Custom server container registry (#713)

docs/examples/custom/README.ipynb

@@ -54,27 +54,29 @@
     "from jax import random\n",
     "from numpyro.infer import MCMC, NUTS\n",
     "\n",
-    "DATASET_URL = 'https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv'\n",
-    "dset = pd.read_csv(DATASET_URL, sep=';')\n",
+    "DATASET_URL = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv\"\n",
+    "dset = pd.read_csv(DATASET_URL, sep=\";\")\n",
     "\n",
     "standardize = lambda x: (x - x.mean()) / x.std()\n",
     "\n",
-    "dset['AgeScaled'] = dset.MedianAgeMarriage.pipe(standardize)\n",
-    "dset['MarriageScaled'] = dset.Marriage.pipe(standardize)\n",
-    "dset['DivorceScaled'] = dset.Divorce.pipe(standardize)\n",
+    "dset[\"AgeScaled\"] = dset.MedianAgeMarriage.pipe(standardize)\n",
+    "dset[\"MarriageScaled\"] = dset.Marriage.pipe(standardize)\n",
+    "dset[\"DivorceScaled\"] = dset.Divorce.pipe(standardize)\n",
+    "\n",
     "\n",
     "def model(marriage=None, age=None, divorce=None):\n",
-    "    a = numpyro.sample('a', dist.Normal(0., 0.2))\n",
-    "    M, A = 0., 0.\n",
+    "    a = numpyro.sample(\"a\", dist.Normal(0.0, 0.2))\n",
+    "    M, A = 0.0, 0.0\n",
     "    if marriage is not None:\n",
-    "        bM = numpyro.sample('bM', dist.Normal(0., 0.5))\n",
+    "        bM = numpyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n",
     "        M = bM * marriage\n",
     "    if age is not None:\n",
-    "        bA = numpyro.sample('bA', dist.Normal(0., 0.5))\n",
+    "        bA = numpyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n",
     "        A = bA * age\n",
-    "    sigma = numpyro.sample('sigma', dist.Exponential(1.))\n",
+    "    sigma = numpyro.sample(\"sigma\", dist.Exponential(1.0))\n",
     "    mu = a + M + A\n",
-    "    numpyro.sample('obs', dist.Normal(mu, sigma), obs=divorce)\n",
+    "    numpyro.sample(\"obs\", dist.Normal(mu, sigma), obs=divorce)\n",
+    "\n",
     "\n",
     "# Start from this source of randomness. We will split keys for subsequent operations.\n",
     "rng_key = random.PRNGKey(0)\n",
@@ -85,7 +87,9 @@
     "# Run NUTS.\n",
     "kernel = NUTS(model)\n",
     "mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples)\n",
-    "mcmc.run(rng_key_, marriage=dset.MarriageScaled.values, divorce=dset.DivorceScaled.values)\n",
+    "mcmc.run(\n",
+    "    rng_key_, marriage=dset.MarriageScaled.values, divorce=dset.DivorceScaled.values\n",
+    ")\n",
     "mcmc.print_summary()"
    ]
   },
@@ -113,9 +117,9 @@
     "serialisable = {}\n",
     "for k, v in samples.items():\n",
     "    serialisable[k] = np.asarray(v).tolist()\n",
-    "    \n",
+    "\n",
     "model_file_name = \"numpyro-divorce.json\"\n",
-    "with open(model_file_name, 'w') as model_file:\n",
+    "with open(model_file_name, \"w\") as model_file:\n",
     "    json.dump(serialisable, model_file)"
    ]
   },
@@ -309,14 +313,7 @@
     "\n",
     "x_0 = [28.0]\n",
     "inference_request = {\n",
-    "    \"inputs\": [\n",
-    "        {\n",
-    "          \"name\": \"marriage\",\n",
-    "          \"shape\": [1],\n",
-    "          \"datatype\": \"FP32\",\n",
-    "          \"data\": x_0\n",
-    "        }\n",
-    "    ]\n",
+    "    \"inputs\": [{\"name\": \"marriage\", \"shape\": [1], \"datatype\": \"FP32\", \"data\": x_0}]\n",
     "}\n",
     "\n",
     "endpoint = \"http://localhost:8080/v2/models/numpyro-divorce/infer\"\n",
@@ -410,21 +407,14 @@
     "\n",
     "x_0 = [28.0]\n",
     "inference_request = {\n",
-    "    \"inputs\": [\n",
-    "        {\n",
-    "          \"name\": \"marriage\",\n",
-    "          \"shape\": [1],\n",
-    "          \"datatype\": \"FP32\",\n",
-    "          \"data\": x_0\n",
-    "        }\n",
-    "    ]\n",
+    "    \"inputs\": [{\"name\": \"marriage\", \"shape\": [1], \"datatype\": \"FP32\", \"data\": x_0}]\n",
     "}\n",
     "\n",
     "endpoint = \"http://localhost:8080/v2/models/numpyro-divorce/infer\"\n",
     "response = requests.post(endpoint, json=inference_request)\n",
     "\n",
     "print(response)\n",
-    "print(response.text)\n"
+    "print(response.text)"
    ]
   },
   {
@@ -448,6 +438,10 @@
     "There is a large number of tools out there to deploy images.\n",
     "However, for our example, we will focus on deploying it to a cluster running [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/).\n",
     "\n",
+    "```{note}\n",
+    "Also consider that depending on your Kubernetes installation Seldon Core might expect to get the container image from a public container registry like [Docker hub](https://hub.docker.com/) or [Google Container Registry](https://cloud.google.com/container-registry). For that you need to do an extra step of pushing the container to the registry using `docker tag <image name> <container registry>/<image name>` and `docker push <container registry>/<image name>` and also updating the `image` section of the yaml file to `<container registry>/<image name>`. \n",
+    "```\n",
+    "\n",
     "For that, we will need to create a `SeldonDeployment` resource which instructs Seldon Core to deploy a model embedded within our custom image and compliant with the [V2 Inference Protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2).\n",
     "This can be achieved by _applying_ (i.e. `kubectl apply`) a `SeldonDeployment` manifest to the cluster, similar to the one below:"
    ]

docs/examples/custom/README.md

@@ -322,6 +322,10 @@ Now that we've built a custom image and verified that it works as expected, we c
 There is a large number of tools out there to deploy images.
 However, for our example, we will focus on deploying it to a cluster running [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/).
 
+```{note}
+Also consider that depending on your Kubernetes installation Seldon Core might excpect to get the container image from a public container registry like [Docker hub](https://hub.docker.com/) or [Google Container Registry](https://cloud.google.com/container-registry). For that you need to do an extra step of pushing the container to the registry using `docker tag <image name> <container registry>/<image name>` and `docker push <container registry>/<image name>` and also updating the `image` section of the yaml file to `<container registry>/<image name>`. 
+```
+
 For that, we will need to create a `SeldonDeployment` resource which instructs Seldon Core to deploy a model embedded within our custom image and compliant with the [V2 Inference Protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2).
 This can be achieved by _applying_ (i.e. `kubectl apply`) a `SeldonDeployment` manifest to the cluster, similar to the one below: