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# Configuration file for lab.
#------------------------------------------------------------------------------
# Application(SingletonConfigurable) configuration
#------------------------------------------------------------------------------
## This is an application.
## Set the log level by value or name.
# Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
# Default: 30
c.Application.log_level = 'INFO'
#------------------------------------------------------------------------------
# JupyterApp(Application) configuration
#------------------------------------------------------------------------------
## Base class for Jupyter applications
## Answer yes to any prompts.
# Default: False
# c.JupyterApp.answer_yes = False
## Full path of a config file.
# Default: ''
# c.JupyterApp.config_file = ''
## Specify a config file to load.
# Default: ''
# c.JupyterApp.config_file_name = ''
## Generate default config file.
# Default: False
# c.JupyterApp.generate_config = False
## The date format used by logging formatters for %(asctime)s
# See also: Application.log_datefmt
# c.JupyterApp.log_datefmt = '%Y-%m-%d %H:%M:%S'
## The Logging format template
# See also: Application.log_format
# c.JupyterApp.log_format = '[%(name)s]%(highlevel)s %(message)s'
## Set the log level by value or name.
# See also: Application.log_level
# c.JupyterApp.log_level = 30
## Instead of starting the Application, dump configuration to stdout
# See also: Application.show_config
# c.JupyterApp.show_config = False
## Instead of starting the Application, dump configuration to stdout (as JSON)
# See also: Application.show_config_json
# c.JupyterApp.show_config_json = False
#------------------------------------------------------------------------------
# ExtensionApp(JupyterApp) configuration
#------------------------------------------------------------------------------
## Base class for configurable Jupyter Server Extension Applications.
#
# ExtensionApp subclasses can be initialized two ways:
# 1. Extension is listed as a jpserver_extension, and ServerApp calls
# its load_jupyter_server_extension classmethod. This is the
# classic way of loading a server extension.
# 2. Extension is launched directly by calling its `launch_instance`
# class method. This method can be set as a entry_point in
# the extensions setup.py
## Answer yes to any prompts.
# See also: JupyterApp.answer_yes
# c.ExtensionApp.answer_yes = False
## Full path of a config file.
# See also: JupyterApp.config_file
# c.ExtensionApp.config_file = ''
## Specify a config file to load.
# See also: JupyterApp.config_file_name
# c.ExtensionApp.config_file_name = ''
# Default: ''
# c.ExtensionApp.default_url = ''
## Generate default config file.
# See also: JupyterApp.generate_config
# c.ExtensionApp.generate_config = False
## Handlers appended to the server.
# Default: []
# c.ExtensionApp.handlers = []
## The date format used by logging formatters for %(asctime)s
# See also: Application.log_datefmt
# c.ExtensionApp.log_datefmt = '%Y-%m-%d %H:%M:%S'
## The Logging format template
# See also: Application.log_format
# c.ExtensionApp.log_format = '[%(name)s]%(highlevel)s %(message)s'
## Set the log level by value or name.
# See also: Application.log_level
# c.ExtensionApp.log_level = 30
## Whether to open in a browser after starting.
# The specific browser used is platform dependent and
# determined by the python standard library `webbrowser`
# module, unless it is overridden using the --browser
# (ServerApp.browser) configuration option.
# Default: False
# c.ExtensionApp.open_browser = False
## Settings that will passed to the server.
# Default: {}
# c.ExtensionApp.settings = {}
## Instead of starting the Application, dump configuration to stdout
# See also: Application.show_config
# c.ExtensionApp.show_config = False
## Instead of starting the Application, dump configuration to stdout (as JSON)
# See also: Application.show_config_json
# c.ExtensionApp.show_config_json = False
## paths to search for serving static files.
#
# This allows adding javascript/css to be available from the notebook server machine,
# or overriding individual files in the IPython
# Default: []
# c.ExtensionApp.static_paths = []
## Url where the static assets for the extension are served.
# Default: ''
# c.ExtensionApp.static_url_prefix = ''
## Paths to search for serving jinja templates.
#
# Can be used to override templates from notebook.templates.
# Default: []
# c.ExtensionApp.template_paths = []
#------------------------------------------------------------------------------
# LabServerApp(ExtensionApp) configuration
#------------------------------------------------------------------------------
## A Lab Server Application that runs out-of-the-box
## "A list of comma-separated URIs to get the allowed extensions list
#
# .. versionchanged:: 2.0.0
# `LabServerApp.whitetlist_uris` renamed to `allowed_extensions_uris`
# Default: ''
# c.LabServerApp.allowed_extensions_uris = ''
## Answer yes to any prompts.
# See also: JupyterApp.answer_yes
# c.LabServerApp.answer_yes = False
## The application settings directory.
# Default: ''
# c.LabServerApp.app_settings_dir = ''
## The url path for the application.
# Default: '/lab'
# c.LabServerApp.app_url = '/lab'
## Deprecated, use `LabServerApp.blocked_extensions_uris`
# Default: ''
# c.LabServerApp.blacklist_uris = ''
## A list of comma-separated URIs to get the blocked extensions list
#
# .. versionchanged:: 2.0.0
# `LabServerApp.blacklist_uris` renamed to `blocked_extensions_uris`
# Default: ''
# c.LabServerApp.blocked_extensions_uris = ''
## Whether to cache files on the server. This should be `True` except in dev
# mode.
# Default: True
# c.LabServerApp.cache_files = True
## Full path of a config file.
# See also: JupyterApp.config_file
# c.LabServerApp.config_file = ''
## Specify a config file to load.
# See also: JupyterApp.config_file_name
# c.LabServerApp.config_file_name = ''
## Extra paths to look for federated JupyterLab extensions
# Default: []
# c.LabServerApp.extra_labextensions_path = []
## Generate default config file.
# See also: JupyterApp.generate_config
# c.LabServerApp.generate_config = False
## Handlers appended to the server.
# See also: ExtensionApp.handlers
# c.LabServerApp.handlers = []
## Options to pass to the jinja2 environment for this
# Default: {}
# c.LabServerApp.jinja2_options = {}
## The standard paths to look in for federated JupyterLab extensions
# Default: []
# c.LabServerApp.labextensions_path = []
## The url for federated JupyterLab extensions
# Default: ''
# c.LabServerApp.labextensions_url = ''
## The interval delay in seconds to refresh the lists
# Default: 3600
# c.LabServerApp.listings_refresh_seconds = 3600
## The optional kwargs to use for the listings HTTP requests as
# described on https://2.python-requests.org/en/v2.7.0/api/#requests.request
# Default: {}
# c.LabServerApp.listings_request_options = {}
## The listings url.
# Default: ''
# c.LabServerApp.listings_url = ''
## The date format used by logging formatters for %(asctime)s
# See also: Application.log_datefmt
# c.LabServerApp.log_datefmt = '%Y-%m-%d %H:%M:%S'
## The Logging format template
# See also: Application.log_format
# c.LabServerApp.log_format = '[%(name)s]%(highlevel)s %(message)s'
## Set the log level by value or name.
# See also: Application.log_level
# c.LabServerApp.log_level = 30
## Whether to open in a browser after starting.
# See also: ExtensionApp.open_browser
# c.LabServerApp.open_browser = False
## The optional location of the settings schemas directory. If given, a handler
# will be added for settings.
# Default: ''
# c.LabServerApp.schemas_dir = ''
## Settings that will passed to the server.
# See also: ExtensionApp.settings
# c.LabServerApp.settings = {}
## The url path of the settings handler.
# Default: ''
# c.LabServerApp.settings_url = ''
## Instead of starting the Application, dump configuration to stdout
# See also: Application.show_config
# c.LabServerApp.show_config = False
## Instead of starting the Application, dump configuration to stdout (as JSON)
# See also: Application.show_config_json
# c.LabServerApp.show_config_json = False
## The optional location of local static files. If given, a static file handler
# will be added.
# Default: ''
# c.LabServerApp.static_dir = ''
## paths to search for serving static files.
# See also: ExtensionApp.static_paths
# c.LabServerApp.static_paths = []
## Url where the static assets for the extension are served.
# See also: ExtensionApp.static_url_prefix
# c.LabServerApp.static_url_prefix = ''
## Paths to search for serving jinja templates.
# See also: ExtensionApp.template_paths
# c.LabServerApp.template_paths = []
## The application templates directory.
# Default: ''
# c.LabServerApp.templates_dir = ''
## The optional location of the themes directory. If given, a handler will be
# added for themes.
# Default: ''
# c.LabServerApp.themes_dir = ''
## The theme url.
# Default: ''
# c.LabServerApp.themes_url = ''
## The url path of the translations handler.
# Default: ''
# c.LabServerApp.translations_api_url = ''
## The url path of the tree handler.
# Default: ''
# c.LabServerApp.tree_url = ''
## The optional location of the user settings directory.
# Default: ''
# c.LabServerApp.user_settings_dir = ''
## Deprecated, use `LabServerApp.allowed_extensions_uris`
# Default: ''
# c.LabServerApp.whitelist_uris = ''
## The url path of the workspaces API.
# Default: ''
# c.LabServerApp.workspaces_api_url = ''
## The optional location of the saved workspaces directory. If given, a handler
# will be added for workspaces.
# Default: ''
# c.LabServerApp.workspaces_dir = ''
#------------------------------------------------------------------------------
# LabApp(LabServerApp) configuration
#------------------------------------------------------------------------------
##
# See also: LabServerApp.allowed_extensions_uris
# c.LabApp.allowed_extensions_uris = ''
## Answer yes to any prompts.
# See also: JupyterApp.answer_yes
# c.LabApp.answer_yes = False
## The app directory to launch JupyterLab from.
# Default: None
# c.LabApp.app_dir = None
## The application settings directory.
# Default: ''
# c.LabApp.app_settings_dir = ''
## The url path for the application.
# Default: '/lab'
# c.LabApp.app_url = '/lab'
## Deprecated, use `LabServerApp.blocked_extensions_uris`
# See also: LabServerApp.blacklist_uris
# c.LabApp.blacklist_uris = ''
##
# See also: LabServerApp.blocked_extensions_uris
# c.LabApp.blocked_extensions_uris = ''
## Whether to cache files on the server. This should be `True` except in dev
# mode.
# Default: True
# c.LabApp.cache_files = True
## Whether to enable collaborative mode (experimental).
# Default: False
# c.LabApp.collaborative = False
## Full path of a config file.
# See also: JupyterApp.config_file
# c.LabApp.config_file = ''
## Specify a config file to load.
# See also: JupyterApp.config_file_name
# c.LabApp.config_file_name = ''
## Whether to start the app in core mode. In this mode, JupyterLab
# will run using the JavaScript assets that are within the installed
# JupyterLab Python package. In core mode, third party extensions are disabled.
# The `--dev-mode` flag is an alias to this to be used when the Python package
# itself is installed in development mode (`pip install -e .`).
# Default: False
# c.LabApp.core_mode = False
## The default URL to redirect to from `/`
# Default: '/lab'
c.LabApp.default_url = '/lab/tree/README.ipynb'
