docs(docker compose): update from docker-compose

CHANGELOG.md

@@ -17,6 +17,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 * Reorganized documentation into thematic areas
 * Update RAPIDS base image
 * Update RAPIDS Python env name to `base` from `rapids`
+* Updated most docker compose command references to `docker compose` from `docker-compose`
 
 ### Infra
 

docs/app-config/configure-investigation.md

@@ -35,8 +35,8 @@ Via `data/pivot-db/config/config.json`:
 
 After setting these, restart your server:
 
-* Full: `[email protected] : /var/graphistry $ docker-compose stop && docker-compose up -d`
-* Pivot: `[email protected] : /var/graphistry $ docker-compose stop nginx pivot && docker-compose up -d`
+* Full: `[email protected] : /var/graphistry $ docker compose stop && docker compose up -d`
+* Pivot: `[email protected] : /var/graphistry $ docker compose stop nginx pivot && docker compose up -d`
 
 
 # Schema

docs/app-config/configure-ontology.md

@@ -15,7 +15,7 @@ See below for the list of built-in types they map to.
 ## Define custom ontologies
 
 1. Edit `data/investigations/config/config.json` as per below
-2. Restart docker service `pivot`: `docker-compose restart pivot`
+2. Restart docker service `pivot`: `docker compose restart pivot`
 
 Generally, you can limit the amount of work by mapping custom column names to built-in types, and thereby reuse their preconfigured settings.
 
@@ -73,7 +73,7 @@ For example, to create a new node type `ip`,
 
 2. Restart the pivot service:
 
-```[email protected]:/var/graphistry $ docker-compose stop pivot nginx && docker-compose up -d```
+```[email protected]:/var/graphistry $ docker compose stop pivot nginx && docker compose up -d```
 
 ### Override default node/edge titles
 
@@ -128,7 +128,7 @@ For example, to recognize `src_ip` and `dest_ip` columns as both generating `ip`
 
 2. Restart the pivot service:
 ```
-[email protected]:/var/graphistry $ docker-compose stop pivot nginx && docker-compose up -d
+[email protected]:/var/graphistry $ docker compose stop pivot nginx && docker compose up -d
 ```
 
 ## Built-in types
@@ -190,7 +190,7 @@ You can put any regular expression here:
 Graphistry tries to detect syntax error, and upon one, logs the error and stops. To see what is going on:
 
 `docker ps` <- see if `pivot` is unhealthy or in a restart loop
-`docker-compose logs pivot` <- see the precise error message
+`docker compose logs pivot` <- see the precise error message
 
