progress
This commit is contained in:
parent
da781060e0
commit
275bdcada6
1
.gitignore
vendored
1
.gitignore
vendored
@ -7,3 +7,4 @@
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data
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*webp
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jupyter_gpu.code-workspace
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cvtt
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1
pytorch/.gitignore
vendored
Normal file
1
pytorch/.gitignore
vendored
Normal file
@ -0,0 +1 @@
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||||
**/AutogluonModels/
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@ -8,17 +8,19 @@ version: '3.8'
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||||
services:
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jupyter:
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build: ${ROOT_DIR}
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image: cvtt/jupyter_gpu_pt:v1.1.2
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image: cvtt/jupyter_gpu_pt:v1.1.3
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container_name: jupyter_gpu_pt
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||||
runtime: nvidia
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||||
environment:
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||||
- JUPYTER_ENABLE_LAB=yes
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||||
- PYTHONPATH=/cvtt/prod
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||||
volumes:
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- ${ROOT_DIR}/notebooks:/workspace
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||||
- ${ROOT_DIR}/jupyter_settings/user-settings:/root/.jupyter/lab/user-settings
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||||
- ${ROOT_DIR}/jupyter_settings/workspaces:/root/.jupyter/lab/workspaces
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||||
- ${ROOT_DIR}/.cache/torch:/root/.cache/torch
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||||
- /opt/jupyter_gpu/data:/workspace/data
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- ${ROOT_DIR}/cvtt:/cvtt
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||||
ports:
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||||
- "${JUPYTER_PORT}:${JUPYTER_PORT}"
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shm_size: "8gb"
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89
pytorch/get_version.sh
Executable file
89
pytorch/get_version.sh
Executable file
@ -0,0 +1,89 @@
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#!/bin/bash
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function usage() {
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echo "Usage: ${0} <project> <version>"
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exit 1
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}
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# ----- Settings
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Root=/opt/jupyter_gpu/pytorch/cvtt
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LocalSoftwareDir=${Root}/software/cvtt2
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ProdDir=${Root}/prod
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||||
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||||
ReleaseHosts=("cloud21.cvtt.vpn")
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ReleasePorts=("22")
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ReleaseUsers=("cvttdist")
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ReleaseDir=("/home/cvttdist/software/cvtt2")
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# ----- Settings
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Project=${1}
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Version=${2}
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if [ "${Project}" == "" ]
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then
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usage
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fi
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||||
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if [ "${Version}" == "" ]
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then
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Version=latest
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fi
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||||
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||||
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function rsync_load_version() {
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for idx in "${!ReleaseHosts[@]}"
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do
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host=${ReleaseHosts[${idx}]}
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port=${ReleasePorts[${idx}]}
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user=${ReleaseUsers[${idx}]}
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rel_dir=${ReleaseDir[${idx}]}
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if [ "${Version}" == "latest" ]; then
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echo "Checking for latest version of ${Project} on ${user}@${host}:${rel_dir}"
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Version=$(ssh -q -p ${port} ${user}@${host} "ls -tr ${rel_dir}/${Project} | tail -1" )
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echo "Latest version is ${Version}"
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fi
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echo "Checking ${user}@${host} for ${rel_dir}/${Project}/${Version} ..."
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if ssh -q -p ${port} ${user}@${host} "test -d ${rel_dir}/${Project}/${Version}"
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then
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echo "Directory found..."
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rsync_cmd="rsync -ahvv -e \"ssh -p ${port}\""
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rsync_cmd="${rsync_cmd} ${user}@${host}:${rel_dir}/${Project}/${Version}"
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rsync_cmd="${rsync_cmd} ${LocalSoftwareDir}/${Project}/"
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echo ${rsync_cmd}
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eval ${rsync_cmd}
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status=$?
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if [ ${status} -eq 0 ]
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then
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echo "Loading successful..."
