Manual inspection of all key running components

Takes about 5-10min. See Quick Testing below for an expedited variant.

Most of the testing and inspection is standard for Docker-based web apps: docker commands, health check URLs, and optional manual smoke tests. GPU testing and debugging involves additional steps.

To test your base Docker environment for GPU RAPIDS (but not docker-compose), see the main docs and the more 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.

NOTE: Below tests use the deprecated 1.0 REST upload API.

1. Start

Put the container in /var/home/my_user/releases/my_release_1: Ensures relative paths work, and good persistence hygiene across upgrades
Go time!

docker load -i containers.tar.gz
docker-compose up

Check health status via docker ps or via the health check REST APIs. Check resource consumption via docker stats, nvidia-smi, and htop. Note that the set of services evolves across releases:

CONTAINER ID   NAME                              CPU %     MEM USAGE / LIMIT     MEM %     NET I/O           BLOCK I/O         PIDS
34f3cf3f9833   compose_nginx_1                   0.00%     23.91MiB / 31.27GiB   0.07%     10.7MB / 11.4MB   1.3MB / 49.2kB    27
3e876f4acf19   compose_forge-etl-python_1        0.08%     1.376GiB / 2.93GiB    46.96%    137kB / 294kB     186MB / 16.4kB    27
ea097f97aac9   compose_pivot_1                   0.00%     87.87MiB / 31.27GiB   0.27%     27.1kB / 1.91kB   28.5MB / 8.19kB   20
2a2307451f30   compose_dask-cuda-worker_1        1.95%     1.147GiB / 8.789GiB   13.05%    839kB / 1.79MB    70.4MB / 8.19kB   22
6d088ab08cfe   compose_streamgl-viz_1            0.23%     202.6MiB / 31.27GiB   0.63%     52.8kB / 11.4kB   287kB / 8.19kB    23
aaf1d61a5de1   compose_notebook_1                0.00%     213.3MiB / 31.27GiB   0.67%     244kB / 7.24MB    58.5MB / 36.9kB   7
06d8ee55767d   compose_streamgl-gpu_1            0.97%     717MiB / 31.27GiB     2.24%     26kB / 1.17kB     41.5MB / 8.19kB   84
b1c4f9b4de53   compose_nexus_1                   0.03%     140.4MiB / 31.27GiB   0.44%     1.02MB / 1.46MB   106MB / 8.19kB    5
a386ff2f93f9   compose_streamgl-sessions_1       0.03%     149.4MiB / 31.27GiB   0.47%     29.3kB / 1.2kB    29MB / 8.19kB     12
2a46c4645224   compose_dask-scheduler_1          1.04%     142.2MiB / 31.27GiB   0.44%     2.09MB / 913kB    55.9MB / 8.19kB   6
ed3a5d6e25ad   compose_forge-etl_1               0.24%     189.9MiB / 31.27GiB   0.59%     28.1kB / 1.17kB   15.4MB / 8.19kB   23
e9bd7d2fa43c   compose_caddy_1                   0.00%     19.74MiB / 31.27GiB   0.06%     11.4MB / 11.4MB   11.9MB / 4.1kB    26
351b27c75b8c   compose_streamgl-vgraph-etl_1     0.01%     128.3MiB / 31.27GiB   0.40%     30.6kB / 3.93kB   16MB / 8.19kB     12
2c596681e5c9   compose_graph-app-kit-public_1    0.00%     133MiB / 31.27GiB     0.42%     56.3kB / 2.81MB   62.9MB / 8.19kB   6
a31f9f54a506   compose_graph-app-kit-private_1   0.00%     97.94MiB / 31.27GiB   0.31%     27.2kB / 644B     6.5MB / 8.19kB    6
aeed3469d559   compose_autoheal_1                0.00%     4.316MiB / 31.27GiB   0.01%     26.9kB / 0B       4.97MB / 0B       2
2ae36f5256e8   compose_postgres_1                0.00%     55.05MiB / 31.27GiB   0.17%     1.16MB / 912kB    13.8MB / 75.6MB   7
f0bc21b5bda2   compose_redis_1                   0.05%     6.781MiB / 31.27GiB   0.02%     29kB / 10.1kB     4.95MB / 0B       5

Type	Containers
Proxies	`caddy`, `nginx`
Infra	`autoheal`
Web (DB/CMS/accounts)	`nexus` `postgres`, `redis`
GPU services	`streamgl-gpu` image `graphistry/etl-server-python`: `forge-etl-python`, `dask-scheduler`, `dask-cuda-worker`
JS viz services	`streamgl-viz` (heavy), `streamgl-sessions`, `streamgl-vgraph-etl`
Investigation automation	`pivot` (heavy)
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>
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