 2. Satisfactory configuration
 

docs/app-config/configure.md

@@ -99,7 +99,7 @@ For visualizations to be embeddable in different origin sites, enable `COOKIE_SE
 COOKIE_SAMESITE=None
 ```
 
-... then restart: `docker-compose up -d --force-recreate --no-deps nexus`
+... then restart: `docker compose up -d --force-recreate --no-deps nexus`
 
 
 ### Setup free Automatic TLS
@@ -182,8 +182,8 @@ Custom TLS setups often fail due to the certificate, OS, network, Caddy config,
 * Test the certificate
 * Test a [standalone Caddy static file server](https://www.baty.net/2018/using-caddy-for-serving-static-content/)
 * ... Including on another box, if OS/network issues are suspected
-* Check the logs of `docker-compose logs -f -t caddy nginx`
-* Test whether the containers are up and ports match via `docker-compose ps`, `curl`, and `curl` from within a docker container (so within the docker network namespace)
+* Check the logs of `docker compose logs -f -t caddy nginx`
+* Test whether the containers are up and ports match via `docker compose ps`, `curl`, and `curl` from within a docker container (so within the docker network namespace)
 
 If problems persist, please reach out to your Graphistry counterparts. Additional workarounds are possible.
 
@@ -281,7 +281,7 @@ SPLUNK_HOST=...
 
 2. Restart `graphistry`, or at least the `pivot` service:
 
-`docker-compose stop && docker-compose up -d` or `docker-compose stop nginx pivot && docker-compose up -d`
+`docker compose stop && docker compose up -d` or `docker compose stop nginx pivot && docker compose up -d`
 
 3. Test
 

docs/commands.md

@@ -1,6 +1,6 @@
 # Top commands
 
-Graphistry supports advanced command-line administration via standard `docker-compose`, `.yml` / `.env` files, and `caddy` reverse-proxy configuration.
+Graphistry supports advanced command-line administration via standard `docker compose`, `.yml` / `.env` files, and `caddy` reverse-proxy configuration.
 
 ## Login to server
 
@@ -18,17 +18,17 @@ All likely require `sudo`. Run from where your `docker-compose.yml` file is loca
 |  TASK	| COMMAND 	| NOTES 	|
 |--: |:---	|:---	|
 | **Install** 	| `docker load -i containers.tar.gz` 	| Install the `containers.tar.gz` Graphistry release from the current folder. You may need to first run `tar -xvvf my-graphistry-release.tar.gz`.	|
-| **Start <br>interactive** 	| `docker-compose up` 	| Starts Graphistry, close with ctrl-c 	|
-| **Start <br>daemon** 	| `docker-compose up -d` 	| Starts Graphistry as background process 	|
-| **Start <br>namespaced (concurrent)** 	| `docker-compose -p my_unique_namespace up` 	| Starts Graphistry in a specific namespace. Enables running multiple independent instances of Graphistry. NOTE: Must modify Caddy service in `docker-compose.yml` to use non-conflicting public ports, and likewise change global volumes to be independent. 	|
-| **Stop** 	| `docker-compose stop` 	| Stops Graphistry 	|
+| **Start <br>interactive** 	| `docker compose up` 	| Starts Graphistry, close with ctrl-c 	|
+| **Start <br>daemon** 	| `docker compose up -d` 	| Starts Graphistry as background process 	|
+| **Start <br>namespaced (concurrent)** 	| `docker compose -p my_unique_namespace up` 	| Starts Graphistry in a specific namespace. Enables running multiple independent instances of Graphistry. NOTE: Must modify Caddy service in `docker-compose.yml` to use non-conflicting public ports, and likewise change global volumes to be independent. 	|
+| **Stop** 	| `docker compose stop` 	| Stops Graphistry 	|
 | **Restart (soft)** 	| `docker restart <CONTAINER>` 	| Soft restart. May also need to restart service `nginx`. 	|
 | **Restart (hard)** 	| `docker up -d --force-recreate --no-deps <CONTAINER>` 	|  Restart with fresh state. May also need to restart service `nginx`.	|