## Whether to start the app in dev mode. Uses the unpublished local
# JavaScript packages in the `dev_mode` folder. In this case JupyterLab will
# show a red stripe at the top of the page. It can only be used if JupyterLab
# is installed as `pip install -e .`.
# Default: False
# c.LabApp.dev_mode = False
## Whether to expose the global app instance to browser via window.jupyterlab
# Default: False
# c.LabApp.expose_app_in_browser = False
## Whether to load prebuilt extensions in dev mode. This may be
# useful to run and test prebuilt extensions in development installs of
# JupyterLab. APIs in a JupyterLab development install may be
# incompatible with published packages, so prebuilt extensions compiled
# against published packages may not work correctly.
# Default: False
# c.LabApp.extensions_in_dev_mode = False
## Extra paths to look for federated JupyterLab extensions
# Default: []
# c.LabApp.extra_labextensions_path = []
## Generate default config file.
# See also: JupyterApp.generate_config
# c.LabApp.generate_config = False
## Handlers appended to the server.
# See also: ExtensionApp.handlers
# c.LabApp.handlers = []
## Options to pass to the jinja2 environment for this
# Default: {}
# c.LabApp.jinja2_options = {}
## The standard paths to look in for federated JupyterLab extensions
# Default: []
# c.LabApp.labextensions_path = []
## The url for federated JupyterLab extensions
# Default: ''
# c.LabApp.labextensions_url = ''
## The interval delay in seconds to refresh the lists
# See also: LabServerApp.listings_refresh_seconds
# c.LabApp.listings_refresh_seconds = 3600
## The optional kwargs to use for the listings HTTP requests as
# described on https://2.python-requests.org/en/v2.7.0/api/#requests.request
# See also: LabServerApp.listings_request_options
# c.LabApp.listings_request_options = {}
## The listings url.
# Default: ''
# c.LabApp.listings_url = ''
## The date format used by logging formatters for %(asctime)s
# See also: Application.log_datefmt
# c.LabApp.log_datefmt = '%Y-%m-%d %H:%M:%S'
## The Logging format template
# See also: Application.log_format
# c.LabApp.log_format = '[%(name)s]%(highlevel)s %(message)s'
## Set the log level by value or name.
# See also: Application.log_level
# c.LabApp.log_level = 30
## Whether to open in a browser after starting.
# See also: ExtensionApp.open_browser
# c.LabApp.open_browser = False
## The override url for static lab assets, typically a CDN.
# Default: ''
# c.LabApp.override_static_url = ''
## The override url for static lab theme assets, typically a CDN.
# Default: ''
# c.LabApp.override_theme_url = ''
## The optional location of the settings schemas directory. If given, a handler
# will be added for settings.
# Default: ''
# c.LabApp.schemas_dir = ''
## Settings that will passed to the server.
# See also: ExtensionApp.settings
# c.LabApp.settings = {}
## The url path of the settings handler.
# Default: ''
# c.LabApp.settings_url = ''
## Instead of starting the Application, dump configuration to stdout
# See also: Application.show_config
# c.LabApp.show_config = False
## Instead of starting the Application, dump configuration to stdout (as JSON)
# See also: Application.show_config_json
# c.LabApp.show_config_json = False
## Splice source packages into app directory.
# Default: False
# c.LabApp.splice_source = False
## The optional location of local static files. If given, a static file handler
# will be added.
# Default: ''
# c.LabApp.static_dir = ''
## paths to search for serving static files.
# See also: ExtensionApp.static_paths
# c.LabApp.static_paths = []
## Url where the static assets for the extension are served.
# See also: ExtensionApp.static_url_prefix
# c.LabApp.static_url_prefix = ''
## Paths to search for serving jinja templates.
# See also: ExtensionApp.template_paths
# c.LabApp.template_paths = []
## The application templates directory.
# Default: ''
# c.LabApp.templates_dir = ''
## The optional location of the themes directory. If given, a handler will be
# added for themes.
# Default: ''
# c.LabApp.themes_dir = ''
## The theme url.
# Default: ''
# c.LabApp.themes_url = ''
## The url path of the translations handler.
# Default: ''
# c.LabApp.translations_api_url = ''
## The url path of the tree handler.
# Default: ''
# c.LabApp.tree_url = ''
## The directory for user settings.
# Default: '/root/.jupyter/lab/user-settings'
# c.LabApp.user_settings_dir = '/root/.jupyter/lab/user-settings'
## Whether to serve the app in watch mode
# Default: False
# c.LabApp.watch = False
## Deprecated, use `LabServerApp.allowed_extensions_uris`
# See also: LabServerApp.whitelist_uris
# c.LabApp.whitelist_uris = ''
## The url path of the workspaces API.
# Default: ''
# c.LabApp.workspaces_api_url = ''
## The directory for workspaces
# Default: '/root/.jupyter/lab/workspaces'
# c.LabApp.workspaces_dir = '/root/.jupyter/lab/workspaces'
#------------------------------------------------------------------------------
# ServerApp(JupyterApp) configuration
#------------------------------------------------------------------------------
## Set the Access-Control-Allow-Credentials: true header
# Default: False
# c.ServerApp.allow_credentials = False
## Set the Access-Control-Allow-Origin header
#
# Use '*' to allow any origin to access your server.
#
# Takes precedence over allow_origin_pat.
# Default: ''
# c.ServerApp.allow_origin = ''
## Use a regular expression for the Access-Control-Allow-Origin header
#
# Requests from an origin matching the expression will get replies with:
#
# Access-Control-Allow-Origin: origin
#
# where `origin` is the origin of the request.
#
# Ignored if allow_origin is set.
# Default: ''
# c.ServerApp.allow_origin_pat = ''
## Allow password to be changed at login for the Jupyter server.
#
# While logging in with a token, the Jupyter server UI will give the opportunity to
# the user to enter a new password at the same time that will replace
# the token login mechanism.
#
# This can be set to false to prevent changing password from
# the UI/API.
# Default: True
c.ServerApp.allow_password_change = False
## Allow requests where the Host header doesn't point to a local server
#
# By default, requests get a 403 forbidden response if the 'Host' header
# shows that the browser thinks it's on a non-local domain.
# Setting this option to True disables this check.
#
# This protects against 'DNS rebinding' attacks, where a remote web server
# serves you a page and then changes its DNS to send later requests to a
# local IP, bypassing same-origin checks.
#
# Local IP addresses (such as 127.0.0.1 and ::1) are allowed as local,
# along with hostnames configured in local_hostnames.
# Default: False
# c.ServerApp.allow_remote_access = False
## Whether to allow the user to run the server as root.
# Default: False
c.ServerApp.allow_root = True
## Answer yes to any prompts.
# See also: JupyterApp.answer_yes
# c.ServerApp.answer_yes = False
## "
# Require authentication to access prometheus metrics.
# Default: True
# c.ServerApp.authenticate_prometheus = True
## Reload the webapp when changes are made to any Python src files.
# Default: False
# c.ServerApp.autoreload = False
## The base URL for the Jupyter server.
#
# Leading and trailing slashes can be omitted,
# and will automatically be added.
# Default: '/'
# c.ServerApp.base_url = '/'
## Specify what command to use to invoke a web
# browser when starting the server. If not specified, the
# default browser will be determined by the `webbrowser`
# standard library module, which allows setting of the
# BROWSER environment variable to override it.
# Default: ''
# c.ServerApp.browser = ''
## The full path to an SSL/TLS certificate file.
# Default: ''
# c.ServerApp.certfile = ''
## The full path to a certificate authority certificate for SSL/TLS client
# authentication.
# Default: ''
# c.ServerApp.client_ca = ''
## Full path of a config file.
# See also: JupyterApp.config_file
# c.ServerApp.config_file = ''
## Specify a config file to load.
# See also: JupyterApp.config_file_name
# c.ServerApp.config_file_name = ''
## The config manager class to use
# Default: 'jupyter_server.services.config.manager.ConfigManager'
# c.ServerApp.config_manager_class = 'jupyter_server.services.config.manager.ConfigManager'
## The content manager class to use.
# Default: 'jupyter_server.services.contents.largefilemanager.LargeFileManager'
# c.ServerApp.contents_manager_class = 'jupyter_server.services.contents.largefilemanager.LargeFileManager'
## Extra keyword arguments to pass to `set_secure_cookie`. See tornado's
# set_secure_cookie docs for details.
# Default: {}
# c.ServerApp.cookie_options = {}
## The random bytes used to secure cookies.
# By default this is a new random number every time you start the server.
# Set it to a value in a config file to enable logins to persist across server sessions.
#
# Note: Cookie secrets should be kept private, do not share config files with
# cookie_secret stored in plaintext (you can read the value from a file).
# Default: b''
# c.ServerApp.cookie_secret = b''
## The file where the cookie secret is stored.
# Default: ''
# c.ServerApp.cookie_secret_file = ''
## Override URL shown to users.
#
# Replace actual URL, including protocol, address, port and base URL,
# with the given value when displaying URL to the users. Do not change
# the actual connection URL. If authentication token is enabled, the
# token is added to the custom URL automatically.
#
# This option is intended to be used when the URL to display to the user
# cannot be determined reliably by the Jupyter server (proxified
# or containerized setups for example).
# Default: ''
# c.ServerApp.custom_display_url = ''
## The default URL to redirect to from `/`
# Default: '/'
c.ServerApp.default_url = '/lab/tree/README.ipynb'
## Disable cross-site-request-forgery protection
#
# Jupyter notebook 4.3.1 introduces protection from cross-site request forgeries,
# requiring API requests to either:
#
# - originate from pages served by this server (validated with XSRF cookie and token), or
# - authenticate with a token
#
# Some anonymous compute resources still desire the ability to run code,
# completely without authentication.
# These services can disable all authentication and security checks,
# with the full knowledge of what that implies.
# Default: False
# c.ServerApp.disable_check_xsrf = False
## handlers that should be loaded at higher priority than the default services
# Default: []
# c.ServerApp.extra_services = []
## Extra paths to search for serving static files.
#
# This allows adding javascript/css to be available from the Jupyter server machine,
# or overriding individual files in the IPython
# Default: []
# c.ServerApp.extra_static_paths = []
## Extra paths to search for serving jinja templates.
#
# Can be used to override templates from jupyter_server.templates.
# Default: []
# c.ServerApp.extra_template_paths = []
## Open the named file when the application is launched.
# Default: ''
# c.ServerApp.file_to_run = ''
## The URL prefix where files are opened directly.
# Default: 'notebooks'
# c.ServerApp.file_url_prefix = 'notebooks'
## Generate default config file.
# See also: JupyterApp.generate_config
# c.ServerApp.generate_config = False
## Extra keyword arguments to pass to `get_secure_cookie`. See tornado's
# get_secure_cookie docs for details.
# Default: {}
# c.ServerApp.get_secure_cookie_kwargs = {}
## (bytes/sec)
# Maximum rate at which stream output can be sent on iopub before they are
# limited.
# Default: 1000000
# c.ServerApp.iopub_data_rate_limit = 1000000
## (msgs/sec)
# Maximum rate at which messages can be sent on iopub before they are
# limited.
# Default: 1000
# c.ServerApp.iopub_msg_rate_limit = 1000
## The IP address the Jupyter server will listen on.
# Default: 'localhost'
c.ServerApp.ip = '0.0.0.0'