2. Basic web servers and networking

Check caddy and nginx: Check https://localhost/caddy/health/ (caddy) and http://localhost/healthz (nginx) via your browser or curl -v http://...: should return status code 200
Check nexus for auth, admin, docs, static resources, and API gateway:
- Go to https://localhost
- Should be a login page or your user dashboard

3. GPU visual analysis of preloaded dataset

Go to your https://graphistry/graph/graph.html?dataset=Facebook
- Can also get by point-and-clicking if URL is uncertain: http://graphistry -> Learn More -> (the page)
Expect to see something similar to https://hub.graphistry.com/graph/graph.html?dataset=Facebook
- The first system load (per GPU worker) will be slow due to GPU JIT warmup; wait 30s-1min and refresh if the page loads but no graph is shown
- 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
Upon failures, see below section on GPU testing

4a. Test 1.0 API uploads, Jupyter, and the PyGraphistry client API

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

Install PyGraphistry and check recent version number (Latest: https://pypi.org/project/graphistry/), or use the provided /notebook install:

!pip install graphistry -q
import graphistry
graphistry.__version__

Try your 1.0 API key, will complain if invalid, otherwise silent

graphistry.register(protocol='https', server='my.server.com', key='my_key')

Try upload and viz, may need to open result in new tab if HTTPS notebook for HTTP graphistry. Expect to see a triangle:

import pandas as pd
df = pd.DataFrame({'s': [0,1,2], 'd': [1,2,0]})
graphistry.bind(source='s', destination='d').plot(df)

4b. Test `/etl` by commandline

If you cannot do 3a, test from the host via curl or wget:

Make samplegraph.json (1.0 API format):

{
    "name": "myUniqueGraphName",
    "type": "edgelist",
    "bindings": {
        "sourceField": "src",
        "destinationField": "dst",
        "idField": "node"
    },
    "graph": [
      {"src": "myNode1", "dst": "myNode2",
       "myEdgeField1": "I'm an edge!", "myCount": 7},
      {"src": "myNode2", "dst": "myNode3",
        "myEdgeField1": "I'm also an edge!", "myCount": 200}
    ],
    "labels": [
      {"node": "myNode1",
       "myNodeField1": "I'm a node!",
       "pointColor": 5},
      {"node": "myNode2",
       "myNodeField1": "I'm a node too!",
       "pointColor": 4},
      {"node": "myNode3",
       "myNodeField1": "I'm a node three!",
       "pointColor": 4}
    ]
}

Get a 1.0 API key

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

Upload your 1.0 API data using the key

curl -H "Content-type: application/json" -X POST -d @samplegraph.json https://graphistry/etl?key=YOUR_API_KEY_HERE

From response, go to corresponding https://graphistry/graph/graph.html?dataset=...
- check the viz loads
- check the GPU iteratively clusters

5. Test pivot

5a. Basic

Test it loads at https://graphistry/pivot
Connector page only shows WHOIS and HTTP pivots (https://graphistry/pivot/connectors), and clicking them returns green

5b. Investigation page

Starts empty at https://graphistry/pivot/home
Pressing + creates a new untitled investigations
Can create and run a manual pivot in it, with settings:

Pivot: Enter data
Events: [ { "x": 1, "y": "b"} ]
Nodes: x y
  * Expect to see a graph with 1 event node, and 2 connected entity nodes `1` and `b`

5c. Configurations

Edit data/config/custom.env and docker-compose.yml as per below
- Set each config in one go so you can test more quickly, vs start/stop.
Run

docker-compose stop
docker-compose up

5c.i Persistence

Pivot should persist to ./data already by default, no need to do anything
Run a pivot investigation and save: should see data/{investigation,pivot,workbook_cache,data_cache}/*.json

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
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:

Pivot: Search: Splunk
Query: *
Max Results: 2
Entities: *

Expect to see two orange nodes on the first line, connected to many nodes in the second

5c.iv Connector - Neo4j

Edit .env for NEO4J_BOLT (bolt://...:...), NEO4J_USER, NEO4J_PASSWORD
Test status button in http://graphistry/pivot/connectors
Make a new investigation
- Pivot 1

Pivot: Search: Neo4j
Query: MATCH (a)-[e*2]->(b) RETURN a,e,b
Max Results: 10
Entities: *