-| **Reset**     | `docker-compose down -v && docker-compose up -d` | Stop Graphistry, remove all internal state (including the user account database!), and start fresh .  |
-| **Status** 	 | `docker-compose ps`, `docker ps`, and `docker status` 	|  Status: Uptime, healthchecks, ...	|
+| **Reset**     | `docker compose down -v && docker compose up -d` | Stop Graphistry, remove all internal state (including the user account database!), and start fresh .  |
+| **Status** 	 | `docker compose ps`, `docker ps`, and `docker status` 	|  Status: Uptime, healthchecks, ...	|
 | **GPU Status** | `nvidia-smi` | See GPU processes, compute/memory consumption, and driver.  Ex: `watch -n 1.5 nvidia-smi`. Also, `docker run --rm -it nvidia/cuda:11.5.0-base-ubuntu20.04 nvidia-smi` for in-container test. |
-| **1.0 API Key** | docker-compose exec streamgl-vgraph-etl curl "http://0.0.0.0:8080/api/internal/provision?text=MYUSERNAME" 	|  Generates API key for a developer or notebook user	(1.0 API is deprecated)|
-| **Logs** 	|  `docker-compose logs <CONTAINER>` 	|  Ex: Watch all logs, starting with the 20 most recent lines:  `docker-compose logs -f -t --tail=20 forge-etl-python`	. You likely need to switch Docker to use the local json logging driver by  deleting the two default managed Splunk log driver options in `/etc/docker/daemon.json` and then restarting the `docker` daemon (see below). |
+| **1.0 API Key** | docker compose exec streamgl-vgraph-etl curl "http://0.0.0.0:8080/api/internal/provision?text=MYUSERNAME" 	|  Generates API key for a developer or notebook user	(1.0 API is deprecated)|
+| **Logs** 	|  `docker compose logs <CONTAINER>` 	|  Ex: Watch all logs, starting with the 20 most recent lines:  `docker compose logs -f -t --tail=20 forge-etl-python`	. You likely need to switch Docker to use the local json logging driver by  deleting the two default managed Splunk log driver options in `/etc/docker/daemon.json` and then restarting the `docker` daemon (see below). |
 | **Create Users** | Use Admin Panel (see [Create Users](tools/user-creation.md)) or `etc/scripts/rest` |
 | **Restart Docker Daemon** | `sudo service docker restart` | Use when changing `/etc/docker/daemon.json`, ... |
 | **Jupyter shell**| `docker exec -it -u root graphistry_notebook_1 bash` then `source activate rapids` | Use for admin tasks like global package installs |

docs/debugging/debug-faq.md

@@ -19,7 +19,7 @@ Visualization page never returns or Nginx "504 Gateway Time-out" due to services
 * Often with first-ever container launch
 * Likely within 60s of launch
 * Can happen even after static homepage loads
-* In `docker-compose up` logs (or `docker logs ubuntu_central_1`):
+* In `docker compose up` logs (or `docker logs ubuntu_central_1`):
   * "Error: Server at maximum capacity...
   * "Error: Too many users...
   * "Error while assigning...

docs/debugging/performance-tuning.md

@@ -11,7 +11,7 @@ See also [deployment planning](../planning/deployment-planning.md) and [hw/sw pl
   * Check for both memory compute, and network consumption, and by which process 
 * Check logs for potential errors
   * System: Standard OS logs
-  * App: `docker-compose logs`
+  * App: `docker compose logs`
 * Log level impacts performance
   * TRACE: Slow due to heavy CPU <> GPU traffic
   * DEBUG: Will cause large log volumes that require rotation

docs/install/cloud/aws_marketplace.md

@@ -74,9 +74,9 @@ Many `ssh` clients may require you to first run `chmod 400 my_key.pem` or `chmod
 
 Graphistry leverages `docker-compose` and the AWS Marketplace AMI preconfigures the `nvidia` runtime for `docker`.
 
-```
+```bash
 cd ~/graphistry
-sudo docker-compose ps
+sudo docker compose ps
 ```
 
