444 lines
14 KiB
Python
444 lines
14 KiB
Python
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for using TensorBoard in notebook contexts, like Colab.
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These APIs are experimental and subject to change.
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"""
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import datetime
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import errno
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import html
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import json
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import os
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import random
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import shlex
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import textwrap
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import time
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from tensorboard import manager
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# Return values for `_get_context` (see that function's docs for
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# details).
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_CONTEXT_COLAB = "_CONTEXT_COLAB"
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_CONTEXT_IPYTHON = "_CONTEXT_IPYTHON"
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_CONTEXT_NONE = "_CONTEXT_NONE"
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def _get_context():
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"""Determine the most specific context that we're in.
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Returns:
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_CONTEXT_COLAB: If in Colab with an IPython notebook context.
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_CONTEXT_IPYTHON: If not in Colab, but we are in an IPython notebook
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context (e.g., from running `jupyter notebook` at the command
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line).
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_CONTEXT_NONE: Otherwise (e.g., by running a Python script at the
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command-line or using the `ipython` interactive shell).
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"""
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# In Colab, the `google.colab` module is available, but the shell
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# returned by `IPython.get_ipython` does not have a `get_trait`
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# method.
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try:
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import google.colab # noqa: F401
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import IPython
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except ImportError:
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pass
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else:
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if IPython.get_ipython() is not None:
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# We'll assume that we're in a Colab notebook context.
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return _CONTEXT_COLAB
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# In an IPython command line shell or Jupyter notebook, we can
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# directly query whether we're in a notebook context.
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try:
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import IPython
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except ImportError:
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pass
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else:
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ipython = IPython.get_ipython()
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if ipython is not None and ipython.has_trait("kernel"):
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return _CONTEXT_IPYTHON
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# Otherwise, we're not in a known notebook context.
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return _CONTEXT_NONE
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def load_ipython_extension(ipython):
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"""Deprecated: use `%load_ext tensorboard` instead.
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Raises:
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RuntimeError: Always.
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"""
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raise RuntimeError(
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"Use '%load_ext tensorboard' instead of '%load_ext tensorboard.notebook'."
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)
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def _load_ipython_extension(ipython):
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"""Load the TensorBoard notebook extension.
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Intended to be called from `%load_ext tensorboard`. Do not invoke this
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directly.
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Args:
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ipython: An `IPython.InteractiveShell` instance.
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"""
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_register_magics(ipython)
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def _register_magics(ipython):
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"""Register IPython line/cell magics.
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Args:
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ipython: An `InteractiveShell` instance.
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"""
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ipython.register_magic_function(
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_start_magic,
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magic_kind="line",
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magic_name="tensorboard",
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)
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def _start_magic(line):
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"""Implementation of the `%tensorboard` line magic."""
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return start(line)
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def start(args_string):
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"""Launch and display a TensorBoard instance as if at the command line.
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Args:
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args_string: Command-line arguments to TensorBoard, to be
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interpreted by `shlex.split`: e.g., "--logdir ./logs --port 0".
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Shell metacharacters are not supported: e.g., "--logdir 2>&1" will
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point the logdir at the literal directory named "2>&1".
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"""
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context = _get_context()
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try:
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import IPython
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import IPython.display
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except ImportError:
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IPython = None
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if context == _CONTEXT_NONE:
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handle = None
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print("Launching TensorBoard...")
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else:
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handle = IPython.display.display(
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IPython.display.Pretty("Launching TensorBoard..."),
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display_id=True,
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)
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def print_or_update(message):
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if handle is None:
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print(message)
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else:
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handle.update(IPython.display.Pretty(message))
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parsed_args = shlex.split(args_string, comments=True, posix=True)
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start_result = manager.start(parsed_args)
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if isinstance(start_result, manager.StartLaunched):
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_display(
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port=start_result.info.port,
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print_message=False,
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display_handle=handle,
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)
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elif isinstance(start_result, manager.StartReused):
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template = (
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"Reusing TensorBoard on port {port} (pid {pid}), started {delta} ago. "
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"(Use '!kill {pid}' to kill it.)"
