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The Phoenix app can be run in various notebook environments such as colab and SageMaker as well as be served via the terminal or a docker container
Phoenix can be run locally, via a cloud notebook, or as a container
Phoenix app is first and foremost an application that can be run just in in your notebook! This makes it an extremely flexible app since it can be accessed directly as you iterate on your AI-powered app!
Looking how to deploy Phoenix outside of the notebook for production use? Checkout the deployment guide.


Currently phoenix supports local, colab, databricks, and SageMaker notebooks.
Note, phoenix only supports running the phoenix server via the notebook for SageMaker notebooks. It cannot setup proxy requests for SageMaker studio since there is no support of jupyter-server-proxy


With SageMaker notebooks, phoenix leverages the jupyter-server-proy to host the server under proxy/6006.Note, that phoenix will automatically try to detect that you are running in SageMaker but you can declare the notebook runtime via a parameter to launch_app or an environment variable
Environment Variable
Launch Parameter
import os
os.environ["PHOENIX_NOTEBOOK_ENV"] = "sagemaker"
import phoenix as px


Container images are still actively being worked on. If you are interested in hosted phoenix, please get in touch!
Phoenix server images are now available via Docker Hub. The hosted phoenix server runs as a trace collector and can be used if you want observability for LLM traces via docker compose or simply want a long-running phoenix instance. Below are examples of how to run phoenix va Docker for a specific version.
First pull the image you want to run (note you can use the tag latest if you would just like the latest version)
docker pull arizephoenix/phoenix:version-2.9.3
Now you can run the image you pulled (note you must expose the port 6006 so you can view the UI).
docker run -p 6006:6006 arizephoenix/phoenix:version-2.9.3
The Phoenix UI will be available at localhost:6006.
If you deploy the phoenix server (collector) to a remote machine, you will have to make sure to configure the remote endpoint as the collector endpoint. (This feature is only available after phoenix 1.3.x)
Set Endpoint Environment Variable
Set Endpoint in Code
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://123.456.789:6006"
from phoenix.trace.tracer import Tracer
from phoenix.trace.exporter import HttpExporter
from phoenix.trace.openai.instrumentor import OpenAIInstrumentor
tracer = Tracer(exporter=HttpExporter(endpoint="http://123.456.789:6006"))
Note that the above is only necessary if your application is running in a Jupyter notebook. If you are trying to deploy your application and have phoenix collect traces via a container, please consult the deployment guide.


If you want to start a phoenix server to collect traces, you can also run phoenix directly from the command line
python3 -m phoenix.server.main serve
This will start the phoenix server on port 6006. If you are running your instrumented notebook or application on the same machine, traces should automatically be exported to so no additional configuration is needed. However if the server is running remotely, you will have to modify the environment variable PHOENIX_COLLECTOR_ENDPOINT to point to that machine (e.g. http://<my-remote-machine>:<port>)
Note that this command has various configuration options such as --host and --port. For example:
python3 -m phoenix.server.main --port 1234 --host serve


Whether you are using phoenix in a notebook or via a container, you can configure it's runtime via the following environment variables. Note that none of these are required.
  • PHOENIX_PORT: The port to run the phoenix server. Defaults to 6006 (since this port works best with other tools like SageMaker notebooks. )
  • PHOENIX_HOST: The host to run the phoenix server. Defaults to
  • PHOENIX_NOTEBOOK_ENV: The notebook environment. Typically you do not need to set this but it can be set explicitly (e.x. sagemaker)
  • PHOENIX_COLLECTOR_ENDPOINT: The endpoint traces and evals are sent to. This must be set if the Phoenix server is running on a remote instance. For example if phoenix is running at , this environment variable must be set where your LLM application is running and being traced. Note that the endpoint should not contain trailing slashes or slugs.
  • PHOENIX_WORKING_DIR: The directory in which to save, load, and export datasets. This directory must be accessible by both the Phoenix server and the notebook environment.