Compare commits
No commits in common. "cd5c78d47ffbcff7701f19d32003198b41d96866" and "275bdcada67017e2997c88b8ea281a75bf0823c3" have entirely different histories.
cd5c78d47f
...
275bdcada6
32
fastai/Dockerfile
Normal file
32
fastai/Dockerfile
Normal file
@ -0,0 +1,32 @@
|
||||
# -------------------------------------------------------
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||||
# Relies on JUPYTER_PORT being set in docker-compose.yml
|
||||
# -------------------------------------------------------
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||||
FROM pytorch/pytorch:latest
|
||||
|
||||
# RUN useradd -m -s /bin/bash oleg
|
||||
|
||||
# Install packages
|
||||
# --- this version of jupyter is required to work with the widgets
|
||||
RUN pip install jupyterlab==3.6.7
|
||||
#
|
||||
RUN pip install ipywidgets
|
||||
RUN pip install pandas numpy plotly
|
||||
#
|
||||
RUN pip install fastbook
|
||||
|
||||
# Install additional applications
|
||||
RUN apt update
|
||||
RUN apt-get install graphviz -y
|
||||
|
||||
# Set environment variables, etc.
|
||||
#ENV MY_ENV_VAR=myvalue
|
||||
|
||||
# Set the working directory
|
||||
WORKDIR /workspace
|
||||
|
||||
# Expose the port for JupyterLab
|
||||
EXPOSE ${JUPYTER_PORT}
|
||||
|
||||
# Command to run JupyterLab
|
||||
CMD ["jupyter-lab", "--ip=0.0.0.0", "--port=7777", "--no-browser", "--allow-root", "--NotebookApp.token=''"]
|
||||
|
||||
@ -7,12 +7,10 @@ version: '3.8'
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||||
#==================================================
|
||||
services:
|
||||
jupyter:
|
||||
# build: ${ROOT_DIR}
|
||||
# image: cvtt/fastai:v0.0.1
|
||||
image: cloud21.cvtt.vpn:5500/fastai_jupyter:latest
|
||||
build: ${ROOT_DIR}
|
||||
image: cvtt/fastai:v0.0.1
|
||||
container_name: fastai_jupyter
|
||||
runtime: nvidia
|
||||
restart: unless-stopped
|
||||
environment:
|
||||
- JUPYTER_ENABLE_LAB=yes
|
||||
volumes:
|
||||
@ -20,7 +18,7 @@ services:
|
||||
- ${ROOT_DIR}/jupyter_settings/user-settings:/root/.jupyter/lab/user-settings
|
||||
- ${ROOT_DIR}/jupyter_settings/workspaces:/root/.jupyter/lab/workspaces
|
||||
- ${ROOT_DIR}/.cache/torch:/root/.cache/torch
|
||||
# - /opt/jupyter_gpu/data:/workspace/data
|
||||
ports:
|
||||
- "${JUPYTER_PORT}:${JUPYTER_PORT}"
|
||||
shm_size: "8gb"
|
||||
|
||||
|
||||
3
fastai/notebooks/.gitignore
vendored
3
fastai/notebooks/.gitignore
vendored
@ -1,3 +1,2 @@
|
||||
fastbook
|
||||
bears
|
||||
*.jpg
|
||||
*.jpg
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||||
1
fastai/notebooks/fastbook
Submodule
1
fastai/notebooks/fastbook
Submodule
@ -0,0 +1 @@
|
||||
Subproject commit 054cd8a62c7b6c11f0c36265149ccbaf95d91034
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57
load_market_data.sh
Executable file
57
load_market_data.sh
Executable file
@ -0,0 +1,57 @@
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||||
#!/usr/bin/env bash
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||||
|
||||
date=${1}
|
||||
if [ "${date}" == "" ] ; then
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date="yesterday"
|
||||
fi
|
||||
|
||||
SourceHost=cryptoval3.cvtt.vpn
|
||||
SourceUser=cvtt
|
||||
SourceRootDir=/localdisk/cvtt/archive/md_archive
|
||||
SourceFile=$(date -d ${date} "+%Y%m%d.mktdata.db")
|
||||
SourceFileZip="${SourceFile}.gz"
|
||||
SourceFilePath=$(date -d ${date} "+${SourceRootDir}/%Y/%m/${SourceFileZip}")
|
||||
|
||||
TargetDir="/opt/jupyter_gpu/data/crypto_md"
|
||||
TargetFile=$(date -d ${date} "+%Y%m%d.mktdata.ohlcv.db")
|
||||
TargetFilePath="${TargetDir}/${TargetFile}"
|
||||
|
||||
echo ${SourceFile}
|
||||