## Supply extra arguments that will be passed to Jinja environment.
# Default: {}
# c.ServerApp.jinja_environment_options = {}
## Extra variables to supply to jinja templates when rendering.
# Default: {}
# c.ServerApp.jinja_template_vars = {}
## Dict of Python modules to load as Jupyter server extensions.Entry values can
# be used to enable and disable the loading ofthe extensions. The extensions
# will be loaded in alphabetical order.
# Default: {}
# c.ServerApp.jpserver_extensions = {}
## The kernel manager class to use.
# Default: 'jupyter_server.services.kernels.kernelmanager.AsyncMappingKernelManager'
# c.ServerApp.kernel_manager_class = 'jupyter_server.services.kernels.kernelmanager.AsyncMappingKernelManager'
## The kernel spec manager class to use. Should be a subclass of
# `jupyter_client.kernelspec.KernelSpecManager`.
#
# The Api of KernelSpecManager is provisional and might change without warning
# between this version of Jupyter and the next stable one.
# Default: 'jupyter_client.kernelspec.KernelSpecManager'
# c.ServerApp.kernel_spec_manager_class = 'jupyter_client.kernelspec.KernelSpecManager'
## Preferred kernel message protocol over websocket to use (default: None). If an
# empty string is passed, select the legacy protocol. If None, the selected
# protocol will depend on what the front-end supports (usually the most recent
# protocol supported by the back-end and the front-end).
# Default: None
# c.ServerApp.kernel_ws_protocol = None
## The full path to a private key file for usage with SSL/TLS.
# Default: ''
# c.ServerApp.keyfile = ''
## Whether to limit the rate of IOPub messages (default: True). If True, use
# iopub_msg_rate_limit, iopub_data_rate_limit and/or rate_limit_window to tune
# the rate.
# Default: True
# c.ServerApp.limit_rate = True
## Hostnames to allow as local when allow_remote_access is False.
#
# Local IP addresses (such as 127.0.0.1 and ::1) are automatically accepted
# as local as well.
# Default: ['localhost']
# c.ServerApp.local_hostnames = ['localhost']
## The date format used by logging formatters for %(asctime)s
# See also: Application.log_datefmt
# c.ServerApp.log_datefmt = '%Y-%m-%d %H:%M:%S'
## The Logging format template
# See also: Application.log_format
# c.ServerApp.log_format = '[%(name)s]%(highlevel)s %(message)s'
## Set the log level by value or name.
# See also: Application.log_level
# c.ServerApp.log_level = 30
## The login handler class to use.
# Default: 'jupyter_server.auth.login.LoginHandler'
# c.ServerApp.login_handler_class = 'jupyter_server.auth.login.LoginHandler'
## The logout handler class to use.
# Default: 'jupyter_server.auth.logout.LogoutHandler'
# c.ServerApp.logout_handler_class = 'jupyter_server.auth.logout.LogoutHandler'
## Sets the maximum allowed size of the client request body, specified in the
# Content-Length request header field. If the size in a request exceeds the
# configured value, a malformed HTTP message is returned to the client.
#
# Note: max_body_size is applied even in streaming mode.
# Default: 536870912
# c.ServerApp.max_body_size = 536870912
## Gets or sets the maximum amount of memory, in bytes, that is allocated for use
# by the buffer manager.
# Default: 536870912
# c.ServerApp.max_buffer_size = 536870912
## Gets or sets a lower bound on the open file handles process resource limit.
# This may need to be increased if you run into an OSError: [Errno 24] Too many
# open files. This is not applicable when running on Windows.
# Default: 0
# c.ServerApp.min_open_files_limit = 0
## DEPRECATED, use root_dir.
# Default: ''
# c.ServerApp.notebook_dir = ''
## Whether to open in a browser after starting.
# The specific browser used is platform dependent and
# determined by the python standard library `webbrowser`
# module, unless it is overridden using the --browser
# (ServerApp.browser) configuration option.
# Default: False
c.ServerApp.open_browser = False
## Hashed password to use for web authentication.
#
# To generate, type in a python/IPython shell:
#
# from jupyter_server.auth import passwd; passwd()
#
# The string should be of the form type:salt:hashed-
# password.
# Default: ''
c.ServerApp.password = ''
## Forces users to use a password for the Jupyter server.
# This is useful in a multi user environment, for instance when
# everybody in the LAN can access each other's machine through ssh.
#
# In such a case, serving on localhost is not secure since
# any user can connect to the Jupyter server via ssh.
# Default: False
# c.ServerApp.password_required = False
## The port the server will listen on (env: JUPYTER_PORT).
# Default: 0
c.ServerApp.port = 8888
## The number of additional ports to try if the specified port is not available
# (env: JUPYTER_PORT_RETRIES).
# Default: 50
# c.ServerApp.port_retries = 50
## Preferred starting directory to use for notebooks and kernels.
# Default: ''
# c.ServerApp.preferred_dir = ''
## DISABLED: use %pylab or %matplotlib in the notebook to enable matplotlib.
# Default: 'disabled'
# c.ServerApp.pylab = 'disabled'
## If True, display controls to shut down the Jupyter server, such as menu items
# or buttons.
# Default: True
c.ServerApp.quit_button = True
## (sec) Time window used to
# check the message and data rate limits.
# Default: 3
# c.ServerApp.rate_limit_window = 3
## Reraise exceptions encountered loading server extensions?
# Default: False
# c.ServerApp.reraise_server_extension_failures = False
## The directory to use for notebooks and kernels.
# Default: ''
import os
#os.environ['FIDLE_MASTER_VERSION'] = '2.4.1'
#fidle_master_version = os.environ.get('FIDLE_MASTER_VERSION')
c.ServerApp.root_dir = f'/notebooks/last'