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break
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fi
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else
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echo "Not Found ${rel_dir}/${Project}/${Version} on ${user}@${host}"
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fi
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done
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if [[ ! -d ${LocalSoftwareDir}/${Project} ]] ; then
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echo ERROR loading software
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exit 1
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fi
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}
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mkdir -p ${LocalSoftwareDir}
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mkdir -p ${ProdDir}
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# exists and not empty
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rsync_load_version
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RelLocalSoftwareDir=../software/cvtt2
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Location="${RelLocalSoftwareDir}/${Project}/${Version}/${Project}"
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Cmd="cd ${ProdDir}"
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Cmd="${Cmd} && rm -rf ${Project}"
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Cmd="${Cmd} && ln -snf ${Location} ${Project}"
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echo ${Cmd} && eval ${Cmd}
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echo "Done: $0 $*"
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@ -1,8 +1,16 @@
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||||
{
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||||
"cells": [
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||||
{
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||||
"cell_type": "markdown",
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||||
"id": "53fb9dcb-843b-4633-b031-22236bfeb815",
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||||
"metadata": {},
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||||
"source": [
|
||||
"### Check CUDA device"
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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": 1,
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||||
"execution_count": 8,
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||||
"id": "6b269e64-be58-43b5-ad60-0fbd1d37861a",
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||||
"metadata": {},
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||||
"outputs": [
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||||
@ -27,56 +35,34 @@
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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": 2,
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"id": "97906ea2-b284-4966-9c11-b8629f053815",
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||||
"cell_type": "markdown",
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||||
"id": "e6b4bc89-4cbd-4d4a-99a8-2d5bfb5d095d",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"import plotly"
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||||
"### Environment"
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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": 3,
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||||
"execution_count": 10,
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||||
"id": "7313a620-a0eb-4207-a12a-90aeee3cd980",
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||||
"metadata": {},
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||||
"outputs": [
|
||||
{
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||||
"data": {
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||||
"text/plain": [
|
||||
"'3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]'"
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||||
]
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||||
},
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||||
"execution_count": 3,
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||||
"metadata": {},
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||||
"output_type": "execute_result"
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||||
}
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||||
],
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"source": [
|
||||
"import sys\n",
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"sys.version"
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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": 4,
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||||
"id": "95d9a2e6-3464-4dbe-9a97-0c2d5eb34193",
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"metadata": {},
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": [
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||||
"environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
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||||
" 'HOSTNAME': '1fc69c311e22',\n",
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||||
"('3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]',\n",