tmp_dir=$(mktemp -d)
|
||||
|
||||
function cleanup {
|
||||
cd ${HOME}
|
||||
rm -rf ${tmp_dir}
|
||||
}
|
||||
trap cleanup EXIT
|
||||
|
||||
function download_file {
|
||||
|
||||
Cmd="rsync"
|
||||
Cmd="${Cmd} -ahv"
|
||||
if tty -s; then
|
||||
Cmd="${Cmd} --progress=info2"
|
||||
fi
|
||||
Cmd="${Cmd} ${SourceUser}@${SourceHost}:${SourceFilePath} ${tmp_dir}/"
|
||||
echo ${Cmd}
|
||||
eval ${Cmd}
|
||||
ls -lh ${tmp_dir}
|
||||
Cmd="gunzip ${tmp_dir}/${SourceFileZip}"
|
||||
echo ${Cmd} && eval ${Cmd}
|
||||
ls -lh ${tmp_dir}
|
||||
|
||||
rm -f ${TargetFilePath}
|
||||
touch ${TargetFilePath}
|
||||
|
||||
for table in bnbfut_ohlcv_1min bnbspot_ohlcv_1min coinbase_ohlcv_1min
|
||||
do
|
||||
Cmd="sqlite3 ${tmp_dir}/${SourceFile} \".dump ${table}\" | sqlite3 ${TargetFilePath}"
|
||||
echo ${Cmd}
|
||||
eval ${Cmd}
|
||||
done
|
||||
chmod 600 ${TargetFilePath}
|
||||
ls -lh ${TargetFilePath}
|
||||
|
||||
|
||||
}
|
||||
download_file
|
||||
@ -1,5 +1,2 @@
|
||||
ROOT_DIR=/opt/jupyter_gpu/pytorch
|
||||
DATA_DIR=/opt/jupyter_gpu/data
|
||||
JUPYTER_PORT=8888
|
||||
# PUID=1000
|
||||
# PGID=1000
|
||||
|
||||
@ -18,7 +18,6 @@ RUN apt-get install graphviz -y
|
||||
|
||||
# Set environment variables, etc.
|
||||
#ENV MY_ENV_VAR=myvalue
|
||||
COPY cvttpy /cvttpy
|
||||
|
||||
# Set the working directory
|
||||
WORKDIR /workspace
|
||||
|
||||
@ -3,18 +3,14 @@ version: '3.8'
|
||||
#==================================================
|
||||
# Relies on the file `.env` content for varables:
|
||||
# ROOT_DIR
|
||||
# DATA_DIR
|
||||
# JUPYTER_PORT
|
||||
# UID
|
||||
# GID
|
||||
#==================================================
|
||||
services:
|
||||
jupyter:
|
||||
# user: "${UID}:${GID}"
|
||||
image: cloud21.cvtt.vpn:5500/jupyter_gpu_pytorch:latest
|
||||
build: ${ROOT_DIR}
|
||||
image: cvtt/jupyter_gpu_pt:v1.1.3
|
||||
container_name: jupyter_gpu_pt
|
||||
runtime: nvidia
|
||||
restart: unless-stopped
|
||||
environment:
|
||||
- JUPYTER_ENABLE_LAB=yes
|
||||
- PYTHONPATH=/cvtt/prod
|
||||
@ -23,8 +19,8 @@ services:
|
||||
- ${ROOT_DIR}/jupyter_settings/user-settings:/root/.jupyter/lab/user-settings
|
||||
- ${ROOT_DIR}/jupyter_settings/workspaces:/root/.jupyter/lab/workspaces
|
||||
- ${ROOT_DIR}/.cache/torch:/root/.cache/torch
|
||||
|
||||
- ${DATA_DIR}:/workspace/data
|
||||
- /opt/jupyter_gpu/data:/workspace/data
|
||||
- ${ROOT_DIR}/cvtt:/cvtt
|
||||
ports:
|
||||
- "${JUPYTER_PORT}:${JUPYTER_PORT}"
|
||||
shm_size: "8gb"
|
||||
|
||||
@ -1,263 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c7dac550-c0ed-4ec7-846e-8edb2086c9cc",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Augmented Dickey-Fuller Test (ADF)\n",
|
||||
"Stationarity Test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "778b9362-37e3-40e0-a20a-1ca5e2cddf05",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Preparing The data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "998ecc54-aaba-4761-bb98-1eda5c9fa091",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>id</th>\n",
|
||||
" <th>tstamp</th>\n",
|
||||
" <th>target</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722470400000000000</td>\n",
|
||||
" <td>64640.679892</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722470460000000000</td>\n",
|
||||
" <td>64652.991289</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722470520000000000</td>\n",
|
||||
" <td>64660.005093</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722470580000000000</td>\n",
|
||||
" <td>64653.482847</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722470640000000000</td>\n",
|
||||