## The session manager class to use.
# Default: 'jupyter_server.services.sessions.sessionmanager.SessionManager'
# c.ServerApp.session_manager_class = 'jupyter_server.services.sessions.sessionmanager.SessionManager'
## Instead of starting the Application, dump configuration to stdout
# See also: Application.show_config
# c.ServerApp.show_config = False
## Instead of starting the Application, dump configuration to stdout (as JSON)
# See also: Application.show_config_json
# c.ServerApp.show_config_json = False
## Shut down the server after N seconds with no kernels or terminals running and
# no activity. This can be used together with culling idle kernels
# (MappingKernelManager.cull_idle_timeout) to shutdown the Jupyter server when
# it's not in use. This is not precisely timed: it may shut down up to a minute
# later. 0 (the default) disables this automatic shutdown.
# Default: 0
# c.ServerApp.shutdown_no_activity_timeout = 0
## The UNIX socket the Jupyter server will listen on.
# Default: ''
# c.ServerApp.sock = ''
## The permissions mode for UNIX socket creation (default: 0600).
# Default: '0600'
# c.ServerApp.sock_mode = '0600'
## Supply SSL options for the tornado HTTPServer.
# See the tornado docs for details.
# Default: {}
# c.ServerApp.ssl_options = {}
## Supply overrides for terminado. Currently only supports "shell_command".
# Default: {}
# c.ServerApp.terminado_settings = {}
## Set to False to disable terminals.
#
# This does *not* make the server more secure by itself.
# Anything the user can in a terminal, they can also do in a notebook.
#
# Terminals may also be automatically disabled if the terminado package
# is not available.
# Default: True
# c.ServerApp.terminals_enabled = True
## Token used for authenticating first-time connections to the server.
#
# The token can be read from the file referenced by JUPYTER_TOKEN_FILE or set directly
# with the JUPYTER_TOKEN environment variable.
#
# When no password is enabled,
# the default is to generate a new, random token.
#
# Setting to an empty string disables authentication altogether, which
# is NOT RECOMMENDED.
# Default: '<generated>'
# c.ServerApp.token = '<generated>'
## Supply overrides for the tornado.web.Application that the Jupyter server uses.
# Default: {}
# c.ServerApp.tornado_settings = {}
## Whether to trust or not X-Scheme/X-Forwarded-Proto and X-Real-Ip/X-Forwarded-
# For headerssent by the upstream reverse proxy. Necessary if the proxy handles
# SSL
# Default: False
# c.ServerApp.trust_xheaders = False
## Disable launching browser by redirect file
# For versions of notebook > 5.7.2, a security feature measure was added that
# prevented the authentication token used to launch the browser from being visible.
# This feature makes it difficult for other users on a multi-user system from
# running code in your Jupyter session as you.
# However, some environments (like Windows Subsystem for Linux (WSL) and Chromebooks),
# launching a browser using a redirect file can lead the browser failing to load.
# This is because of the difference in file structures/paths between the runtime and
# the browser.
#
# Disabling this setting to False will disable this behavior, allowing the browser
# to launch by using a URL and visible token (as before).
# Default: True
# c.ServerApp.use_redirect_file = True
## Specify where to open the server on startup. This is the
# `new` argument passed to the standard library method `webbrowser.open`.
# The behaviour is not guaranteed, but depends on browser support. Valid
# values are:
#
# - 2 opens a new tab,
# - 1 opens a new window,
# - 0 opens in an existing window.
#
# See the `webbrowser.open` documentation for details.
# Default: 2
# c.ServerApp.webbrowser_open_new = 2
## Set the tornado compression options for websocket connections.
#
# This value will be returned from
# :meth:`WebSocketHandler.get_compression_options`. None (default) will disable
# compression. A dict (even an empty one) will enable compression.
#
# See the tornado docs for WebSocketHandler.get_compression_options for details.
# Default: None
# c.ServerApp.websocket_compression_options = None
## The base URL for websockets,
# if it differs from the HTTP server (hint: it almost certainly doesn't).
#
# Should be in the form of an HTTP origin: ws[s]://hostname[:port]
# Default: ''
# c.ServerApp.websocket_url = ''
# ----------------------------------------------------
# ______ _ _ _ __ __
# | ____(_) | | | \ \ / /
# | |__ _ __| | | ___ \ \ / /__ _ ____ __
# | __| | |/ _` | |/ _ \ \ \/ / _ \ '_ \ \ / /
# | | | | (_| | | __/ \ / __/ | | \ V /
# |_| |_|\__,_|_|\___| \/ \___|_| |_|\_/
# Fidle pip virtual env
# ----------------------------------------------------
#
# To install your Fidle env, see https://fidle.cnrs.fr/installation
#
# Keras 3 / PyTorch version (Python 3.9.2)
--extra-index-url https://download.pytorch.org/whl/cpu
torch
torchvision
torch-geometric
torchtext
torchdata
lightning
tensorboard
keras
transformers
numpy
Scikit-image
Scikit-learn
Matplotlib
plotly
seaborn
einops
datasets
barviz
pyarrow
Pandas
Pandoc
pyyaml
Jupyterlab
fidle
# ----------------------------------------------------
# ______ _ _ _ __ __
# | ____(_) | | | \ \ / /
# | |__ _ __| | | ___ \ \ / /__ _ ____ __
# | __| | |/ _` | |/ _ \ \ \/ / _ \ '_ \ \ / /
# | | | | (_| | | __/ \ / __/ | | \ V /
# |_| |_|\__,_|_|\___| \/ \___|_| |_|\_/
# Fidle pip virtual env
# ----------------------------------------------------
#
# To install your Fidle env, see https://fidle.cnrs.fr/installation
#
# Keras 3 / PyTorch version (Python 3.9.2)
torch
torchvision
torch-geometric
torchtext
torchdata
lightning
tensorboard
keras
transformers
numpy
Scikit-image
Scikit-learn
Matplotlib
plotly
seaborn
einops
datasets
barviz
pyarrow
Pandas
Pandoc
pyyaml
Jupyterlab
fidle
name: deeplearning2
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _tflow_select=2.1.0=gpu
- absl-py=0.8.1=py37_0
- astor=0.8.0=py37_0
- attrs=19.3.0=py_0
- backcall=0.1.0=py37_0
- blas=1.0=mkl
- bleach=3.1.0=py_0
- blosc=1.16.3=hd408876_0