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||||
" environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
|
||||
" 'HOSTNAME': '6c24ffc52409',\n",
|
||||
" 'JUPYTER_ENABLE_LAB': 'yes',\n",
|
||||
" 'PYTHONPATH': '/cvtt/prod',\n",
|
||||
" 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
|
||||
" 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',\n",
|
||||
" 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib:/usr/local/nvidia/lib64',\n",
|
||||
" 'PYTORCH_VERSION': '2.2.1',\n",
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||||
" 'HOME': '/root',\n",
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||||
" 'LC_CTYPE': 'C.UTF-8',\n",
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||||
" 'JPY_SESSION_NAME': '/workspace/oleg/Testing GPU.ipynb',\n",
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||||
" 'JPY_SESSION_NAME': '/workspace/Testing GPU.ipynb',\n",
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||||
" 'JPY_PARENT_PID': '1',\n",
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||||
" 'PYDEVD_USE_FRAME_EVAL': 'NO',\n",
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" 'TERM': 'xterm-color',\n",
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@ -86,31 +72,58 @@
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" 'PAGER': 'cat',\n",
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||||
" 'GIT_PAGER': 'cat',\n",
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||||
" 'MPLBACKEND': 'module://matplotlib_inline.backend_inline',\n",
|
||||
" 'CUDA_MODULE_LOADING': 'LAZY'}"
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||||
" 'CUDA_MODULE_LOADING': 'LAZY'})"
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||||
]
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||||
},
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||||
"execution_count": 4,
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||||
"execution_count": 10,
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||||
"metadata": {},
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||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
|
||||
"import os\n",
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||||
"import pandas as pd\n",
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||||
"import matplotlib\n",
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||||
"os.environ"
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||||
"import sys\n",
|
||||
"sys.version,os.environ"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "markdown",
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||||
"id": "943ac637-42c7-4a69-a6c4-94c382e22653",
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||||
"metadata": {},
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||||
"source": [
|
||||
"### Test packages avai"
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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": 1,
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||||
"execution_count": 11,
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||||
"id": "95d9a2e6-3464-4dbe-9a97-0c2d5eb34193",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"import os\n",
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||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
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||||
"\n",
|
||||
"import matplotlib\n",
|
||||
"import plotly\n",
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||||
"\n",
|
||||
"import cvttpy.tools.timeutils as tu\n",
|
||||
"\n",
|
||||
"import autogluon\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "eb38de31-fc19-4515-b08d-9cd7607ea958",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "75642bda0eed47598e340b8c1766949a",
|
||||
"model_id": "2b2a98cdd61d477b811279a6753630a2",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
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||||
},
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||||
@ -127,42 +140,37 @@
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||||
"text": [
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||||
"Done\n"
|
||||
]
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||||
},
|
||||
{
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||||
"data": {
|
||||
"text/plain": [
|
||||
"1717721060059418080"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import ipywidgets\n",
|
||||
"from ipywidgets import interact, IntSlider\n",
|
||||
"import cvttpy.tools.timeutils as tu\n",
|
||||
"\n",
|
||||
"def f(x):\n",
|
||||
" return x\n",
|
||||
"\n",
|
||||
"interact(f, x=IntSlider(min=0, max=10, step=1, value=5))\n",
|
||||
"print(\"Done\")\n"
|
||||
"print(\"Done\")\n",
|
||||
"tu.current_nanoseconds()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"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==2.14.1\n",
|
||||
"jupyter_server_terminals==0.5.3\n",
|
||||
"jupyterlab==4.2.1\n",
|
||||
"jupyterlab_pygments==0.3.0\n",
|
||||
"jupyterlab_server==2.27.2\n",
|
||||
"jupyterlab_widgets==3.0.11\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip freeze | grep jupyter"
|
||||
]
|
||||
|
||||
391
pytorch/notebooks/autogluon_chronos.ipynb
Normal file
391
pytorch/notebooks/autogluon_chronos.ipynb
Normal file
File diff suppressed because one or more lines are too long
