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It describes how to build the sidecar container image with custom Python dependencies. This sidecar container runs on the TPU workers and facilitates remote Python code execution, enabling seamless integration between the user code and Python-based tasks on the TPU worker. + +**Why use Colocated Python?** + +Colocated Python enables users to execute code that runs explicitly on a specified set of TPU VMs using simple annotations and sharding information. This increases throughput on data or I/O intensive tasks like data loading (as implemented in [MaxText's RemoteIterator class](https://github.com/AI-Hypercomputer/maxtext/blob/391a5a788d85cae8942334b042fdabdbd549af51/MaxText/multihost_dataloading.py#L175)). + +## Examples + +### Simple (No User Dependencies) + +The following small example is modified from [JAX](https://github.com/jax-ml/jax/blob/f4c727abb3989048f49e3d9a4bf2e4052969974b/tests/colocated_python_test.py#L78-L89) with no additional user dependencies installed. It shows how you can use the JAX Colocated Python API to create a file on the specified TPU worker. + +```python +import jax +from jax.experimental import colocated_python +from jax.experimental.colocated_python import serialization + +@colocated_python.colocated_python +def create_a_file(dummy): + """ + Creates a simple file on the TPU worker. + """ + filename = "my_new_file.txt" + content_to_write = f"This is written on TPU worker {jax.process_id}" + + try: + with open(filename, 'w', encoding='utf-8') as file: + file.write(content_to_write) + print(f"Content written to '{filename}'.") + + print(f"File '{filename}' created and closed.") + except IOError as e: + print(f"An error occurred: {e}") + + return dummy + +devices = jax.devices() +dummy_array = np.array(1) +dummy_array = jax.device_put(dummy_array, devices[0]) + +out = create_a_file(dummy_array) +``` + +### Medium (With User Dependencies) + +What if you want to add your own dependencies to do more advanced logic? + +The following is a simple line chart of the first 5 primes in matplotlib that is saved locally to the TPU worker. + +```python +import jax +import numpy as np +from jax.experimental import colocated_python +from jax.experimental.colocated_python import serialization + +# User added dependency +import matplotlib.pyplot as plt + +@colocated_python.colocated_python +def create_and_save_primes_plot(dummy): + """ + Creates a simple matplotlib line plot and saves it as a PNG image + on the TPU worker. + """ + worker_id = jax.process_id() + plot_filename = f"simple_line_plot_worker_{worker_id}.png" + + # Sample data for the plot + x_data = np.array([1, 2, 3, 4, 5]) + y_data = np.array([2, 3, 5, 7, 11]) + + try: + # Create the line plot + plt.figure(figsize=(6, 4)) + plt.plot(x_data, y_data, marker='o', linestyle='-') + + # Add labels and title + plt.xlabel("Nth Prime") + plt.ylabel("Primes") + plt.title(f"Simple Plot from TPU Worker {worker_id}") + plt.grid(True) + + # Save the plot to the specified file + plt.savefig(plot_filename) + print(f"Plot successfully saved to '{plot_filename}' on worker {worker_id}.") + + plt.close() + except Exception as e: + print(f"An error occurred on worker {worker_id} while creating/saving the plot: {e}") + + return dummy + +devices = jax.devices() +dummy_np_array = np.array(1, dtype=np.float32) +dummy_device_array = jax.device_put(dummy_np_array, devices[0]) +out = create_and_save_plot(dummy_device_array) +``` + +### Advanced Usage (With User Dependencies and Control Flow Logic) + +For more advanced usage (such as data loading), check out [MaxText's RemoteIterator class](https://github.com/AI-Hypercomputer/maxtext/blob/391a5a788d85cae8942334b042fdabdbd549af51/MaxText/multihost_dataloading.py#L175). + +### Verification + +To verify files were created, SSH into one of the TPU workers using the following command and check that the file was created. + +`kubectl exec -it -- /bin/sh -c "cat my_new_file.txt"` + +Logs can also be verified by tailing the pod. + +`kubectl logs -f ` + +## Installation and Usage + +Follow these steps to set up, build, and deploy your application with the Colocated Python sidecar. + +**Prerequisites** + +Ensure [Docker](https://docs.docker.com/engine/install/) is installed on your system along with [gcloud](https://cloud.google.com/sdk/docs/install). Ensure you are authenticated into gcloud. + +**1. Clone the Repository** + +Get the necessary code and scripts. + +```bash +git clone https://github.com/AI-Hypercomputer/pathways-utils.git +cd pathwaysutils +``` + +**2. Prepare Sidecar Dependencies** + +Update the file named `requirements.txt`. List all the additional Python packages you need specifically for the sidecar environment, one package per line. + +These dependencies may be the same as your main workload's dependencies. + +``` +# Example requirements.txt +jax>=0.5.1 +tensorflow-datasets +tiktoken +grain-nightly>=0.0.10 +sentencepiece==0.1.97 +``` + +**3. Build the Colocated Python Sidecar Image and upload it to Artifact Registry** + +Use the provided Dockerfile to create the sidecar image. This image will contain the required dependencies in your `requirements.txt`. Also specify the image location to upload to in Artifact Registry + +```bash +export PROJECT_ID= +export IMAGE_LOCATION=us-docker.pkg.dev/${PROJECT_ID}/colocated-python:latest + +docker build -t ${IMAGE_LOCATION} . +``` + +**4. Update Deployment Configuration** + +Modify your Kubernetes deployment YAML file to use your colocated python sidecar image. This assumes you are using the [pathways-job](https://github.com/google/pathways-job) api. + +For example. + +```yaml +... +spec: + maxRestarts: 0 + customComponents: + - componentType: colocated_python_sidecar + image: us-docker.pkg.dev//colocated-python:latest +... +``` + +For a full sample Yaml, please refer to [pathways-job](https://github.com/google/pathways-job/blob/main/config/samples/colocated_python_example_pathwaysjob.yaml). + +**5. (Optional) Turn on Data Loading Optimization in MaxText** + +If using MaxText, to turn on the data loading optimization that uses Colocated Python feature. + +```python +colocated_python_data_input = True +``` + +**6. Deploy the Application** + +Apply the updated deployment configuration to your Kubernetes cluster: + +```bash +kubectl apply -f path/to/your/deployment.yaml +``` + +This will create the necessary pods with your application, pathways head, and the Colocated Python sidecar containers. + +## The Sharp Bits 🔪 + +**User Dependency Conflicts** + +Colocated Python relies on specific internal dependencies, including JAX. Refer to the provided `server_requirements.txt` for the complete list of required dependencies. Using a different dependency version than the one provided in `server_requirements.txt` will cause the remote Python image build to fail. +