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manage start all notebooks, updates on the notebooks of the other hospitals, generalise get_URLs script
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‎projects/aries-fl/manage

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@@ -63,7 +63,7 @@ EOF
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# Default Settings:
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# -----------------------------------------------------------------------------------------------------------------
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# DEFAULT_CONTAINERS="nhstrust-agent nhstrust-notebook regulator-agent regulator-notebook researcher-agent researcher-notebook hospital1-agent hospital1-notebook hospital2-agent hospital2-notebook hospital3-agent hospital3-notebook"
66-
DEFAULT_CONTAINERS="researcher-agent researcher-notebook hospital1-agent hospital1-notebook"
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DEFAULT_CONTAINERS="researcher-agent researcher-notebook hospital1-agent hospital1-notebook hospital2-agent hospital2-notebook hospital3-agent hospital3-notebook"
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# -----------------------------------------------------------------------------------------------------------------
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# Functions:
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# -----------------------------------------------------------------------------------------------------------------

‎projects/aries-fl/notebooks/hospital/Hospital1.ipynb

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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "markdown",
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"id": "indoor-valley",
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"id": "alive-sucking",
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"metadata": {},
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"source": [
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"# Hospital 1\n",
@@ -13,7 +13,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "heated-typing",
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"id": "unnecessary-composite",
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"metadata": {},
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"outputs": [],
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"source": [
@@ -26,7 +26,7 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "enclosed-webmaster",
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"id": "changing-stress",
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"metadata": {},
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"outputs": [],
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"source": [
@@ -36,7 +36,7 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "massive-envelope",
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"id": "invalid-viking",
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"metadata": {},
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"outputs": [],
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"source": [
@@ -56,7 +56,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "colonial-quebec",
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"id": "environmental-letters",
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"metadata": {},
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"outputs": [],
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"source": [
@@ -65,49 +65,53 @@
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},
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{
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"cell_type": "markdown",
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"id": "demanding-advice",
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"id": "scheduled-blood",
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"metadata": {},
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"source": [
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"## Copy from Researcher"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "accepted-chamber",
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"execution_count": 5,
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"id": "casual-market",
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"metadata": {},
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"outputs": [],
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"source": [
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"research_invite = {\"@type\": \"did:sov:BzCbsNYhMrjHiqZDTUASHg;spec/connections/1.0/invitation\", \"@id\": \"06fa0413-825f-4600-8bec-a582e5d75acc\", \"label\": \"Health Researcher\", \"serviceEndpoint\": \"http://172.17.0.1:8040\", \"recipientKeys\": [\"HrC95BgohtYLsTHjn6g4UspCx8tWzviGAg7PbvnRWHjZ\"]}"
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"research_invite = {\"@type\": \"did:sov:BzCbsNYhMrjHiqZDTUASHg;spec/connections/1.0/invitation\", \"@id\": \"8adcb4ac-5b8c-4ccb-8586-7ec87d8da60a\", \"label\": \"Health Researcher\", \"recipientKeys\": [\"EVma5YcwgwHkZYQgpvenCW7BYq5EEdDFmdDEzBiSB4FX\"], \"serviceEndpoint\": \"http://172.30.144.131:8040\"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "magnetic-announcement",
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"execution_count": 6,
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"id": "buried-addition",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State invitation\n",