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.15.0=h7b6447c_1001
- ca-certificates=2019.11.27=0
- certifi=2019.11.28=py37_0
- cloudpickle=1.2.2=py_0
- cudatoolkit=10.0.130=0
- cudnn=7.6.4=cuda10.0_0
- cupti=10.0.130=0
- cycler=0.10.0=py37_0
- cytoolz=0.10.1=py37h7b6447c_0
- dask-core=2.9.0=py_0
- dbus=1.13.12=h746ee38_0
- decorator=4.4.1=py_0
- defusedxml=0.6.0=py_0
- entrypoints=0.3=py37_0
- expat=2.2.6=he6710b0_0
- fontconfig=2.13.0=h9420a91_0
- freetype=2.9.1=h8a8886c_1
- gast=0.2.2=py37_0
- glib=2.63.1=h5a9c865_0
- gmp=6.1.2=h6c8ec71_1
- google-pasta=0.1.8=py_0
- grpcio=1.16.1=py37hf8bcb03_1
- gst-plugins-base=1.14.0=hbbd80ab_1
- gstreamer=1.14.0=hb453b48_1
- h5py=2.9.0=py37h7918eee_0
- hdf5=1.10.4=hb1b8bf9_0
- icu=58.2=h9c2bf20_1
- imageio=2.6.1=py37_0
- importlib_metadata=1.3.0=py37_0
- intel-openmp=2019.4=243
- ipykernel=5.1.3=py37h39e3cac_0
- ipython=7.10.2=py37h39e3cac_0
- ipython_genutils=0.2.0=py37_0
- jedi=0.15.1=py37_0
- jinja2=2.10.3=py_0
- joblib=0.14.1=py_0
- jpeg=9b=h024ee3a_2
- json5=0.8.5=py_0
- jsonschema=3.2.0=py37_0
- jupyter_client=5.3.4=py37_0
- jupyter_core=4.6.1=py37_0
- jupyterlab=1.2.4=pyhf63ae98_0
- jupyterlab_server=1.0.6=py_0
- keras-applications=1.0.8=py_0
- keras-preprocessing=1.1.0=py_1
- kiwisolver=1.1.0=py37he6710b0_0
- libedit=3.1.20181209=hc058e9b_0
- libffi=3.2.1=hd88cf55_4
- libgcc-ng=9.1.0=hdf63c60_0
- libgfortran-ng=7.3.0=hdf63c60_0
- libpng=1.6.37=hbc83047_0
- libprotobuf=3.11.2=hd408876_0
- libsodium=1.0.16=h1bed415_0
- libstdcxx-ng=9.1.0=hdf63c60_0
- libtiff=4.1.0=h2733197_0
- libuuid=1.0.3=h1bed415_2
- libxcb=1.13=h1bed415_1
- libxml2=2.9.9=hea5a465_1
- lz4-c=1.8.1.2=h14c3975_0
- lzo=2.10=h49e0be7_2
- markdown=3.1.1=py37_0
- markupsafe=1.1.1=py37h7b6447c_0
- matplotlib=3.1.1=py37h5429711_0
- mistune=0.8.4=py37h7b6447c_0
- mkl=2019.4=243
- mkl-service=2.3.0=py37he904b0f_0
- mkl_fft=1.0.15=py37ha843d7b_0
- mkl_random=1.1.0=py37hd6b4f25_0
- mock=3.0.5=py37_0
- more-itertools=8.0.2=py_0
- nbconvert=5.6.1=py37_0
- nbformat=4.4.0=py37_0
- ncurses=6.1=he6710b0_1
- networkx=2.4=py_0
- notebook=6.0.2=py37_0
- numexpr=2.7.0=py37h9e4a6bb_0
- numpy=1.17.4=py37hc1035e2_0
- numpy-base=1.17.4=py37hde5b4d6_0
- olefile=0.46=py_0
- openssl=1.1.1d=h7b6447c_3
- opt_einsum=3.1.0=py_0
- pandas=0.25.3=py37he6710b0_0
- pandoc=2.2.3.2=0
- pandocfilters=1.4.2=py37_1
- parso=0.5.2=py_0
- patsy=0.5.1=py37_0
- pcre=8.43=he6710b0_0
- pexpect=4.7.0=py37_0
- pickleshare=0.7.5=py37_0
- pillow=6.2.1=py37h34e0f95_0
- pip=19.3.1=py37_0
- prometheus_client=0.7.1=py_0
- prompt_toolkit=3.0.2=py_0
- protobuf=3.11.2=py37he6710b0_0
- ptyprocess=0.6.0=py37_0
- pygments=2.5.2=py_0
- pyparsing=2.4.5=py_0
- pyqt=5.9.2=py37h05f1152_2
- pyrsistent=0.15.6=py37h7b6447c_0
- pytables=3.6.1=py37h71ec239_0
- python=3.7.5=h0371630_0
- python-dateutil=2.8.1=py_0
- pytz=2019.3=py_0
- pywavelets=1.1.1=py37h7b6447c_0
- pyzmq=18.1.0=py37he6710b0_0
- qt=5.9.7=h5867ecd_1
- readline=7.0=h7b6447c_5
- scikit-image=0.15.0=py37he6710b0_0
- scikit-learn=0.22=py37hd81dba3_0
- scipy=1.3.2=py37h7c811a0_0
- seaborn=0.9.0=pyh91ea838_1
- send2trash=1.5.0=py37_0
- setuptools=42.0.2=py37_0
- sip=4.19.8=py37hf484d3e_0
- six=1.13.0=py37_0
- snappy=1.1.7=hbae5bb6_3
- sqlite=3.30.1=h7b6447c_0
- statsmodels=0.10.1=py37hdd07704_0
- tensorboard=2.0.0=pyhb38c66f_1
- tensorflow=2.0.0=gpu_py37h768510d_0
- tensorflow-base=2.0.0=gpu_py37h0ec5d1f_0
- tensorflow-estimator=2.0.0=pyh2649769_0
- tensorflow-gpu=2.0.0=h0d30ee6_0
- termcolor=1.1.0=py37_1
- terminado=0.8.3=py37_0
- testpath=0.4.4=py_0
- tk=8.6.8=hbc83047_0
- toolz=0.10.0=py_0
- tornado=6.0.3=py37h7b6447c_0
- traitlets=4.3.3=py37_0
- wcwidth=0.1.7=py37_0
- webencodings=0.5.1=py37_1
- werkzeug=0.16.0=py_0
- wheel=0.33.6=py37_0
- wrapt=1.11.2=py37h7b6447c_0
- xz=5.2.4=h14c3975_4
- zeromq=4.3.1=he6710b0_3
- zipp=0.6.0=py_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.3.7=h0b5b093_0
prefix: /home/pjluc/anaconda3/envs/deeplearning2
VERSION='0.1a'
\ No newline at end of file
#--------------------------------------------------------------------
# ______ _ _ _ _____ _ _
# | ____(_) | | | / ____| | | | |
# | |__ _ __| | | ___ | | ___ _ __ | |_ ___ _ __ | |_ ___
# | __| | |/ _` | |/ _ \ | | / _ \| '_ \| __/ _ \ '_ \| __/ __|
# | | | | (_| | | __/ | |___| (_) | | | | || __/ | | | |_\__ \
# |_| |_|\__,_|_|\___| \_____\___/|_| |_|\__\___|_| |_|\__|___/
#
# Formation Introduction au Deep Learning - 2024
#--------------------------------------------------------------------
# Formation Introduction au Deep Learning https://fidle.cnrs.fr
# By MIAI/CNRS/UGA 2023/24
#
# This file describes the notebooks used by the Fidle training.
version: 3.0.15
content: notebooks
name: Notebooks Fidle
description: All notebooks used by the Fidle training
readme_md: README.md
readme_ipynb: README.ipynb
default_ci: fidle/ci/default.yml
toc:
LinearReg: Linear and logistic regression
Perceptron: Perceptron Model 1957
BHPD.Keras3: BHPD regression (DNN), using Keras3/PyTorch
BHPD.PyTorch: BHPD regression (DNN), using PyTorch
Wine.Keras3: Wine Quality prediction (DNN), using Keras3/PyTorch
Wine.Lightning: Wine Quality prediction (DNN), using PyTorch/Lightning
MNIST.Keras3: MNIST classification (DNN,CNN), using Keras3/PyTorch
MNIST.PyTorch: MNIST classification (DNN,CNN), using PyTorch
MNIST.Lightning: MNIST classification (DNN,CNN), using PyTorch/Lightning
GTSRB.Keras3: Images classification GTSRB with Convolutional Neural Networks (CNN), using Keras3/PyTorch
Embedding.Keras3: Sentiment analysis with word embedding, using Keras3/PyTorch
RNN.Keras3: Time series with Recurrent Neural Network (RNN), using Keras3/PyTorch
GNN.PyTorch: Graph Neural Networks
AE.Keras3: Unsupervised learning with an autoencoder neural network (AE), using Keras3
VAE.Keras3: Generative network with Variational Autoencoder (VAE), using Keras3
DCGAN.Lightning: Generative Adversarial Networks (GANs), using Lightning
DDPM.PyTorch: Diffusion Model (DDPM) using PyTorch
Optimization.PyTorch: Training optimization, using PyTorch
DRL.PyTorch: Deep Reinforcement Learning (DRL), using PyTorch
Misc: Miscellaneous things, but very important!
