API:
-
-```bash
-$ cortex deploy --env aws
-
-updating text-generator (RealtimeAPI)
+cx_aws.deploy(api_spec, project_dir=".")
 ```
 
-You can track the status of your API using `cortex get`:
+As your new API is initializing, the old API will continue to respond to prediction requests. Once the API's status becomes "live" (with one up-to-date replica), traffic will be routed to the updated version. You can track the status of your API using `cortex get`:
 
 ```bash
 $ cortex get --env aws --watch
@@ -220,78 +181,12 @@ realtime api     status     up-to-date   stale   requested   last update   avg r
 text-generator   updating   0            1       1           29s           -             -
 ```
 
-As your new implementation is initializing, the old implementation will continue to be used to respond to prediction requests. Eventually the API's status will become "live" (with one up-to-date replica), and traffic will be routed to the updated version.
-
-Try your new code:
-
-```bash
-$ curl https://***.execute-api.us-west-2.amazonaws.com/text-generator?length=30 \
-    -X POST -H "Content-Type: application/json" \
-    -d '{"text": "machine learning is"}'
-
-"machine learning is ..."
-```
-
-<br>
-
-## Run on GPUs
-
-If your cortex cluster is using GPU instances (configured during cluster creation), you can run your text generator API on GPUs. Add the `compute` field to your API configuration:
-
-```yaml
-# cortex.yaml
-
-- name: text-generator
-  kind: RealtimeAPI
-  predictor:
-    type: python
-    path: predictor.py
-  compute:
-    gpu: 1
-```
-
-Run `cortex deploy` to update your API with this configuration:
-
-```bash
-$ cortex deploy --env aws
-
-updating text-generator (RealtimeAPI)
-```
-
-You can use `cortex get` to check the status of your API, and once it's live, prediction requests should be faster.
-
-### A note about rolling updates in dev environments
-
-In development environments, you may wish to disable rolling updates since rolling updates require additional cluster resources. For example, a rolling update of a GPU-based API will require at least two GPUs, which can require a new instance to spin up if your cluster only has one instance. To disable rolling updates, set `max_surge` to 0 in the `update_strategy` configuration:
-
-```yaml
-# cortex.yaml
-
-- name: text-generator
-  kind: RealtimeAPI
-  predictor:
-    type: python
-    path: predictor.py
-  compute:
-    gpu: 1
-  update_strategy:
-    max_surge: 0
-```
-
-<br>
-
 ## Cleanup
 
-Run `cortex delete` to delete each API:
+Deleting APIs will free up cluster resources and allow Cortex to scale down to the minimum number of instances you specified during cluster creation:
 
-```bash
-$ cortex delete text-generator --env local
-
-deleting text-generator
-
-$ cortex delete text-generator --env aws
+```python
+cx_local.delete_api("text-generator")
 
-deleting text-generator
+cx_aws.delete_api("text-generator")
 ```
-
-Running `cortex delete` will free up cluster resources and allow Cortex to scale down to the minimum number of instances you specified during cluster creation. It will not spin down your cluster.