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)
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message = template.format(
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port=start_result.info.port,
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pid=start_result.info.pid,
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delta=_time_delta_from_info(start_result.info),
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)
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print_or_update(message)
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_display(
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port=start_result.info.port,
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print_message=False,
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display_handle=None,
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)
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elif isinstance(start_result, manager.StartFailed):
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def format_stream(name, value):
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if value == "":
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return ""
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elif value is None:
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return "\n<could not read %s>" % name
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else:
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return "\nContents of %s:\n%s" % (name, value.strip())
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message = (
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"ERROR: Failed to launch TensorBoard (exited with %d).%s%s"
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% (
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start_result.exit_code,
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format_stream("stderr", start_result.stderr),
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format_stream("stdout", start_result.stdout),
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)
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)
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print_or_update(message)
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elif isinstance(start_result, manager.StartExecFailed):
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the_tensorboard_binary = (
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"%r (set by the `TENSORBOARD_BINARY` environment variable)"
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% (start_result.explicit_binary,)
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if start_result.explicit_binary is not None
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else "`tensorboard`"
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)
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if start_result.os_error.errno == errno.ENOENT:
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message = (
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"ERROR: Could not find %s. Please ensure that your PATH contains "
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"an executable `tensorboard` program, or explicitly specify the path "
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"to a TensorBoard binary by setting the `TENSORBOARD_BINARY` "
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"environment variable." % (the_tensorboard_binary,)
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)
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else:
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message = "ERROR: Failed to start %s: %s" % (
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the_tensorboard_binary,
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start_result.os_error,
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)
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print_or_update(textwrap.fill(message))
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elif isinstance(start_result, manager.StartTimedOut):
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message = (
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"ERROR: Timed out waiting for TensorBoard to start. "
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"It may still be running as pid %d." % start_result.pid
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)
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print_or_update(message)
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else:
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raise TypeError(
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"Unexpected result from `manager.start`: %r.\n"
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"This is a TensorBoard bug; please report it." % start_result
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)
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def _time_delta_from_info(info):
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"""Format the elapsed time for the given TensorBoardInfo.
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Args:
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info: A TensorBoardInfo value.
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Returns:
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A human-readable string describing the time since the server
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described by `info` started: e.g., "2 days, 0:48:58".
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"""
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delta_seconds = int(time.time()) - info.start_time
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return str(datetime.timedelta(seconds=delta_seconds))
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def display(port=None, height=None):
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"""Display a TensorBoard instance already running on this machine.
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Args:
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port: The port on which the TensorBoard server is listening, as an
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`int`, or `None` to automatically select the most recently
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launched TensorBoard.
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height: The height of the frame into which to render the TensorBoard
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UI, as an `int` number of pixels, or `None` to use a default value
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(currently 800).
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"""
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_display(port=port, height=height, print_message=True, display_handle=None)
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def _display(port=None, height=None, print_message=False, display_handle=None):
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"""Internal version of `display`.
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Args:
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port: As with `display`.
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height: As with `display`.
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print_message: True to print which TensorBoard instance was selected
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for display (if applicable), or False otherwise.
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display_handle: If not None, an IPython display handle into which to
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render TensorBoard.
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"""
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if height is None:
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height = 800
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if port is None:
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infos = manager.get_all()
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if not infos:
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raise ValueError(
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"Can't display TensorBoard: no known instances running."
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)
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else:
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info = max(manager.get_all(), key=lambda x: x.start_time)
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port = info.port
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else:
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infos = [i for i in manager.get_all() if i.port == port]
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info = max(infos, key=lambda x: x.start_time) if infos else None
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if print_message:
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if info is not None:
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message = (
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"Selecting TensorBoard with {data_source} "
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"(started {delta} ago; port {port}, pid {pid})."
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).format(
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data_source=manager.data_source_from_info(info),
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delta=_time_delta_from_info(info),
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port=info.port,
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pid=info.pid,
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)
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print(message)
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else:
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# The user explicitly provided a port, and we don't have any
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# additional information. There's nothing useful to say.
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pass
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fn = {
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_CONTEXT_COLAB: _display_colab,
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_CONTEXT_IPYTHON: _display_ipython,
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_CONTEXT_NONE: _display_cli,
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}[_get_context()]
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return fn(port=port, height=height, display_handle=display_handle)
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def _display_colab(port, height, display_handle):
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"""Display a TensorBoard instance in a Colab output frame.
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The Colab VM is not directly exposed to the network, so the Colab
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runtime provides a service worker tunnel to proxy requests from the
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end user's browser through to servers running on the Colab VM: the
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output frame may issue requests to https://localhost:<port> (HTTPS
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only), which will be forwarded to the specified port on the VM.
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It does not suffice to create an `iframe` and let the service worker
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redirect its traffic (`<iframe src="https://localhost:6006">`),
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because for security reasons service workers cannot intercept iframe
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traffic. Instead, we manually fetch the TensorBoard index page with an
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XHR in the output frame, and inject the raw HTML into `document.body`.
