Remove Run on Gradient links

graphcore · Mar 11, 2024 · 08f030f · 08f030f
1 parent 8c4a1dd
commit 08f030f
Show file tree

Hide file tree

Showing 24 changed files with 362 additions and 458 deletions.
diff --git a/notebooks/external_model.ipynb b/notebooks/external_model.ipynb
@@ -40,11 +40,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/3xwTmHM)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -170,7 +166,7 @@
    "outputs": [],
    "source": [
     "from transformers import AutoTokenizer\n",
-    "    \n",
+    "\n",
     "tokenizer_checkpoint = \"gpt2\"\n",
     "tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)"
    ]
@@ -503,7 +499,7 @@
     "    \"optimizer_state_offchip\": True,\n",
     "    \"replicated_tensor_sharding\": True,\n",
     "    \"enable_half_partials\": True,\n",
-    "    \"device_iterations\": 1,      \n",
+    "    \"device_iterations\": 1,\n",
     "    \"inference_device_iterations\": 5,\n",
     "    \"gradient_accumulation_steps\": 512,\n",
     "    \"executable_cache_dir\": executable_cache_dir,\n",

diff --git a/notebooks/flan_t5_inference.ipynb b/notebooks/flan_t5_inference.ipynb
@@ -32,11 +32,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/AWzcPN)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled and a PopTorch wheel installed. Refer to the [Getting Started guide for your system](https://docs.graphcore.ai/en/latest/getting-started.html) for details on how to do this. Also refer to the Jupyter Quick Start guide for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled and a PopTorch wheel installed. Refer to the [Getting Started guide for your system](https://docs.graphcore.ai/en/latest/getting-started.html) for details on how to do this. Also refer to the Jupyter Quick Start guide for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {

diff --git a/notebooks/image_classification.ipynb b/notebooks/image_classification.ipynb
@@ -49,11 +49,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/3QxTCyU)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -339,7 +335,7 @@
    },
    "outputs": [],
    "source": [
-    "from datasets import load_dataset \n",
+    "from datasets import load_dataset\n",
     "from pathlib import Path\n",
     "\n",
     "# load a custom dataset from local/remote files or folders using the ImageFolder feature\n",
@@ -866,7 +862,7 @@
     "from optimum.graphcore import IPUTrainingArguments, IPUTrainer, IPUConfig\n",
     "\n",
     "model = AutoModelForImageClassification.from_pretrained(\n",
-    "    model_checkpoint, \n",
+    "    model_checkpoint,\n",
     "    label2id=label2id,\n",
     "    id2label=id2label,\n",
     "    ignore_mismatched_sizes = True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint\n",

diff --git a/notebooks/introduction_to_optimum_graphcore.ipynb b/notebooks/introduction_to_optimum_graphcore.ipynb
@@ -95,11 +95,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/3CExwVy)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -609,7 +605,7 @@
    "source": [
     "trainer = IPUTrainer(model=model,\n",
     "                     ipu_config=ipu_config,\n",
-    "                     args=training_args, \n",
+    "                     args=training_args,\n",
     "                     train_dataset=train_dataset,\n",
     "                     eval_dataset=validation_features,\n",
     "                    )"
@@ -820,7 +816,7 @@
     "input_tensor = torch.tensor(input_encoding[\"input_ids\"]).unsqueeze(0)\n",
     "attention_tensor= torch.tensor(input_encoding[\"attention_mask\"]).unsqueeze(0)\n",
     "token_types=torch.tensor(input_encoding[\"token_type_ids\"]).unsqueeze(0)\n",
-    "    \n",
+    "\n",
     "# Get model and load the fine-tuned weights\n",
     "model = transformers.BertForQuestionAnswering.from_pretrained(\"/tmp/outputs\")"
    ]
@@ -900,8 +896,8 @@
    "outputs": [],
    "source": [
     "# Make sure you have git-lfs and huggingface-hub\n",
-    "!apt-get update && apt-get upgrade -y && apt-get install -y git git-lfs \n",
-    "# !pip install -y huggingface-hub "
+    "!apt-get update && apt-get upgrade -y && apt-get install -y git git-lfs\n",
+    "# !pip install -y huggingface-hub"
    ]
   },
   {

diff --git a/notebooks/language_modelling_from_scratch.ipynb b/notebooks/language_modelling_from_scratch.ipynb
@@ -57,11 +57,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/414XiNp)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -287,7 +283,7 @@
     "        while pick in picks:\n",
     "            pick = random.randint(0, len(dataset)-1)\n",
     "        picks.append(pick)\n",
-    "    \n",
+    "\n",
     "    df = pd.DataFrame(dataset[picks])\n",
     "    for column, typ in dataset.features.items():\n",
     "        if isinstance(typ, ClassLabel):\n",
@@ -391,7 +387,7 @@
    "outputs": [],
    "source": [
     "from transformers import AutoTokenizer\n",
-    "    \n",
+    "\n",
     "tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)"
    ]
   },