#!/bin/bash
# -----------------------------------------------
# _ _ _
# | |__ __ _| |_ ___| |__
# | '_ \ / _` | __/ __| '_ \
# | |_) | (_| | || (__| | | |
# |_.__/ \__,_|\__\___|_| |_|
# Fidle at IDRIS
# -----------------------------------------------
#
# SLURM batch script
# Bash script for SLURM batch submission of ci notebooks
# by Jean-Luc Parouty (CNRS/SIMaP)
#
# Soumission : sbatch /(...)/batch_slurm.sh
# Suivi : squeue -u $USER
# ==== Job parameters ==============================================
#SBATCH --job-name="Fidle ci" # nom du job
#SBATCH --ntasks=1 # nombre de tâche (un unique processus ici)
#SBATCH --gres=gpu:1 # nombre de GPU à réserver (un unique GPU ici)
#SBATCH --cpus-per-task=10 # nombre de coeurs à réserver (un quart du noeud)
#SBATCH --hint=nomultithread # on réserve des coeurs physiques et non logiques
#SBATCH --time=05:00:00 # temps exécution maximum demande (HH:MM:SS)
#SBATCH --output="FIDLE_CI_%j.out" # nom du fichier de sortie
#SBATCH --error="FIDLE_CI_%j.err" # nom du fichier des erreurs
#SBATCH --mail-user=Someone@somewhere.fr
#SBATCH --mail-type=END,FAIL
# ==== Parameters ==================================================
MODULE_ENV="pytorch-gpu/py3/2.1.1"
RUN_DIR="$WORK/fidle-project/fidle"
CAMPAIN_PROFILE="./fidle/ci/gpu-scale1.yml"
FILTER=( '.*' )
# ==================================================================
echo '------------------------------------------------------------'
echo "Start : $0"
echo '------------------------------------------------------------'
echo "Job id : $SLURM_JOB_ID"
echo "Job name : $SLURM_JOB_NAME"
echo "Job node list : $SLURM_JOB_NODELIST"
echo '------------------------------------------------------------'
echo "module loaded : $MODULE_ENV"
echo "run dir : $RUN_DIR"
echo "campain profile : $CAMPAIN_PROFILE"
echo "filter : ${FILTER[@]}"
echo '------------------------------------------------------------'
# ---- Module + env.
module purge
module load "$MODULE_ENV"
export PYTHONUSERBASE=$WORK/local/fidle-k3
export PATH=$PATH:$PYTHONUSERBASE/bin
# ---- Run it...
cd "$RUN_DIR"
fid run_ci --quiet --campain "$CAMPAIN_PROFILE" --filter ${FILTER[@]}
echo 'Done.'
campain:
version: '1.0'
description: Notebook test on CPU, default settings.
directory: ./campains/cpu-default
existing_notebook: 'skip' # remove|skip
report_template: 'fidle' # fidle|default
timeout: 6000
environment_vars:
FIDLE_SAVE_FIGS: true
#
# ------------ LinearReg
#
LINR1:
notebook: LinearReg/01-Linear-Regression.ipynb
GRAD1:
notebook: LinearReg/02-Gradient-descent.ipynb
POLR1:
notebook: LinearReg/03-Polynomial-Regression.ipynb
LOGR1:
notebook: LinearReg/04-Logistic-Regression.ipynb
#
# ------------ Perceptron
#
PER57:
notebook: Perceptron/01-Simple-Perceptron.ipynb
#
# ------------ BHPD.Keras3
#
K3BHPD1:
notebook: BHPD.Keras3/01-DNN-Regression.ipynb
overrides:
fit_verbosity: 2
K3BHPD2:
notebook: BHPD.Keras3/02-DNN-Regression-Premium.ipynb
overrides:
fit_verbosity: 2
#
# ------------ BHPD.PyTorch
#
PBHPD1:
notebook: BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
#
# ------------ Wine.Keras3
#
K3WINE1:
notebook: Wine.Keras3/01-DNN-Wine-Regression.ipynb
overrides:
fit_verbosity: 2
dataset_name: default
#
# ------------ Wine.Lightning
#
LWINE1:
notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
overrides:
fit_verbosity: 2
dataset_name: default
#
# ------------ MNIST.Keras3
#
K3MNIST1:
notebook: MNIST.Keras3/01-DNN-MNIST.ipynb
overrides:
fit_verbosity: 2
K3MNIST2:
notebook: MNIST.Keras3/02-CNN-MNIST.ipynb
overrides:
fit_verbosity: 2
#
# ------------ MNIST.PyTorch
#
#PMNIST1:
# notebook: MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
#
# ------------ MNIST.Lightning
#
#LMNIST2:
# notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
#
# ------------ GTSRB.Keras3
#
K3GTSRB1:
notebook: GTSRB.Keras3/01-Preparation-of-data.ipynb
overrides:
scale: default
output_dir: default
progress_verbosity: default
K3GTSRB2:
notebook: GTSRB.Keras3/02-First-convolutions.ipynb
after: K3GTSRB1
overrides:
enhanced_dir: default
dataset_name: default
batch_size: default
epochs: default
scale: default
fit_verbosity: 2
K3GTSRB3:
notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
after: K3GTSRB1
overrides:
enhanced_dir: default
model_name: default
dataset_name: default
batch_size: default
epochs: default
scale: default
fit_verbosity: 2
#
# ------------ Embedding.Keras3
#
K3IMDB1:
notebook: Embedding.Keras3/01-One-hot-encoding.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
batch_size: default
epochs: default
fit_verbosity: 2
K3IMDB2:
notebook: Embedding.Keras3/02-Keras-embedding.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
output_dir: default
fit_verbosity: 2
K3IMDB3:
notebook: Embedding.Keras3/03-Prediction.ipynb
after: K3IMDB2
overrides:
vocab_size: default
review_len: default
saved_models: default
dictionaries_dir: default
K3IMDB4:
notebook: Embedding.Keras3/04-Show-vectors.ipynb
after: K3IMDB2
overrides:
vocab_size: default
review_len: default
saved_models: default
dictionaries_dir: default
K3IMDB5:
notebook: Embedding.Keras3/05-LSTM-Keras.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
fit_verbosity: 2
scale: default
#
# ------------ RNN.Keras3
#
K3LADYB1:
notebook: RNN.Keras3/01-Ladybug.ipynb
overrides:
scale: default
train_prop: default
sequence_len: default
predict_len: default
batch_size: default
epochs: default
#
# ------------ AE.Keras3
#
K3AE1:
notebook: AE.Keras3/01-Prepare-MNIST-dataset.ipynb
overrides:
prepared_dataset: default
scale: 0.1
progress_verbosity: default
K3AE2:
notebook: AE.Keras3/02-AE-with-MNIST.ipynb
after: K3AE1
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
latent_dim: default
train_prop: default
batch_size: default
epochs: 4
fit_verbosity: default
K3AE3:
notebook: AE.Keras3/03-AE-with-MNIST-post.ipynb
after: K3AE2
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
train_prop: default
K3AE4:
notebook: AE.Keras3/04-ExtAE-with-MNIST.ipynb
after: K3AE1
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
train_prop: default
batch_size: default
epochs: 4
fit_verbosity: default
K3AE5:
notebook: AE.Keras3/05-ExtAE-with-MNIST.ipynb
after: K3AE1
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
train_prop: default
batch_size: default
epochs: 4
fit_verbosity: default
#
# ------------ VAE.Keras3
#
K3VAE1:
notebook: VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb
overrides:
latent_dim: default
loss_weights: default
scale: 0.1
seed: default
batch_size: default
epochs: 4
fit_verbosity: default
K3VAE2:
notebook: VAE.Keras3/02-VAE-with-MNIST.ipynb
overrides:
latent_dim: default
loss_weights: default
scale: 0.1
seed: default
batch_size: default
epochs: 4
fit_verbosity: default
K3VAE3:
notebook: VAE.Keras3/03-VAE-with-MNIST-post.ipynb
after: K3VAE2
overrides:
scale: 0.1
seed: default
models_dir: default