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By default, the TensorBoard web app requests resources against
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relative paths, like `./data/logdir`. Within the output frame, these
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requests must instead hit `https://localhost:<port>/data/logdir`. To
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redirect them, we change the document base URI, which transparently
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affects all requests (XHRs and resources alike).
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"""
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import IPython.display
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shell = """
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(async () => {
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const url = new URL(await google.colab.kernel.proxyPort(%PORT%, {'cache': true}));
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url.searchParams.set('tensorboardColab', 'true');
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const iframe = document.createElement('iframe');
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iframe.src = url;
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iframe.setAttribute('width', '100%');
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iframe.setAttribute('height', '%HEIGHT%');
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iframe.setAttribute('frameborder', 0);
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document.body.appendChild(iframe);
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})();
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"""
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replacements = [
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("%PORT%", "%d" % port),
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("%HEIGHT%", "%d" % height),
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]
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for (k, v) in replacements:
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shell = shell.replace(k, v)
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script = IPython.display.Javascript(shell)
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if display_handle:
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display_handle.update(script)
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else:
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IPython.display.display(script)
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def _display_ipython(port, height, display_handle):
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import IPython.display
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frame_id = "tensorboard-frame-{:08x}".format(random.getrandbits(64))
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shell = """
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<iframe id="%HTML_ID%" width="100%" height="%HEIGHT%" frameborder="0">
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</iframe>
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<script>
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(function() {
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const frame = document.getElementById(%JSON_ID%);
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const url = new URL(%URL%, window.location);
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const port = %PORT%;
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if (port) {
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url.port = port;
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}
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frame.src = url;
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})();
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</script>
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"""
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proxy_url = os.environ.get("TENSORBOARD_PROXY_URL")
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if proxy_url is not None:
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# Allow %PORT% in $TENSORBOARD_PROXY_URL
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proxy_url = proxy_url.replace("%PORT%", "%d" % port)
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|
|
replacements = [
|
||
|
|
("%HTML_ID%", html.escape(frame_id, quote=True)),
|
||
|
|
("%JSON_ID%", json.dumps(frame_id)),
|
||
|
|
("%HEIGHT%", "%d" % height),
|
||
|
|
("%PORT%", "0"),
|
||
|
|
("%URL%", json.dumps(proxy_url)),
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
replacements = [
|
||
|
|
("%HTML_ID%", html.escape(frame_id, quote=True)),
|
||
|
|
("%JSON_ID%", json.dumps(frame_id)),
|
||
|
|
("%HEIGHT%", "%d" % height),
|
||
|
|
("%PORT%", "%d" % port),
|
||
|
|
("%URL%", json.dumps("/")),
|
||
|
|
]
|
||
|
|
|
||
|
|
for (k, v) in replacements:
|
||
|
|
shell = shell.replace(k, v)
|
||
|
|
iframe = IPython.display.HTML(shell)
|
||
|
|
if display_handle:
|
||
|
|
display_handle.update(iframe)
|
||
|
|
else:
|
||
|
|
IPython.display.display(iframe)
|
||
|
|
|
||
|
|
|
||
|
|
def _display_cli(port, height, display_handle):
|
||
|
|
del height # unused
|
||
|
|
del display_handle # unused
|
||
|
|
message = "Please visit http://localhost:%d in a web browser." % port
|
||
|
|
print(message)
|
||
|
|
|
||
|
|
|
||
|
|
def list():
|
||
|
|
"""Print a listing of known running TensorBoard instances.
|
||
|
|
|
||
|
|
TensorBoard instances that were killed uncleanly (e.g., with SIGKILL
|
||
|
|
or SIGQUIT) may appear in this list even if they are no longer
|
||
|
|
running. Conversely, this list may be missing some entries if your
|
||
|
|
operating system's temporary directory has been cleared since a
|
||
|
|
still-running TensorBoard instance started.
|
||
|
|
"""
|
||
|
|
infos = manager.get_all()
|
||
|
|
if not infos:
|
||
|
|
print("No known TensorBoard instances running.")
|
||
|
|
return
|
||
|
|
|
||
|
|
print("Known TensorBoard instances:")
|
||
|
|
for info in infos:
|
||
|
|
template = (
|
||
|
|
" - port {port}: {data_source} (started {delta} ago; pid {pid})"
|
||
|
|
)
|
||
|
|
print(
|
||
|
|
template.format(
|
||
|
|
port=info.port,
|
||
|
|
data_source=manager.data_source_from_info(info),
|
||
|
|
delta=_time_delta_from_info(info),
|
||
|
|
pid=info.pid,
|
||
|
|
)
|
||
|
|
)
|