 =>

docs/install/cloud/azure.md

@@ -91,7 +91,7 @@ For steps involving an IP address, see needed IP value at Azure console in `Over
 
 Install docker-compose:
 
-```
+```bash
 sudo curl -L "https://github.com/docker/compose/releases/download/1.23.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
 sudo chmod +x /usr/local/bin/docker-compose
 ```

docs/install/on-prem/index.rst

@@ -28,7 +28,7 @@ Note: In previous versions (< `v2.35`), the file was `containers.tar`
 **2. Launch** from the folder with `docker-compose.yml` if not already up, and likely using `sudo`:
 
 ```bash
-docker-compose up -d
+docker compose up -d
 ```
 
 Note: Takes 1-3 min, and around 5 min, `docker ps` should report all services as `healthy`

docs/install/on-prem/manual.md

@@ -53,7 +53,7 @@ Skip almost all of these steps by instead running through [AWS Marketplace](../c
 
 * **Start from an Nvidia instace**
 <br>You can skip most of the steps by starting with an Nvidia NGC or Tensorflow instance. 
-  * These still typically require installing `docker-compose` (and testing it), setting `/etc/docker/daemon.json` to default to the `nvidia-docker` runtime, and restarting `docker` (and testing it). See end of [RHEL 7.6](rhel_7_6_setup.md) and [Ubuntu 18.04 LTS](ubuntu_18_04_lts_setup.md) sample scripts for install and test instruction.
+  * These still typically require installing `docker compose` (and testing it), setting `/etc/docker/daemon.json` to default to the `nvidia-docker` runtime, and restarting `docker` (and testing it). See end of [RHEL 7.6](rhel_7_6_setup.md) and [Ubuntu 18.04 LTS](ubuntu_18_04_lts_setup.md) sample scripts for install and test instruction.
 * **Start from raw Ubuntu/RHEL**
 <br>You can build from scratch by picking a fully unconfigured starting point and following the [RHEL 7.6](rhel_7_6_setup.md) and [Ubuntu 18.04 LTS](ubuntu_18_04_lts_setup.md) On-Prem Sample instructions. Contact Graphistry staff for automation script assistance if also applicable.
 
@@ -84,6 +84,6 @@ docker load -i containers.tar
 
 ## 5. Start
 
-Launch with `docker-compose up`, and stop with `ctrl-c`. To start as a background daemon, use `docker-compose up -d`.
+Launch with `docker compose up`, and stop with `ctrl-c`. To start as a background daemon, use `docker compose up -d`.
 
 Congratulations, you have installed Graphistry!

docs/install/on-prem/vGPU.md

@@ -17,7 +17,7 @@ A *baremetal OS* (no hypervisor) or *passthrough driver* (hypervisor with non-vG
   * Graphistry already automatically uses all GPUs exposed to it, primarily for scaling to more user sessions
   * New APIs are starting to use multi-GPUs for acceleration as well
 * Multiple Graphistry installs
-  * You can launch concurrent instances of Graphistry using docker: `docker-compose up -p my_unique_namespace_123`
+  * You can launch concurrent instances of Graphistry using docker: `docker compose up -p my_unique_namespace_123`
   * You can configure docker to use different GPUs or share the same ones
 * Isolate Graphistry from other GPU software
   * Docker allows picking which GPUs + CPUs are used

docs/install/testing-an-install.md

@@ -6,7 +6,7 @@ Most of the testing and inspection is standard for Docker-based web apps: `docke
 
 * To test your base Docker environment for GPU RAPIDS, see the in-depth GPU testing section below.
 
-* For logs throughout your session, you can run `docker-compose logs -f -t --tail=1` and `docker-compose logs -f -t --tail=1 SOME_SERVICE_NAME` to see the effects of your activities. Modify `custom.env` to increase `GRAPHISTRY_LOG_LEVEL` and `LOG_LEVEL` to `DEBUG` for increased logging, and `/etc/docker/daemon.json` to use log driver `json-file` for local logs.
+* For logs throughout your session, you can run `docker compose logs -f -t --tail=1` and `docker compose logs -f -t --tail=1 SOME_SERVICE_NAME` to see the effects of your activities. Modify `custom.env` to increase `GRAPHISTRY_LOG_LEVEL` and `LOG_LEVEL` to `DEBUG` for increased logging, and `/etc/docker/daemon.json` to use log driver `json-file` for local logs.
 
 NOTE: Below tests use the deprecated 1.0 REST upload API.
 
@@ -54,9 +54,9 @@ f0bc21b5bda2   compose_redis_1                   0.05%     6.781MiB / 31.27GiB
 | Jupyter notebooks | `notebook` (heavy) |
 | Dashboards | `graph-app-kit-public`, `graph-app-kit-private` |
 
-* It is safe to reset any individual container **except** `postgres`, which is stateful: `docker-compose up -d --force-recreate --no-deps <some_stateless_services>`
+* It is safe to reset any individual container **except** `postgres`, which is stateful: `docker compose up -d --force-recreate --no-deps <some_stateless_services>`
 
-* For any unhealthy container, such as stuck in a restart loop, check `docker-compose logs -f -t --tail=1000 that_service`. To further diagnose, potentially increase the system log level (edit `data/config/custom.env` to have `LOG_LEVEL=DEBUG`, `GRAPHISTRY_LOG_LEVEL=DEBUG`) and recreate + restart the unhealthy container
+* For any unhealthy container, such as stuck in a restart loop, check `docker compose logs -f -t --tail=1000 that_service`. To further diagnose, potentially increase the system log level (edit `data/config/custom.env` to have `LOG_LEVEL=DEBUG`, `GRAPHISTRY_LOG_LEVEL=DEBUG`) and recreate + restart the unhealthy container
 
 * Check `data/config/custom.env` has system-local keys (ex: `STREAMGL_SECRET_KEY`) with fallback to `.env`
 
@@ -77,7 +77,7 @@ f0bc21b5bda2   compose_redis_1                   0.05%     6.781MiB / 31.27GiB
   * If points still do not load, or appear and freeze, likely issues with GPU init (driver) or websocket (firewall)
   * Can also be because preloaded datasets are unavailable: not provided, or externally mounted data sources
     * In this case, use ETL test, and ensure clustering runs for a few seconds (vs. just initial pageload)
-* Check `docker-compose logs -f -t --tail=1` and `docker ps` in case config or GPU driver issues, especially for GPU services listed above
+* Check `docker compose logs -f -t --tail=1` and `docker ps` in case config or GPU driver issues, especially for GPU services listed above
 * Upon failures, see below section on GPU testing
 
 ## 4a. Test 1.0 API uploads, Jupyter, and the PyGraphistry client API
@@ -87,7 +87,7 @@ Do via notebook if possible, else `curl`
 * Get a 1.0 API key by logging into your user's dashboard, or generating a new one using host access:
 
 ```
-docker-compose exec central curl -s http://localhost:10000/api/internal/provision?text=MYUSERNAME
+docker compose exec central curl -s http://localhost:10000/api/internal/provision?text=MYUSERNAME
 ```
 
 * Install PyGraphistry and check recent version number (Latest: https://pypi.org/project/graphistry/), or use the provided `/notebook` install:
@@ -152,7 +152,7 @@ If you cannot do **3a**, test from the host via `curl` or `wget`:
 Login and get the API key from your dashboard homepage, or run the following:
 