@ -2,7 +2,7 @@
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||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
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||||
"id": "000d047d-3dfd-48cb-a3c3-bd1ad7c78b71",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -55,6 +55,14 @@
|
||||
"for gpu in gpus:\n",
|
||||
" print(gpu)"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "080fdf50-a5d5-4650-8f29-34c773443d59",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
443
tensorflow/notebooks/leo/LSTM_All_Crypto_01.ipynb
Normal file
443
tensorflow/notebooks/leo/LSTM_All_Crypto_01.ipynb
Normal file
@ -0,0 +1,443 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "1023f2c1-e45f-4e1c-9a1b-66f59f128196",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Panda Version: 2.2.2\n",
|
||||
"Today date is: 2024-06-06\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import sqlite3\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"# It is apparently officially accepted to explicitly\n",
|
||||
"# list all the functions you need from numpy:\n",
|
||||
"from numpy import array, zeros, exp, random, dot\n",
|
||||
"from numpy import shape, reshape, meshgrid, linspace\n",
|
||||
"from numpy import hstack, vstack\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"print('Panda Version:', pd.__version__)\n",
|
||||
"\n",
|
||||
"# Set ipython's max row display\n",
|
||||
"pd.set_option('display.max_row', 100, 'display.max_columns', 25)\n",
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt # for plotting\n",
|
||||
"import matplotlib\n",
|
||||
"matplotlib.rcParams['figure.dpi'] = 100 # highres display\n",
|
||||
"\n",
|
||||
"import tensorflow as tf\n",
|
||||
"from tensorflow import Variable\n",
|
||||
"\n",
|
||||
"from tensorflow.keras import Sequential\n",
|
||||
"from tensorflow.keras import Model\n",
|
||||
"from tensorflow.keras.layers import Dense\n",
|
||||
"from tensorflow.keras.layers import Dropout\n",
|
||||
"from tensorflow.keras.layers import TimeDistributed, RepeatVector\n",
|
||||
"\n",
|
||||
"from keras.optimizers import SGD\n",
|
||||
"\n",
|
||||
"from keras.models import load_model\n",
|
||||
"from keras.callbacks import EarlyStopping\n",
|
||||
"from keras.callbacks import ModelCheckpoint\n",
|
||||
"\n",
|
||||
"from keras.layers import LSTM, Dense, Concatenate\n",
|
||||
"\n",
|
||||
"from keras.optimizers import SGD\n",
|
||||
"\n",
|
||||
"import collections\n",
|
||||
"from collections import Counter\n",
|
||||
"\n",
|
||||
"# Import date class from datetime module\n",
|
||||
"import time\n",
|
||||
"# import datetime as dt\n",
|
||||
"from datetime import date, datetime\n",
|
||||
"print(\"Today date is: \", date.today())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "c09a37a6-f0d9-48e3-a1d1-65ddaf2c489c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/workspace/leo\n",
|
||||
"total 6528\n",
|
||||
"drwxrwxr-x 1 oleg oleg 250 Jun 6 10:00 .\n",
|
||||
"drwxrwxr-x 1 oleg oleg 18 Jun 3 23:40 ..\n",
|
||||
"-rw------- 1 oleg oleg 1449984 Jun 4 00:49 20240601.mktdata.ohlcv.db\n",
|
||||
"-rw------- 1 oleg oleg 1445888 Jun 3 23:44 20240602.mktdata.ohlcv.db\n",
|
||||
"-rw------- 1 oleg oleg 1437696 Jun 4 16:45 20240603.mktdata.ohlcv.db\n",
|
||||
"-rw------- 1 oleg oleg 1269760 Jun 5 10:00 20240604.mktdata.ohlcv.db\n",
|
||||
"-rw------- 1 oleg oleg 1081344 Jun 6 10:00 20240605.mktdata.ohlcv.db\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pwd\n",
|
||||
"\n",
|
||||
"!ls -la /workspace/data/crypto_md/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8d72d849-e8df-4564-a006-03ab646b9330",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "58d263ae-d028-4de0-9f10-0f14c4de28f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mktdata_db_file = \"/workspace/data/crypto_md/20240601.mktdata.ohlcv.db\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "70033f48-f283-43f3-8e86-3bd533a10fed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"db_conn = sqlite3.connect(mktdata_db_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "2cb82c91-a487-46b1-bcd0-012f7675010f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" type name tbl_name rootpage \\\n",
|
||||
"0 table bnbfut_ohlcv_1min bnbfut_ohlcv_1min 2 \n",
|
||||
"1 table bnbspot_ohlcv_1min bnbspot_ohlcv_1min 66 \n",
|
||||
"2 table coinbase_ohlcv_1min coinbase_ohlcv_1min 224 \n",
|
||||
"\n",
|
||||
" sql \n",
|
||||
"0 CREATE TABLE bnbfut_ohlcv_1min (tstamp INTEGER... \n",
|
||||
"1 CREATE TABLE bnbspot_ohlcv_1min (tstamp INTEGE... \n",
|
||||
"2 CREATE TABLE coinbase_ohlcv_1min (tstamp INTEG... \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tables_df = pd.read_sql_query(\"select * from sqlite_master where type = 'table'\", db_conn)\n",
|
||||
"print (tables_df.head())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"id": "cf159bde-1ccc-40e0-9eb9-a010c1372e07",
|
||||
"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>tstamp</th>\n",
|
||||
" <th>exchange_id</th>\n",
|
||||
" <th>instrument_id</th>\n",
|
||||
" <th>open</th>\n",
|
||||
" <th>high</th>\n",
|
||||
" <th>low</th>\n",
|
||||
" <th>close</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>vwap</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>5754</th>\n",
|
||||
" <td>1717286100000000000</td>\n",
|