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"Connection 16b3f361-870c-4864-8e54-f1862cc0304d in State invitation\n",
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State request\n",
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"Connection 16b3f361-870c-4864-8e54-f1862cc0304d in State request\n",
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State response\n",
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"Connection 16b3f361-870c-4864-8e54-f1862cc0304d in State response\n",
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State active\n",
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"Connection 16b3f361-870c-4864-8e54-f1862cc0304d in State active\n",
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"Pending connection moved to active. Challenging with auth policy if set\n",
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"Trusted Research Connection Established - b16c712d-640e-4948-8ac6-ac767f3cddc2\n",
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"ML Message from b16c712d-640e-4948-8ac6-ac767f3cddc2\n",
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"Trusted Research Connection Established - 16b3f361-870c-4864-8e54-f1862cc0304d\n",
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"Connection Handler Called\n",
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"Connection 10944e1d-1a53-46be-b0ec-3424a6d24273 in State invitation\n",
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"Connection Handler Called\n",
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"Connection 10944e1d-1a53-46be-b0ec-3424a6d24273 in State request\n",
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"ML Message from 16b3f361-870c-4864-8e54-f1862cc0304d\n",
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"Open file\n",
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"learning\n",
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"untrained_model.pt\n",
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"HOSPITAL MODEL LOADED\n",
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"HOSPITAL IS TRAINING\n",
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"loss at epoch 0 : tensor(0.2577)\n"
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"loss at epoch 0 : tensor(0.2499)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"loss at epoch 5000 : tensor(0.1234)\n",
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"loss at epoch 10000 : tensor(0.1123)\n",
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"loss at epoch 15000 : tensor(0.1070)\n",
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"loss at epoch 20000 : tensor(0.1005)\n",
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"loss at epoch 25000 : tensor(0.0970)\n",
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"loss at epoch 30000 : tensor(0.0950)\n",
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"loss at epoch 35000 : tensor(0.0935)\n",
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"loss at epoch 40000 : tensor(0.0925)\n",
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"loss at epoch 45000 : tensor(0.0917)\n",
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"loss at epoch 5000 : tensor(0.1313)\n",
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"loss at epoch 10000 : tensor(0.1146)\n",
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"loss at epoch 15000 : tensor(0.1100)\n",
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"loss at epoch 20000 : tensor(0.1043)\n",
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"loss at epoch 25000 : tensor(0.0986)\n",
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"loss at epoch 30000 : tensor(0.0958)\n",
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"loss at epoch 35000 : tensor(0.0942)\n",
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"loss at epoch 40000 : tensor(0.0931)\n",
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"loss at epoch 45000 : tensor(0.0922)\n",
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"Learnt \n",
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"Trained file open\n",
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"connection ID b16c712d-640e-4948-8ac6-ac767f3cddc2\n"
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"connection ID 16b3f361-870c-4864-8e54-f1862cc0304d\n",
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"Connection Handler Called\n",
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"Connection 4feda1ac-0d66-42a7-9a42-cbb711efae97 in State invitation\n",
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"Connection Handler Called\n",
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"Connection 4feda1ac-0d66-42a7-9a42-cbb711efae97 in State request\n"
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]
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}
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],
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "assumed-password",
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"id": "amber-novel",
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"metadata": {},
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"outputs": [],
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"source": []
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.7.6"
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}
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},
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"nbformat": 4,

