transformers_learn_icl_by_gd: add requirements.txt

transformers_learn_icl_by_gd/constructed_token_setup.ipynb

@@ -3,13 +3,25 @@
     {
       "cell_type": "markdown",
       "metadata": {
+        "cellView": "form",
         "id": "nN2OaMWcczfa"
       },
       "source": [
         "# **Transformers learn in-context by gradient descent**\n",
         "This specific notebook can be used to reproduce the results shown in the paper when using the specific token construction i.e. concatinate input and targets i.e. $e_i = (x_i,y_i)$."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#@title Install requirements\n",
+        "!wget -O requirements.txt https://raw.githubusercontent.com/google-research/self-organising-systems/master/transformers_learn_icl_by_gd/requirements.txt\n",
+        "!pip install -r requirements.txt"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -40,17 +52,12 @@
         "import time\n",
         "from typing import Any, MutableMapping, NamedTuple, Tuple\n",
         "\n",
-        "!pip install --quiet --upgrade jax\n",
-        "!pip install --quiet --upgrade jaxlib \n",
         "import jax\n",
         "from jax import grad, jit, vmap\n",
         "import jax.numpy as jnp\n",
         "\n",
-        "!pip install --quiet -U dm-haiku\n",
-        "!pip install --quiet -U optax\n",
         "import haiku as hk\n",
         "import math\n",
-        "!pip install --quiet -U ml_collections\n",
         "from ml_collections import config_dict\n",
         "import matplotlib.pylab as pl\n",
         "import matplotlib.colors as mcolors\n",

transformers_learn_icl_by_gd/non_linear_regression.ipynb

@@ -10,6 +10,19 @@
         "This specific notebook can be used to reproduce the non-linear regression task results."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Install requirements\n",
+        "!wget -O requirements.txt https://raw.githubusercontent.com/google-research/self-organising-systems/master/transformers_learn_icl_by_gd/requirements.txt\n",
+        "!pip install -r requirements.txt"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -39,18 +52,12 @@
         "from tqdm import tqdm_notebook, tnrange\n",
         "import time\n",
         "from typing import Any, MutableMapping, NamedTuple, Tuple\n",
-        "!pip install --quiet --upgrade tensorflow \n",
-        "!pip install --quiet --upgrade jax\n",
-        "!pip install --quiet --upgrade jaxlib \n",
         "import jax\n",
         "from jax import grad, jit, vmap\n",
         "import jax.numpy as jnp\n",
         "\n",
-        "!pip install --quiet -U dm-haiku\n",
-        "!pip install --quiet -U optax\n",
         "import haiku as hk\n",
         "import math\n",
-        "!pip install --quiet -U ml_collections\n",
         "from ml_collections import config_dict\n",
         "import matplotlib.pylab as pl\n",
         "import matplotlib.colors as mcolors\n",

transformers_learn_icl_by_gd/normal_token_construct.ipynb

@@ -10,6 +10,19 @@
         "This specific notebook can be used to reproduce the results that assumes the standard token construction i.e. where $e_{2i} = x_i, e_{2i+1} = y_i$. We here show that the Transformer needs to first copy over neighboring tokens after which it can perform gradient descent steps in the following self-attention layers."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Install requirements\n",
+        "!wget -O requirements.txt https://raw.githubusercontent.com/google-research/self-organising-systems/master/transformers_learn_icl_by_gd/requirements.txt\n",
+        "!pip install -r requirements.txt"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 1,
@@ -39,18 +52,12 @@
         "from tqdm import tqdm_notebook, tnrange\n",
         "import time\n",
         "from typing import Any, MutableMapping, NamedTuple, Tuple\n",
-        "!pip install --quiet --upgrade tensorflow \n",
-        "!pip install --quiet --upgrade jax\n",
-        "!pip install --quiet --upgrade jaxlib \n",
         "import jax\n",
         "from jax import grad, jit, vmap\n",
         "import jax.numpy as jnp\n",
         "\n",
-        "!pip install --quiet -U dm-haiku\n",
-        "!pip install --quiet -U optax\n",
         "import haiku as hk\n",
         "import math\n",
-        "!pip install --quiet -U ml_collections\n",
         "from ml_collections import config_dict\n",
         "import matplotlib.pylab as pl\n",
         "import matplotlib.colors as mcolors\n",

transformers_learn_icl_by_gd/requirements.txt

@@ -0,0 +1,10 @@
+jax==0.3.25
+jaxlib==0.3.25
+dm-haiku==0.0.10
+optax==0.1.7
+ml_collections==0.1.1
+Pillow==9.4.0
+matplotlib==3.7.1
+scipy==1.11.3
+tqdm==4.66.1
+requests==2.31.0