||||
" <td>COINBASE</td>\n",
|
||||
" <td>PAIR-XRP-USD</td>\n",
|
||||
" <td>0.5183</td>\n",
|
||||
" <td>0.5183</td>\n",
|
||||
" <td>0.5180</td>\n",
|
||||
" <td>0.5180</td>\n",
|
||||
" <td>7401.243996</td>\n",
|
||||
" <td>0.518096</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5755</th>\n",
|
||||
" <td>1717286160000000000</td>\n",
|
||||
" <td>COINBASE</td>\n",
|
||||
" <td>PAIR-XRP-USD</td>\n",
|
||||
" <td>0.5180</td>\n",
|
||||
" <td>0.5182</td>\n",
|
||||
" <td>0.5180</td>\n",
|
||||
" <td>0.5180</td>\n",
|
||||
" <td>3491.632415</td>\n",
|
||||
" <td>0.518020</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5756</th>\n",
|
||||
" <td>1717286220000000000</td>\n",
|
||||
" <td>COINBASE</td>\n",
|
||||
" <td>PAIR-XRP-USD</td>\n",
|
||||
" <td>0.5181</td>\n",
|
||||
" <td>0.5181</td>\n",
|
||||
" <td>0.5180</td>\n",
|
||||
" <td>0.5181</td>\n",
|
||||
" <td>6963.191620</td>\n",
|
||||
" <td>0.518095</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5757</th>\n",
|
||||
" <td>1717286280000000000</td>\n",
|
||||
" <td>COINBASE</td>\n",
|
||||
" <td>PAIR-XRP-USD</td>\n",
|
||||
" <td>0.5181</td>\n",
|
||||
" <td>0.5183</td>\n",
|
||||
" <td>0.5181</td>\n",
|
||||
" <td>0.5182</td>\n",
|
||||
" <td>35743.462893</td>\n",
|
||||
" <td>0.518140</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5758</th>\n",
|
||||
" <td>1717286340000000000</td>\n",
|
||||
" <td>COINBASE</td>\n",
|
||||
" <td>PAIR-XRP-USD</td>\n",
|
||||
" <td>0.5182</td>\n",
|
||||
" <td>0.5184</td>\n",
|
||||
" <td>0.5181</td>\n",
|
||||
" <td>0.5184</td>\n",
|
||||
" <td>45521.897366</td>\n",
|
||||
" <td>0.518245</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" tstamp exchange_id instrument_id open high low \\\n",
|
||||
"5754 1717286100000000000 COINBASE PAIR-XRP-USD 0.5183 0.5183 0.5180 \n",
|
||||
"5755 1717286160000000000 COINBASE PAIR-XRP-USD 0.5180 0.5182 0.5180 \n",
|
||||
"5756 1717286220000000000 COINBASE PAIR-XRP-USD 0.5181 0.5181 0.5180 \n",
|
||||
"5757 1717286280000000000 COINBASE PAIR-XRP-USD 0.5181 0.5183 0.5181 \n",
|
||||
"5758 1717286340000000000 COINBASE PAIR-XRP-USD 0.5182 0.5184 0.5181 \n",
|
||||
"\n",
|
||||
" close volume vwap \n",
|
||||
"5754 0.5180 7401.243996 0.518096 \n",
|
||||
"5755 0.5180 3491.632415 0.518020 \n",
|
||||
"5756 0.5181 6963.191620 0.518095 \n",
|
||||
"5757 0.5182 35743.462893 0.518140 \n",
|
||||
"5758 0.5184 45521.897366 0.518245 "
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_sql_query(\"select * from coinbase_ohlcv_1min\", db_conn)\n",
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "11d73b0a-fe9b-4d73-87c3-4250ef98b16a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 0601 - 0605"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cd83027c-eaa1-44d9-98ad-8506099c323d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6ccc60bc-6b74-408c-946c-bdbfee0cffb5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0f358ee5-9ec0-4582-be09-4e4ad84faca7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fab70130-9661-4efd-ad45-7647d81cc6ce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"id": "77504183-34d8-42c6-93a3-d86a34388f9e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" tstamp exchange_id instrument_id open \\\n",
|
||||
"timestamp \n",
|
||||
"2024-06-01 00:00:00 1717200000000000000 COINBASE PAIR-BTC-USD 67473.07 \n",
|
||||
"2024-06-01 00:01:00 1717200060000000000 COINBASE PAIR-BTC-USD 67506.57 \n",
|
||||
"2024-06-01 00:02:00 1717200120000000000 COINBASE PAIR-BTC-USD 67494.73 \n",
|
||||
"2024-06-01 00:03:00 1717200180000000000 COINBASE PAIR-BTC-USD 67512.78 \n",
|
||||
"2024-06-01 00:04:00 1717200240000000000 COINBASE PAIR-BTC-USD 67538.81 \n",
|
||||
"\n",
|
||||
" high low close volume vwap \n",
|
||||
"timestamp \n",
|
||||
"2024-06-01 00:00:00 67514.99 67468.13 67506.03 2.045049 67496.869352 \n",
|
||||
"2024-06-01 00:01:00 67515.00 67480.66 67494.74 0.646759 67500.893305 \n",
|
||||
"2024-06-01 00:02:00 67515.00 67455.91 67514.99 9.732906 67477.840770 \n",
|
||||
"2024-06-01 00:03:00 67553.85 67496.58 67539.76 4.374730 67522.912057 \n",
|
||||
"2024-06-01 00:04:00 67579.13 67530.05 67579.13 2.215383 67557.086208 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[\"timestamp\"] = pd.to_datetime(df['tstamp'], unit='ns')\n",
|
||||
"df.set_index(\"timestamp\", inplace=True)\n",
|
||||
"print (df.head())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "316c0cfb-c73d-4dad-9d49-eb80daa229ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "92700c76-8eac-4ebb-86d3-27066486c437",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3604f3f2-3e56-477d-a7ac-d6490839642d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_union = pd.read_sql_query(\"select * from coinbase_ohlcv_1min\", db_conn)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a7c8b332-cd4a-455f-b7cf-381aec15c456",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0a27972a-f457-4ca5-8530-d6c87c7d9d91",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b5356525-e614-4858-af4e-648c03b7d21e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "695de24a-41f4-4c82-a0dd-dbbd7bfba2bd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3c6d80b3-4a46-432b-9b88-6a264f9b7a7e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": "3.11.0rc1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user