" <td>64687.458279</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1372</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722556500000000000</td>\n",
|
||||
" <td>65439.307663</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1373</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722556560000000000</td>\n",
|
||||
" <td>65445.733114</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1374</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722556620000000000</td>\n",
|
||||
" <td>65446.371741</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1375</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722556680000000000</td>\n",
|
||||
" <td>65420.879478</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1376</th>\n",
|
||||
" <td>PAIR-BTC-USDT</td>\n",
|
||||
" <td>1722556740000000000</td>\n",
|
||||
" <td>65377.032222</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>1377 rows × 3 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" id tstamp target\n",
|
||||
"0 PAIR-BTC-USDT 1722470400000000000 64640.679892\n",
|
||||
"1 PAIR-BTC-USDT 1722470460000000000 64652.991289\n",
|
||||
"2 PAIR-BTC-USDT 1722470520000000000 64660.005093\n",
|
||||
"3 PAIR-BTC-USDT 1722470580000000000 64653.482847\n",
|
||||
"4 PAIR-BTC-USDT 1722470640000000000 64687.458279\n",
|
||||
"... ... ... ...\n",
|
||||
"1372 PAIR-BTC-USDT 1722556500000000000 65439.307663\n",
|
||||
"1373 PAIR-BTC-USDT 1722556560000000000 65445.733114\n",
|
||||
"1374 PAIR-BTC-USDT 1722556620000000000 65446.371741\n",
|
||||
"1375 PAIR-BTC-USDT 1722556680000000000 65420.879478\n",
|
||||
"1376 PAIR-BTC-USDT 1722556740000000000 65377.032222\n",
|
||||
"\n",
|
||||
"[1377 rows x 3 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from statsmodels.tsa.stattools import adfuller\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"def demo_example_data() -> pd.Series:\n",
|
||||
" # Generate example time series data\n",
|
||||
" # np.random.seed(0)\n",
|
||||
" time_series_data = np.random.randn(100) # Random data for demonstration\n",
|
||||
" \n",
|
||||
" # Create a pandas Series\n",
|
||||
" data = pd.Series(time_series_data)\n",
|
||||
" \n",
|
||||
" # Optionally, you can add a datetime index if you have time-indexed data\n",
|
||||
" dates = pd.date_range(start='2020-01-01', periods=len(time_series_data), freq='D')\n",
|
||||
" data = pd.Series(time_series_data, index=dates)\n",
|
||||
"\n",
|
||||
" # Display the first few rows of the data\n",
|
||||
" print(data.head())\n",
|
||||
" return data\n",
|
||||
"\n",
|
||||
"def load_df_from_db(file: str, query: str) -> pd.DataFrame:\n",
|
||||
" import sqlite3 \n",
|
||||
" \n",
|
||||
" conn = sqlite3.connect(file)\n",
|
||||
" df = pd.read_sql_query(query, conn)\n",
|
||||
" df['timestamp'] = pd.to_datetime(df['tstamp'])\n",
|
||||
" df.set_index('timestamp', inplace=True)\n",
|
||||
" return df\n",
|
||||
"\n",
|
||||
"file_path = \"/workspace/data/crypto_md/20240801.mktdata.ohlcv.db\"\n",
|
||||
"instrument_id='PAIR-BTC-USDT'\n",
|
||||
"query = f\"\"\"\n",
|
||||
"select \n",
|
||||
" instrument_id as id, \n",
|
||||
" tstamp, \n",
|
||||
" vwap \n",
|
||||
"from bnbspot_ohlcv_1min \n",
|
||||
"where instrument_id = '{instrument_id}'\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"df = load_df_from_db(file=file_path, query=query)\n",
|
||||
"df.rename(columns={'vwap': 'target'}, inplace=True)\n",
|
||||
"# df[\"tstamp2\"] = df.index\n",
|
||||
"df = df.reset_index()\n",
|
||||
"df = df.drop([\"timestamp\"], axis=1) \n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "43e43154-5a04-4a1d-977c-7f930d62f241",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Running Test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "b97d357f-787b-4cff-849f-b91b1ec35e7c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2020-01-01 -1.652908\n",