#
# ------------ DCGAN.Lightning
#
PLSHEEP3:
notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
overrides:
latent_dim: default
gan_name: GAN
generator_name: default
discriminator_name: default
epochs: 4
lr: default
b1: default
b2: default
batch_size: default
num_img: default
fit_verbosity: default
dataset_file: default
data_shape: default
scale: 0.001
num_workers: default
PLSHEEP3:
notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
overrides:
latent_dim: default
gan_name: WGANGP
generator_name: default
discriminator_name: default
epochs: 4
lr: default
b1: default
b2: default
batch_size: default
num_img: default
fit_verbosity: default
dataset_file: default
data_shape: default
scale: 0.001
num_workers: default
#
# ------------ Misc
#
NP1:
notebook: Misc/00-Numpy.ipynb
ACTF1:
notebook: Misc/01-Activation-Functions.ipynb
PANDAS1:
notebook: Misc/02-Using-pandas.ipynb
FID1:
notebook: Misc/07-Fid-Example.ipynb
overrides:
scale: .1
x: 345
batch_size: default
\ No newline at end of file
campain:
version: '1.0'
description: Automatically generated ci profile (06/01/25 16:42:30)
directory: ./campains/default
existing_notebook: 'remove # remove|skip'
report_template: 'fidle # fidle|default'
timeout: 6000
#
# ------------ LinearReg
#
LINR1:
notebook: LinearReg/01-Linear-Regression.ipynb
GRAD1:
notebook: LinearReg/02-Gradient-descent.ipynb
POLR1:
notebook: LinearReg/03-Polynomial-Regression.ipynb
LOGR1:
notebook: LinearReg/04-Logistic-Regression.ipynb
#
# ------------ Perceptron
#
PER57:
notebook: Perceptron/01-Simple-Perceptron.ipynb
#
# ------------ BHPD.Keras3
#
K3BHPD1:
notebook: BHPD.Keras3/01-DNN-Regression.ipynb
overrides:
fit_verbosity: default
K3BHPD2:
notebook: BHPD.Keras3/02-DNN-Regression-Premium.ipynb
overrides:
fit_verbosity: default
#
# ------------ BHPD.PyTorch
#
PBHPD1:
notebook: BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
#
# ------------ Wine.Keras3
#
K3WINE1:
notebook: Wine.Keras3/01-DNN-Wine-Regression.ipynb
overrides:
fit_verbosity: default
dataset_name: default
#
# ------------ Wine.Lightning
#
LWINE1:
notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
overrides:
fit_verbosity: default
dataset_name: default
#
# ------------ MNIST.Keras3
#
K3MNIST1:
notebook: MNIST.Keras3/01-DNN-MNIST.ipynb
overrides:
fit_verbosity: default
K3MNIST2:
notebook: MNIST.Keras3/02-CNN-MNIST.ipynb
overrides:
fit_verbosity: default
#
# ------------ MNIST.PyTorch
#
PMNIST1:
notebook: MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
#
# ------------ MNIST.Lightning
#
LMNIST1:
notebook: MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb
LMNIST2:
notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
#
# ------------ GTSRB.Keras3
#
K3GTSRB1:
notebook: GTSRB.Keras3/01-Preparation-of-data.ipynb
overrides:
scale: default
output_dir: default
progress_verbosity: default
K3GTSRB2:
notebook: GTSRB.Keras3/02-First-convolutions.ipynb
overrides:
enhanced_dir: default
dataset_name: default
batch_size: default
epochs: default
scale: default
fit_verbosity: default
K3GTSRB3:
notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
overrides:
enhanced_dir: default
model_name: default
dataset_name: default
batch_size: default
epochs: default
scale: default
fit_verbosity: default
#
# ------------ Embedding.Keras3
#
K3IMDB1:
notebook: Embedding.Keras3/01-One-hot-encoding.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
batch_size: default
epochs: default
fit_verbosity: default
K3IMDB2:
notebook: Embedding.Keras3/02-Keras-embedding.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
output_dir: default
fit_verbosity: default
K3IMDB3:
notebook: Embedding.Keras3/03-Prediction.ipynb
overrides:
vocab_size: default
review_len: default
saved_models: default
dictionaries_dir: default
K3IMDB4:
notebook: Embedding.Keras3/04-Show-vectors.ipynb
overrides:
vocab_size: default
review_len: default
saved_models: default
dictionaries_dir: default
K3IMDB5:
notebook: Embedding.Keras3/05-LSTM-Keras.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
fit_verbosity: default
scale: default
#
# ------------ RNN.Keras3
#
K3LADYB1:
notebook: RNN.Keras3/01-Ladybug.ipynb
overrides:
scale: default
train_prop: default
sequence_len: default
predict_len: default
batch_size: default
epochs: default
fit_verbosity: default
#
# ------------ GNN.PyTorch
#
{}
#
# ------------ AE.Keras3
#
K3AE1:
notebook: AE.Keras3/01-Prepare-MNIST-dataset.ipynb
overrides:
prepared_dataset: default
scale: default
progress_verbosity: default
K3AE2:
notebook: AE.Keras3/02-AE-with-MNIST.ipynb
overrides:
prepared_dataset: default
dataset_seed: default
scale: default
latent_dim: default
train_prop: default
batch_size: default
epochs: default
fit_verbosity: default
K3AE3:
notebook: AE.Keras3/03-AE-with-MNIST-post.ipynb
overrides:
prepared_dataset: default
dataset_seed: default
scale: default
train_prop: default
K3AE4:
notebook: AE.Keras3/04-ExtAE-with-MNIST.ipynb
overrides:
prepared_dataset: default
dataset_seed: default
scale: default
train_prop: default
batch_size: default
epochs: default
fit_verbosity: default
K3AE5:
notebook: AE.Keras3/05-ExtAE-with-MNIST.ipynb
overrides:
prepared_dataset: default
dataset_seed: default
scale: default
train_prop: default
batch_size: default
epochs: default
fit_verbosity: default
#
# ------------ VAE.Keras3
#
K3VAE1:
notebook: VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb
overrides:
latent_dim: default
loss_weights: default
scale: default
seed: default
batch_size: default
epochs: default
fit_verbosity: default
K3VAE2:
notebook: VAE.Keras3/02-VAE-with-MNIST.ipynb
overrides:
latent_dim: default
loss_weights: default
scale: default
seed: default
batch_size: default
epochs: default
fit_verbosity: default
K3VAE3:
notebook: VAE.Keras3/03-VAE-with-MNIST-post.ipynb
overrides:
scale: default
seed: default
models_dir: default
#
# ------------ DCGAN.Lightning
#
PLSHEEP3:
notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
overrides:
latent_dim: default
gan_name: default
generator_name: default
discriminator_name: default
epochs: default
lr: default
b1: default
b2: default
batch_size: default
num_img: default
fit_verbosity: default
dataset_file: default
data_shape: default
scale: default
num_workers: default
#
# ------------ DDPM.PyTorch
#
DDPM1:
notebook: DDPM.PyTorch/01-ddpm.ipynb
#
# ------------ Optimization.PyTorch
#
OPT1:
notebook: Optimization.PyTorch/01-Apprentissages-rapides-et-Optimisations.ipynb
#
# ------------ DRL.PyTorch
#
DRL1:
notebook: DRL.PyTorch/FIDLE_DQNfromScratch.ipynb
DRL2:
notebook: DRL.PyTorch/FIDLE_rl_baselines_zoo.ipynb
#
# ------------ Misc
#
NP1:
notebook: Misc/00-Numpy.ipynb
ACTF1:
notebook: Misc/01-Activation-Functions.ipynb
PANDAS1:
notebook: Misc/02-Using-pandas.ipynb
PYTORCH1:
notebook: Misc/03-Using-Pytorch.ipynb
TSB1:
notebook: Misc/04-Using-Tensorboard.ipynb
overrides: ??