‎projects/aries-fl/notebooks/hospital/Hospital2.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "completed-question",
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"id": "amended-resort",
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"metadata": {},
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"source": [
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"# Hospital 2\n",
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "cubic-international",
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"id": "selected-strategy",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "piano-joyce",
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"id": "meaning-fiber",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "complex-direction",
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"id": "published-channels",
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"metadata": {},
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"outputs": [],
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"source": [
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"WEBHOOK_HOST = \"0.0.0.0\"\n",
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"WEBHOOK_PORT = 8062\n",
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"WEBHOOK_BASE = \"\"\n",
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"ADMIN_URL = \"http://hospital1-agent:8061\"\n",
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"ADMIN_URL = \"http://hospital2-agent:8061\"\n",
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"\n",
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"agent_config = {\n",
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" \"webhook_host\": WEBHOOK_HOST,\n",
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "thirty-scratch",
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"id": "bigger-civilian",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "afraid-marijuana",
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"id": "moderate-connecticut",
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"metadata": {},
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"source": [
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"## Copy from Researcher"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "based-blackberry",
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"execution_count": 7,
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"id": "advised-greene",
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"metadata": {},
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"outputs": [],
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"source": [
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"research_invite = {\"@type\": \"did:sov:BzCbsNYhMrjHiqZDTUASHg;spec/connections/1.0/invitation\", \"@id\": \"06fa0413-825f-4600-8bec-a582e5d75acc\", \"label\": \"Health Researcher\", \"serviceEndpoint\": \"http://172.17.0.1:8040\", \"recipientKeys\": [\"HrC95BgohtYLsTHjn6g4UspCx8tWzviGAg7PbvnRWHjZ\"]}"
81+
"research_invite = {\"@type\": \"did:sov:BzCbsNYhMrjHiqZDTUASHg;spec/connections/1.0/invitation\", \"@id\": \"cc5fd255-89c9-4e2c-8f2a-a799474821c9\", \"label\": \"Health Researcher\", \"recipientKeys\": [\"Bw9KodyAtMJTkFoYHjqdZWsxQPQAX7jgjxuc5BoepnhQ\"], \"serviceEndpoint\": \"http://172.30.144.131:8040\"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "dated-beverage",
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"execution_count": 8,
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"id": "third-kansas",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State invitation\n",
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State request\n",
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State response\n",
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"Connection Handler Called\n",
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"Connection b16c712d-640e-4948-8ac6-ac767f3cddc2 in State active\n",
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"Pending connection moved to active. Challenging with auth policy if set\n",
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"Trusted Research Connection Established - b16c712d-640e-4948-8ac6-ac767f3cddc2\n",
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"ML Message from b16c712d-640e-4948-8ac6-ac767f3cddc2\n",
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"Open file\n",
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"learning\n",
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"untrained_model.pt\n",
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"HOSPITAL MODEL LOADED\n",
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"HOSPITAL IS TRAINING\n",
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"loss at epoch 0 : tensor(0.2577)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/conda/lib/python3.7/site-packages/torch/autograd/__init__.py:132: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
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" allow_unreachable=True) # allow_unreachable flag\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"loss at epoch 5000 : tensor(0.1234)\n",
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"loss at epoch 10000 : tensor(0.1123)\n",
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"loss at epoch 15000 : tensor(0.1070)\n",
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"loss at epoch 20000 : tensor(0.1005)\n",
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"loss at epoch 25000 : tensor(0.0970)\n",
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"loss at epoch 30000 : tensor(0.0950)\n",
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"loss at epoch 35000 : tensor(0.0935)\n",
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"loss at epoch 40000 : tensor(0.0925)\n",
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"loss at epoch 45000 : tensor(0.0917)\n",
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"Learnt \n",
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"Trained file open\n",
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"connection ID b16c712d-640e-4948-8ac6-ac767f3cddc2\n"
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-8-7ca3898bbdc5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhospital2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_research_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresearch_invite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m/workspace/hospital.py\u001b[0m in \u001b[0;36mestablish_research_connection\u001b[0;34m(self, invitation)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpending_connections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpending_connection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpending_connection\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"is_trusted\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpending_connection\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"is_trusted\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_destroy_pending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 93\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stopping\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/nest_asyncio.py\u001b[0m in \u001b[0;36m_run_once\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0.01\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_proactorloop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m else None)\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mevent_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
101+
"\u001b[0;32m/opt/conda/lib/python3.7/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 466\u001b[0m \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_ev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 469\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 470\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
102+
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
137103
]
138104
}
139105
],
@@ -144,7 +110,7 @@
144110
{
145111
"cell_type": "code",
146112
"execution_count": null,
147-
"id": "cardiovascular-typing",
113+
"id": "pleasant-football",
148114
"metadata": {},
149115
"outputs": [],
150116
"source": []
@@ -166,7 +132,7 @@
166132
"name": "python",
167133
"nbconvert_exporter": "python",
168134
"pygments_lexer": "ipython3",
169-
"version": "3.8.5"
135+
"version": "3.7.6"
170136
}
171137
},
172138
"nbformat": 4,

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