diff --git a/notebooks/mt5_translation.ipynb b/notebooks/mt5_translation.ipynb
@@ -47,11 +47,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)]()\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -243,7 +239,7 @@
     "        while pick in picks:\n",
     "            pick = random.randint(0, len(dataset)-1)\n",
     "        picks.append(pick)\n",
-    "    \n",
+    "\n",
     "    df = pd.DataFrame(dataset[picks])\n",
     "    for column, typ in dataset.features.items():\n",
     "        if isinstance(typ, datasets.ClassLabel):\n",
@@ -299,7 +295,7 @@
    "source": [
     "from transformers import AutoTokenizer\n",
     "from dataclasses import dataclass\n",
-    "    \n",
+    "\n",
     "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
     "\n",
     "@dataclass\n",
@@ -361,7 +357,7 @@
     "    # Setup the tokenizer for targets\n",
     "    with tokenizer.as_target_tokenizer():\n",
     "        labels = tokenizer(targets, max_length=tokenizer_config.max_length, padding=tokenizer_config.padding, truncation=tokenizer_config.truncation)\n",
-    "        \n",
+    "\n",
     "    # Since we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
     "    # padding in the loss.\n",
     "    labels[\"input_ids\"] = [\n",
@@ -510,7 +506,7 @@
     "\n",
     "# Gradient accumulation steps = 128 for 1 replica.\n",
     "# Increase replicas if we have additional ipus available and\n",
-    "# adjust gradient accumulation steps. The trainer will automatically \n",
+    "# adjust gradient accumulation steps. The trainer will automatically\n",
     "# use data parallel training by using the `n_ipu` argument\n",
     "replication_factor = n_ipu // ipu_config.ipus_per_replica\n",
     "gradient_accumulation_steps = 128 // replication_factor\n",

diff --git a/notebooks/mt5_xnli.ipynb b/notebooks/mt5_xnli.ipynb
@@ -38,11 +38,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)]()\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {

diff --git a/notebooks/multiple_choice.ipynb b/notebooks/multiple_choice.ipynb
@@ -45,11 +45,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/3lI3fzQ)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -377,7 +373,7 @@
     "        while pick in picks:\n",
     "            pick = random.randint(0, len(dataset)-1)\n",
     "        picks.append(pick)\n",
-    "    \n",
+    "\n",
     "    df = pd.DataFrame(dataset[picks])\n",
     "    for column, typ in dataset.features.items():\n",
     "        if isinstance(typ, ClassLabel):\n",
@@ -474,7 +470,7 @@
    "outputs": [],
    "source": [
     "from transformers import AutoTokenizer\n",
-    "    \n",
+    "\n",
     "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"
    ]
   },
@@ -551,15 +547,15 @@
     "    # Grab all second sentences possible for each context.\n",
     "    question_headers = examples[\"sent2\"]\n",
     "    second_sentences = [[f\"{header} {examples[end][i]}\" for end in ending_names] for i, header in enumerate(question_headers)]\n",
-    "    \n",
+    "\n",
     "    # Flatten everything\n",
     "    first_sentences = sum(first_sentences, [])\n",
     "    second_sentences = sum(second_sentences, [])\n",
-    "    \n",
+    "\n",
     "    # Tokenize\n",
     "    tokenized_examples = tokenizer(\n",
-    "        first_sentences, \n",
-    "        second_sentences, \n",
+    "        first_sentences,\n",
+    "        second_sentences,\n",
     "        truncation=True,\n",
     "        max_length=max_seq_length,\n",
     "        padding=\"max_length\",\n",

diff --git a/notebooks/name-entity-extraction.ipynb b/notebooks/name-entity-extraction.ipynb
@@ -36,11 +36,7 @@
    "source": [
     "## Environment setup\n",
     "\n",
-    "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n",
-    "\n",
-    "[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/3XgZ7V2)\n",
-    "\n",
-    "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
+    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
    ]
   },
   {
@@ -144,9 +140,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ner_pipeline = pipelines.pipeline(\"ner\", \n",
-    "                                  ipu_config=inference_config, \n",
-    "                                  padding='max_length', \n",
+    "ner_pipeline = pipelines.pipeline(\"ner\",\n",
+    "                                  ipu_config=inference_config,\n",
+    "                                  padding='max_length',\n",
     "                                  max_length=256)"
    ]
   },
@@ -212,7 +208,7 @@
     "out = ner_pipeline(prompt)\n",
     "\n",
     "def app_for_pipeline(pipeline, examples=[], description=\"\", label_description=\"\"):\n",
-    "    demo = gr.Blocks(   \n",
+    "    demo = gr.Blocks(\n",
     "        title=description,\n",
     "    )\n",
     "    with demo:\n",
@@ -398,9 +394,9 @@
    "source": [
     "medical_model = \"d4data/biomedical-ner-all\"\n",
     "ner_pipeline_medical = pipelines.pipeline(\n",
-    "    \"ner\", model= medical_model, \n",
+    "    \"ner\", model= medical_model,\n",
     "    ipu_config=inference_config,\n",
-    "    padding='max_length', \n",
+    "    padding='max_length',\n",
     "    max_length=256\n",
     ")\n",
     "medical_output = ner_pipeline_medical(prompt)"
@@ -441,7 +437,7 @@
    "outputs": [],
    "source": [
     "app_for_pipeline(\n",
-    "    ner_pipeline_medical, \n",
+    "    ner_pipeline_medical,\n",
     "    examples=medical_examples,\n",
     "    description=\"Try prompting me with some medical anecdotes!\"\n",
     ").launch(share=share_gradio)"