K3LSTM1:
notebook: Misc/05-RNN.ipynb
PGRAD1:
notebook: Misc/06-Gradients.ipynb
FID1:
notebook: Misc/99-Fid-Example.ipynb
overrides:
scale: default
x: default
batch_size: default
campain:
version: '1.0'
description: Notebook test on GPU, with scale=1
directory: ./campains/gpu-scale1
existing_notebook: 'remove' # remove|skip
report_template: 'fidle' # fidle|default
timeout: 6000
environment_vars:
FIDLE_SAVE_FIGS: true
#
# ------------ LinearReg
#
LINR1:
notebook: LinearReg/01-Linear-Regression.ipynb
GRAD1:
notebook: LinearReg/02-Gradient-descent.ipynb
POLR1:
notebook: LinearReg/03-Polynomial-Regression.ipynb
LOGR1:
notebook: LinearReg/04-Logistic-Regression.ipynb
#
# ------------ Perceptron
#
PER57:
notebook: Perceptron/01-Simple-Perceptron.ipynb
#
# ------------ BHPD.Keras3
#
K3BHPD1:
notebook: BHPD.Keras3/01-DNN-Regression.ipynb
overrides:
fit_verbosity: 2
K3BHPD2:
notebook: BHPD.Keras3/02-DNN-Regression-Premium.ipynb
overrides:
fit_verbosity: 2
#
# ------------ BHPD.PyTorch
#
PBHPD1:
notebook: BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
#
# ------------ Wine.Keras3
#
K3WINE1:
notebook: Wine.Keras3/01-DNN-Wine-Regression.ipynb
overrides:
fit_verbosity: 2
dataset_name: default
#
# ------------ Wine.Lightning
#
LWINE1:
notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
overrides:
fit_verbosity: 2
dataset_name: default
#
# ------------ MNIST.Keras3
#
K3MNIST1:
notebook: MNIST.Keras3/01-DNN-MNIST.ipynb
overrides:
fit_verbosity: 2
K3MNIST2:
notebook: MNIST.Keras3/02-CNN-MNIST.ipynb
overrides:
fit_verbosity: 2
#
# ------------ MNIST.PyTorch
#
#PMNIST1:
# notebook: MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
#
# ------------ MNIST.Lightning
#
#LMNIST2:
# notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
#
# ------------ GTSRB.Keras3
#
K3GTSRB1:
notebook: GTSRB.Keras3/01-Preparation-of-data.ipynb
overrides:
scale: 1
output_dir: "{datasets_dir}/GTSRB/enhanced"
progress_verbosity: default
K3GTSRB2:
notebook: GTSRB.Keras3/02-First-convolutions.ipynb
after: K3GTSRB1
overrides:
enhanced_dir: "{datasets_dir}/GTSRB/enhanced"
dataset_name: set-24x24-L
batch_size: 64
epochs: 5
scale: 1
fit_verbosity: 2
K3GTSRB3_1:
notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
after: K3GTSRB1
overrides:
enhanced_dir: "{datasets_dir}/GTSRB/enhanced"
model_name: model_01
dataset_name: set-48x48-RGB
batch_size: 64
epochs: 5
scale: 1
fit_verbosity: 2
K3GTSRB3_2:
notebook: GTSRB.Keras3/03-Better-convolutions.ipynb
after: K3GTSRB1
overrides:
enhanced_dir: "{datasets_dir}/GTSRB/enhanced"
model_name: model_02
dataset_name: set-48x48-RGB
batch_size: 64
epochs: 5
scale: 1
fit_verbosity: 2
#
# ------------ Embedding.Keras3
#
K3IMDB1:
notebook: Embedding.Keras3/01-One-hot-encoding.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
batch_size: default
epochs: default
fit_verbosity: 2
K3IMDB2:
notebook: Embedding.Keras3/02-Keras-embedding.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
output_dir: default
fit_verbosity: 2
K3IMDB3:
notebook: Embedding.Keras3/03-Prediction.ipynb
after: K3IMDB2
overrides:
vocab_size: default
review_len: default
saved_models: default
dictionaries_dir: default
K3IMDB4:
notebook: Embedding.Keras3/04-Show-vectors.ipynb
after: K3IMDB2
overrides:
vocab_size: default
review_len: default
saved_models: default
dictionaries_dir: default
K3IMDB5:
notebook: Embedding.Keras3/05-LSTM-Keras.ipynb
overrides:
vocab_size: default
hide_most_frequently: default
review_len: default
dense_vector_size: default
batch_size: default
epochs: default
fit_verbosity: 2
scale: 1
#
# ------------ RNN.Keras3
#
K3LADYB1:
notebook: RNN.Keras3/01-Ladybug.ipynb
overrides:
scale: 1
train_prop: 0.8
sequence_len: 20
predict_len: 5
batch_size: 32
epochs: 10
fit_verbosity: 2
#
# ------------ AE.Keras3
#
K3AE1:
notebook: AE.Keras3/01-Prepare-MNIST-dataset.ipynb
overrides:
prepared_dataset: default
scale: 1
progress_verbosity: 2
K3AE2:
notebook: AE.Keras3/02-AE-with-MNIST.ipynb
after: K3AE1
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
latent_dim: 10
train_prop: default
batch_size: default
epochs: 20
fit_verbosity: 2
K3AE3:
notebook: AE.Keras3/03-AE-with-MNIST-post.ipynb
after: K3AE2
overrides:
prepared_dataset: default
dataset_seed: default
scale: default
train_prop: default
K3AE4:
notebook: AE.Keras3/04-ExtAE-with-MNIST.ipynb
after: K3AE1
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
train_prop: default
batch_size: default
epochs: 20
fit_verbosity: 2
K3AE5:
notebook: AE.Keras3/05-ExtAE-with-MNIST.ipynb
after: K3AE1
overrides:
prepared_dataset: default
dataset_seed: default
scale: 1
train_prop: default
batch_size: default
epochs: 30
fit_verbosity: 2
#
# ------------ VAE.Keras3
#
K3VAE1:
notebook: VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb
overrides:
latent_dim: default
loss_weights: default
scale: 1
seed: default
batch_size: default
epochs: 20
fit_verbosity: 2
K3VAE2:
notebook: VAE.Keras3/02-VAE-with-MNIST.ipynb
overrides:
latent_dim: default
loss_weights: default
scale: 1
seed: default
batch_size: default
epochs: 20
fit_verbosity: 2
K3VAE3:
notebook: VAE.Keras3/03-VAE-with-MNIST-post.ipynb
after: K3VAE2
overrides:
scale: 1
seed: default
models_dir: default
#
# ------------ DCGAN.Lightning
#
PLSHEEP3_1:
notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
overrides:
latent_dim: default
gan_name: GAN
generator_name: Generator_2
discriminator_name: Discriminator_3
epochs: 30
lr: default
b1: default
b2: default
batch_size: 64
num_img: default
fit_verbosity: default
dataset_file: default
data_shape: default
scale: 1
num_workers: 2
PLSHEEP3_2:
notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
overrides:
latent_dim: default
gan_name: WGANGP
generator_name: Generator_2
discriminator_name: Discriminator_3
epochs: 30
lr: default
b1: default
b2: default
batch_size: 64
num_img: default
fit_verbosity: default
dataset_file: default
data_shape: default
scale: 1
num_workers: 2
#
# ------------ Misc
#
NP1:
notebook: Misc/00-Numpy.ipynb
ACTF1:
notebook: Misc/01-Activation-Functions.ipynb
PANDAS1:
notebook: Misc/02-Using-pandas.ipynb
FID1:
notebook: Misc/99-Fid-Example.ipynb
overrides:
scale: 1
x: 345
batch_size: default
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background-color: #fcf2f2;
border-color: #dFb5b4;
border-left: 5px solid #dfb5b4;
padding: 0.5em;
font-weight: bold;
font-size: 1.1em;;
}
div.nota {
background-color: #DAFFDE;
border-left: 5px solid #92CC99;
padding: 0.5em;
}
div.todo:before { 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float:left;
margin-right:20px;
margin-top:-20px;
margin-bottom:20px;
}
div.todo{
font-weight: bold;
font-size: 1.1em;
margin-top:40px;
}
div.todo ul{
margin: 0.2em;
}
div.todo li{
margin-left:60px;
margin-top:0;
margin-bottom:0;
}
div .comment{
font-size:0.8em;
color:#696969;
}
</style>
<style>
.fid_line{
padding-top: 10px
}
.fid_id {
font-size:1.em;
color:black;
font-weight: bold;
padding:0px;
margin-left: 20px;
display: inline-block;
width: 60px;
}
.fid_desc {
font-size:1.em;
padding:0px;
margin-left: 85px;
display: inline-block;
width: 600px;
}
div.fid_section {
font-size:1.2em;
color:black;
margin-left: 0px;
margin-top: 12px;
margin-bottom:8px;
border-bottom: solid;
border-block-width: 1px;
border-block-color: #dadada;
width: 700px;
}
</style>
\ No newline at end of file
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