fix: update kubeflow sdk reference

.github/workflows/test-e2e.yaml

@@ -41,7 +41,7 @@ jobs:
           pip install papermill==2.6.0 jupyter==1.1.1 ipykernel==6.29.5
 
           echo "Install Kubeflow SDK"
-          pip install git+https://github.com/kubeflow/sdk.git@main#subdirectory=python
+          pip install git+https://github.com/kubeflow/sdk.git@main
 
       - name: Setup cluster
         run: |

CONTRIBUTING.md

@@ -135,4 +135,4 @@ On ubuntu the default go package appears to be gccgo-go which has problems. It's
 
 Changes to the Kubeflow Trainer Python SDK can be made in the https://github.com/kubeflow/sdk repo.
 
-The Trainer SDK can be found at https://github.com/kubeflow/sdk/tree/main/python/kubeflow/trainer.
+The Trainer SDK can be found at https://github.com/kubeflow/sdk/tree/main/kubeflow/trainer.

examples/deepspeed/text-summarization/T5-Fine-Tuning.ipynb

@@ -35,9 +35,7 @@
    "id": "4900404c5d532bdf",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# !pip install git+https://github.com/kubeflow/sdk.git@main#subdirectory=python"
-   ]
+   "source": "# !pip install git+https://github.com/kubeflow/sdk.git@main"
   },
   {
    "cell_type": "markdown",

examples/mlx/image-classification/MLX-Distributed-Mnist.ipynb

@@ -82,9 +82,7 @@
    "id": "bd62189280760f42",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# !pip install git+https://github.com/kubeflow/sdk.git@main#subdirectory=python"
-   ]
+   "source": "# !pip install git+https://github.com/kubeflow/sdk.git@main"
   },
   {
    "cell_type": "markdown",

examples/pytorch/image-classification/mnist.ipynb

@@ -31,9 +31,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# !pip install git+https://github.com/kubeflow/sdk.git@main#subdirectory=python"
-   ]
+   "source": "# !pip install git+https://github.com/kubeflow/sdk.git@main"
   },
   {
    "cell_type": "markdown",

examples/pytorch/question-answering/fine-tune-distilbert.ipynb

@@ -38,9 +38,7 @@
    "id": "e5e86ea307b3eec9",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# !pip install git+https://github.com/kubeflow/sdk.git@main#subdirectory=python"
-   ]
+   "source": "# !pip install git+https://github.com/kubeflow/sdk.git@main"
   },
   {
    "cell_type": "markdown",
@@ -116,8 +114,8 @@
       "Requirement already satisfied: pycparser in /Users/andrew/git/trainer/.venv/lib/python3.13/site-packages (from cffi>=1.12->cryptography>=2.1.4->azure-storage-blob>=12->cloudpathlib[all]) (2.22)\n",
       "Requirement already satisfied: pyasn1<0.7.0,>=0.6.1 in /Users/andrew/git/trainer/.venv/lib/python3.13/site-packages (from pyasn1-modules>=0.2.1->google-auth<3.0dev,>=2.26.1->google-cloud-storage->cloudpathlib[all]) (0.6.1)\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.0.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
      ]
     }
    ],
@@ -449,21 +447,21 @@
       "  0%|          | 0/40 [00:00<?, ?it/s][rank0]:[W429 01:22:58.895439547 reducer.cpp:1400] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())\n",
       "[node-0]: [rank1]:[W429 01:22:58.895689005 reducer.cpp:1400] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())\n",
       "100%|██████████| 40/40 [02:36<00:00,  4.10s/it]\n",
-      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\n",
-      " 20%|██        | 2/10 [00:00<00:03,  2.54it/s]\u001b[A\n",
-      " 30%|███       | 3/10 [00:01<00:03,  1.80it/s]\u001b[A\n",
-      " 40%|████      | 4/10 [00:02<00:03,  1.55it/s]\u001b[A\n",
-      " 50%|█████     | 5/10 [00:03<00:03,  1.43it/s]\u001b[A\n",
-      " 60%|██████    | 6/10 [00:03<00:02,  1.37it/s]\u001b[A\n",
-      " 70%|███████   | 7/10 [00:04<00:02,  1.32it/s]\u001b[A\n",
-      " 80%|████████  | 8/10 [00:05<00:01,  1.30it/s]\u001b[A\n",
-      " 90%|█████████ | 9/10 [00:06<00:00,  1.29it/s]\u001b[A\n",
-      "                                               \u001b[A\n",
+      "  0%|          | 0/10 [00:00<?, ?it/s]\u001B[A\n",
+      " 20%|██        | 2/10 [00:00<00:03,  2.54it/s]\u001B[A\n",
+      " 30%|███       | 3/10 [00:01<00:03,  1.80it/s]\u001B[A\n",
+      " 40%|████      | 4/10 [00:02<00:03,  1.55it/s]\u001B[A\n",
+      " 50%|█████     | 5/10 [00:03<00:03,  1.43it/s]\u001B[A\n",
+      " 60%|██████    | 6/10 [00:03<00:02,  1.37it/s]\u001B[A\n",
+      " 70%|███████   | 7/10 [00:04<00:02,  1.32it/s]\u001B[A\n",
+      " 80%|████████  | 8/10 [00:05<00:01,  1.30it/s]\u001B[A\n",
+      " 90%|█████████ | 9/10 [00:06<00:00,  1.29it/s]\u001B[A\n",
+      "                                               \u001B[A\n",
       "[node-0]: {'eval_loss': 5.543211936950684, 'eval_runtime': 7.9713, 'eval_samples_per_second': 2.509, 'eval_steps_per_second': 1.254, 'epoch': 1.0}\n",
       "100%|██████████| 40/40 [02:45<00:00,  4.10s/it]\n",
-      "100%|██████████| 10/10 [00:07<00:00,  1.28it/s]\u001b[A\n",
+      "100%|██████████| 10/10 [00:07<00:00,  1.28it/s]\u001B[A\n",
       "[node-0]: {'train_runtime': 165.12, 'train_samples_per_second': 0.484, 'train_steps_per_second': 0.242, 'train_loss': 5.764264678955078, 'epoch': 1.0}\n",
-      "100%|██████████| 40/40 [02:45<00:00,  4.13s/it]\u001b[A\n"
+      "100%|██████████| 40/40 [02:45<00:00,  4.13s/it]\u001B[A\n"
      ]
     }
    ],

examples/torchtune/llama3_2/alpaca-trainjob-yaml.ipynb

@@ -38,9 +38,7 @@
    "id": "288ec515",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "!pip install git+https://github.com/kubeflow/sdk.git@main#subdirectory=python"
-   ]
+   "source": "!pip install git+https://github.com/kubeflow/sdk.git@main"
   },
   {
    "cell_type": "markdown",