 ```
-docker-compose exec central curl -s http://localhost:10000/api/internal/provision?text=MYUSERNAME
+docker compose exec central curl -s http://localhost:10000/api/internal/provision?text=MYUSERNAME
 ```
 
 * Upload your 1.0 API data using the key
@@ -190,8 +190,8 @@ Nodes: x y
   * Set each config in one go so you can test more quickly, vs start/stop. 
 * Run
 ```
-docker-compose stop
-docker-compose up
+docker compose stop
+docker compose up
 ```
 
 
@@ -203,7 +203,7 @@ docker-compose up
 #### 5c.ii Connector - Splunk
 
 * Edit `data/custom/custom.env` for `SPLUNK_HOST`, `SPLUNK_PORT`, `SPLUNK_USER`, `SPLUNK_KEY`
-* Restart the `/pivot` service: `docker-compose restart pivot`
+* Restart the `/pivot` service: `docker compose restart pivot`
 * In `/pivot/connectors`, the `Live Connectors` should list `Splunk`, and clicking `Status` will test logging in
 * In `Investigations and Templates`, create a new investigation:
   * Create and run one pivot:
@@ -244,18 +244,18 @@ Cloud:
   * In Route53/DNS: Assign a domain to your IP, ex: `mytest.graphistry.com`
 * Modify `data/config/Caddyfile` to use your domain name
   * Unlikely: If needed, run `DOMAIN=my.site.com ./scripts/letsencrypt.sh` and `./gen_dhparam.sh`
-  * Restart `docker-compose restart caddy`, check pages load
+  * Restart `docker compose restart caddy`, check pages load
 * Try a notebook upload with `graphistry.register(...., protocol='https')`
 
 ## 7. Quick Testing and Test GPU
 
 Most of the below tests can be automatically run by `cd etc/scripts && ./test-gpu.sh`:
   * Checks `nvidia-smi` works in your OS
-  * Checks `nvidia-smi` works in Docker, including runtime defaults used by `docker-compose`
+  * Checks `nvidia-smi` works in Docker, including runtime defaults used by `docker compose`
   * Checks Nvidia RAPIDS can successfully create CUDA contexts and run a simple on-GPU compute and I/O task of `1 + 1 == 2`
 
 `docker ps` reports no "unhealthy", "restarting", or prolonged "starting" services:
-  * check `docker-compose logs`, `docker-compose logs <service>`, `docker-compose logs -f -t --tail=100 <service>`
+  * check `docker compose logs`, `docker compose logs <service>`, `docker compose logs -f -t --tail=100 <service>`
   * unhealthy `streamgl-gpu`, `forge-etl-python` on start: likely GPU driver issue
     * GPU is not the default runtime in `/etc/docker/deamon.json` (`docker info | grep Default`)
     * `OpenlCL` Initialization error: GPU drivers insufficently setup
@@ -279,7 +279,7 @@ Reports available GPUs (public base image) <- tests Graphistry container CUDA ve
     ``docker run --rm -it --entrypoint=/bin/bash graphistry/etl-server-python:`cat VERSION`-11.8 -c "source activate base && python3 -c \"import cudf; print(cudf.DataFrame({'x': [0,1,2]})['x'].sum())\""``
 Tests Nvidia RAPIDS  (VERSION is your Graphistry version)
   * `docker run graphistry/cljs:1.1 npm test` reports success  <-- tests driver versioning, may be a faulty test however
-  * If running in a hypervisor, ensure `RMM_ALLOCATOR=default` in `data/config/custom.env`, and check the startup logs of `docker-compose logs -f -t --tail=1000 forge-etl-python` that `cudf` / `cupy` are respecting that setting (`LOG_LEVEL=INFO`)
+  * If running in a hypervisor, ensure `RMM_ALLOCATOR=default` in `data/config/custom.env`, and check the startup logs of `docker compose logs -f -t --tail=1000 forge-etl-python` that `cudf` / `cupy` are respecting that setting (`LOG_LEVEL=INFO`)
 * Health checks
   * CLI: Check `docker ps` for per-service status, may take 1-2min for services to connect and warm up
     * Per-service checks run every ~30s after a ~1min initialization delay, with several retries before capped restart