Pivot 2

Pivot: Expand: Neo4j
Depends on Pivot 1
Max Results: 20
Steps out: 1..1

Run all: Gets values for both

6. Test TLS Certificates

Cloud:

Ensure domain<>IP assignment
- In EC2/Azure: Allocate an Elastic/Static IP to your instance (may be optional)
- 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
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 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>
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
- NVRTC error: NVRTC_ERROR_INVALID_OPTION: Check GPU/drivers for RAPIDS compatiblility
- forge-etl: restart the service (bad start) or RAPIDS-incompatible GPU/drivers
unhealthy pivot: likely config file issue
unhealthy nginx, nexus, caddy:
- likely config file issue, unable to start due to other upstream services, or public ports are already taken
If a GPU service is unhealthy, the typical cause is an unhealthy Nvidia host or Nvidia container environment setup. Pinpoint the misconfiguration through the following progression, or run as part of etc/scripts/test-gpu.sh (Graphistry 2.33+). For on-prem users, your container.tar load will import Nvidia's official nvidia/cuda container used by Graphistry your version, which can aid pinpointing ecosystem issues outside of Graphistry (v2.33.20+).
- docker run hello-world reports a message <-- tests CPU Docker installation
- nvidia-smi reports available GPUs <-- tests host has a GPU configured with expected GPU driver version number
- docker run --gpus nvidia/cuda nvidia-smi reports available GPUs <-- tests nvidia-docker installation
- docker run --runtime=nvidia nvidia/cuda nvidia-smi reports available GPUs <-- tests nvidia-docker installation
- docker run --rm nvidia/cuda nvidia-smi reports available GPUs <-- tests Docker GPU defaults (used by docker-compose via /etc/docker/daemon.json)
- "docker run --rm graphistry/graphistry-blazing:cat VERSION-dev nvidia-smi" reports available GPUs (public base image) <- tests Graphistry container CUDA versions are compatible with host versions
- "docker run --rm graphistry/etl-server-python:cat VERSION-dev nvidia-smi" reports available GPUs (application image)
- Repeat the docker tests, but with cudf execution. Ex: docker run --rm -it --entrypoint=/bin/bash graphistry/etl-server-python:cat VERSION -c "source activate rapids && 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 / BlazingSQL 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
  - Configure via docker-compose.yml
- URLs: See official list
Pages load
- site.com shows Graphistry homepage and is stylized <-- Static assets are functioning
- site.com/graph/graph.html?dataset=Facebook clusters and renders a graph
  - If the page loads but the graph is empty, see above instructions for testing Nvidia environment
  - Check browser console logs for WebGL errors
  - Check browser and network logs for Websocket errors, which may require a change in Caddy reverse proxying
Notebooks
- Running the analyst notebook example generates running visualizations (see logged-in homepage)
- For further information about the Notebook client, see the OSS project PyGraphistry ( PyPI ).
Investigations
- site.com/pivot loads
- site.com/pivot/connectors loads a list of connectors
  - When clicking the Status button for each connector, it reports green. Check error reported in UI or docker logs (docker compose logs -f -t pivot): likely configuration issues such as password, URL domain vs fqdn, or firewall.
- Opening and running an investigation in site.com/pivot uploads and shows a graph

8. Dashboard

When logged out, ensure site.com/public/dash/ loads (note trailing slash), corresponding to graph-app-kit-public
When logged in as admin/staff, ensure site.com/private/dash/ loads (note trailing slash), corresponding to graph-app-kit-private

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testing-an-install.md

testing-an-install.md

Manual inspection of all key running components

1. Start

2. Basic web servers and networking

3. GPU visual analysis of preloaded dataset

4a. Test 1.0 API uploads, Jupyter, and the PyGraphistry client API

4b. Test `/etl` by commandline

5. Test pivot

5a. Basic

5b. Investigation page

5c. Configurations

5c.i Persistence

5c.ii Connector - Splunk

5c.iv Connector - Neo4j

6. Test TLS Certificates

7. Quick Testing and Test GPU

8. Dashboard

Files

testing-an-install.md

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testing-an-install.md

File metadata and controls

Manual inspection of all key running components

1. Start

2. Basic web servers and networking

3. GPU visual analysis of preloaded dataset

4a. Test 1.0 API uploads, Jupyter, and the PyGraphistry client API

4b. Test /etl by commandline

5. Test pivot

5a. Basic

5b. Investigation page

5c. Configurations

5c.i Persistence

5c.ii Connector - Splunk

5c.iv Connector - Neo4j

6. Test TLS Certificates

7. Quick Testing and Test GPU

8. Dashboard

4b. Test `/etl` by commandline