|
||||
"2020-01-02 -0.157302\n",
|
||||
"2020-01-03 -1.396187\n",
|
||||
"2020-01-04 0.150374\n",
|
||||
"2020-01-05 1.048603\n",
|
||||
"Freq: D, dtype: float64\n",
|
||||
"ADF Statistic: -9.985535987881171\n",
|
||||
"p-value: 2.060269774403535e-17\n",
|
||||
"Critical Values: {'1%': -3.498198082189098, '5%': -2.891208211860468, '10%': -2.5825959973472097}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from statsmodels.tsa.stattools import adfuller\n",
|
||||
"\n",
|
||||
"# Example time series data\n",
|
||||
"data = demo_example_data()\n",
|
||||
"\n",
|
||||
"# Perform the ADF test\n",
|
||||
"result = adfuller(data)\n",
|
||||
"\n",
|
||||
"# Extract and print the results\n",
|
||||
"print('ADF Statistic:', result[0])\n",
|
||||
"print('p-value:', result[1])\n",
|
||||
"print('Critical Values:', result[4])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -10,7 +10,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 8,
|
||||
"id": "6b269e64-be58-43b5-ad60-0fbd1d37861a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -19,8 +19,7 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CUDA is available. Number of GPUs: 1\n",
|
||||
"GPU Name: NVIDIA GeForce RTX 3060\n",
|
||||
"GPU Capability: 8\n"
|
||||
"GPU Name: NVIDIA GeForce RTX 3060\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -31,7 +30,6 @@
|
||||
"if torch.cuda.is_available():\n",
|
||||
" print(f\"CUDA is available. Number of GPUs: {torch.cuda.device_count()}\")\n",
|
||||
" print(f\"GPU Name: {torch.cuda.get_device_name(0)}\")\n",
|
||||
" print(f\"GPU Capability: {torch.cuda.get_device_capability()[0]}\")\n",
|
||||
"else:\n",
|
||||
" print(\"CUDA is not available. No GPU detected.\")\n"
|
||||
]
|
||||
@ -46,7 +44,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 10,
|
||||
"id": "7313a620-a0eb-4207-a12a-90aeee3cd980",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -55,7 +53,7 @@
|
||||
"text/plain": [
|
||||
"('3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]',\n",
|
||||
" environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
|
||||
" 'HOSTNAME': '30c1a1a6daca',\n",
|
||||
" 'HOSTNAME': '6c24ffc52409',\n",
|
||||
" 'JUPYTER_ENABLE_LAB': 'yes',\n",
|
||||
" 'PYTHONPATH': '/cvtt/prod',\n",
|
||||
" 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
|
||||
@ -77,18 +75,14 @@
|
||||
" 'CUDA_MODULE_LOADING': 'LAZY'})"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"sys.path.append(\"/\")\n",
|
||||
"sys.version,os.environ\n",
|
||||
"\n"
|
||||
"sys.version,os.environ"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -96,12 +90,12 @@
|
||||
"id": "943ac637-42c7-4a69-a6c4-94c382e22653",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Test packages availability"
|
||||
"### Test packages avai"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 11,
|
||||
"id": "95d9a2e6-3464-4dbe-9a97-0c2d5eb34193",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -120,29 +114,21 @@
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "566016ad-3f13-4a61-b460-39738ae3bf4c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Jupyter widgets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 12,
|
||||
"id": "eb38de31-fc19-4515-b08d-9cd7607ea958",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "54e9ddbe3f4349ca9ebbee5aaec14477",
|
||||
"model_id": "2b2a98cdd61d477b811279a6753630a2",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"interactive(children=(IntSlider(value=5, description='x', max=1000), Output()), _dom_classes=('widget-interact…"
|
||||
"interactive(children=(IntSlider(value=5, description='x', max=10), Output()), _dom_classes=('widget-interact',…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@ -158,10 +144,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1723585308075979805"
|
||||
"1717721060059418080"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -174,34 +160,17 @@
|
||||
"def f(x):\n",
|
||||
" return x\n",
|
||||
"\n",
|
||||
"interact(f, x=IntSlider(min=0, max=1000, step=1, value=5))\n",
|
||||
"interact(f, x=IntSlider(min=0, max=10, step=1, value=5))\n",
|
||||
"print(\"Done\")\n",
|
||||
"tu.current_nanoseconds()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": null,
|
||||
"id": "f46e46a7-9b57-44aa-9bc9-dcbcf643bc88",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"jupyter-events==0.10.0\n",
|
||||
"jupyter-lsp==2.2.5\n",
|
||||
"jupyter_client==8.6.2\n",
|
||||
"jupyter_core==5.7.2\n",
|
||||
"jupyter_server==2.14.2\n",
|
||||
"jupyter_server_terminals==0.5.3\n",
|
||||
"jupyterlab==4.2.4\n",
|
||||
"jupyterlab_pygments==0.3.0\n",
|
||||
"jupyterlab_server==2.27.3\n",
|
||||
"jupyterlab_widgets==3.0.11\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip freeze | grep jupyter"
|
||||
]
|
||||
|
||||
@ -1,3 +1,2 @@
|
||||
ROOT_DIR=/opt/jupyter_gpu/tensorflow
|
||||
DATA_DIR=/opt/jupyter_gpu/data
|
||||
JUPYTER_PORT=9999
|
||||
|
||||
@ -17,15 +17,6 @@ RUN pip install jupyterlab
|
||||
RUN pip install pandas
|
||||
RUN pip install numpy
|
||||
RUN pip install plotly
|
||||
RUN pip install seaborn
|
||||
RUN pip install statsmodels
|
||||
|
||||
RUN pip install scikit-learn
|
||||
RUN pip install Arch
|
||||
RUN pip install Quantlib
|
||||
RUN pip install Backtrader
|
||||
RUN pip install pyfolio
|
||||
RUN pip install XGBoost
|
||||
|
||||
# Install additional applications
|
||||
RUN apt update
|
||||
|
||||
@ -3,25 +3,24 @@ version: '3.8'
|
||||
#==================================================
|
||||
# Relies on the file `.env` content for varables:
|
||||
# ROOT_DIR
|
||||
# DATA_DIR
|
||||
# JUPYTER_PORT
|
||||
#==================================================
|
||||
|
||||
services:
|
||||
jupyter:
|
||||
image: cloud21.cvtt.vpn:5500/jupyter_gpu_tensorflow:latest
|
||||
# user: "oleg"
|
||||
build: ${ROOT_DIR}
|
||||
image: cvtt/jupyter_gpu_tf:v1.0.5
|
||||
container_name: jupyter_gpu_tf
|
||||
runtime: nvidia
|
||||
restart: unless-stopped
|
||||
environment:
|
||||
- JUPYTER_ENABLE_LAB=yes
|
||||
#- PYTHONPATH=/cvtt/prod
|
||||
- TF_CACHE_DIR=/workspace/tf_cache
|
||||
volumes:
|
||||
- ${ROOT_DIR}/notebooks:/workspace
|
||||
- ${ROOT_DIR}/jupyter_settings/user-settings:/home/oleg/.jupyter/lab/user-settings
|
||||
- ${ROOT_DIR}/jupyter_settings/workspaces:/home/oleg/.jupyter/lab/workspaces
|
||||
- ${DATA_DIR}:/workspace/data
|
||||
- /opt/jupyter_gpu/data:/workspace/data
|
||||
ports:
|
||||
- "${JUPYTER_PORT}:${JUPYTER_PORT}"
|
||||
shm_size: "8gb"
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"id": "89251b34-9738-4fa3-b2db-7d6a7dde7d48",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -24,7 +24,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"id": "eb24b805-ee73-4db4-b1de-0c43bea7d86f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -42,7 +42,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 14,
|
||||
"id": "8dcb0ec1-4e55-4d13-91fc-57c24fac6460",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -246,7 +246,7 @@
|
||||
"[5760 rows x 9 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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Loading…
x
Reference in New Issue
Block a user