def gather_tb_directories(
source_dir: str,
run_name: Optional[str],
env_name: Optional[str],
skip_failed_runs: bool,
) -> dict:
"""Gather Tensorboard directories from a `parallel_ex` run.
The directories are copied to a unique directory in `/tmp/analysis_tb/` under
subdirectories matching the Tensorboard events' Ray Tune trial names.
Undocumented arguments are the same as in `analyze_imitation()`.
Args:
source_dir: A local_dir for Ray. For example, `~/ray_results/`.
Returns:
A dict with two keys. "gather_dir" (str) is a path to a /tmp/
directory containing all the TensorBoard runs filtered from `source_dir`.
"n_tb_dirs" (int) is the number of TensorBoard directories that were
filtered.
"""
sacred_dicts = _get_sacred_dicts(source_dir, run_name, env_name,
skip_failed_runs)
os.makedirs("/tmp/analysis_tb", exist_ok=True)
tmp_dir = tempfile.mkdtemp(dir="/tmp/analysis_tb/")
tb_dirs_count = 0
for sd in sacred_dicts:
# Expecting a path like "~/ray_results/{run_name}/sacred/1".
# Want to search for all Tensorboard dirs inside
# "~/ray_results/{run_name}".
sacred_dir = sd.sacred_dir.rstrip("/")
run_dir = osp.dirname(osp.dirname(sacred_dir))
run_name = osp.basename(run_dir)
# "tb" is TensorBoard directory built by our codebase. "sb_tb" is Stable
# Baselines TensorBoard directory. There should be at most one of each
# directory.
for basename in ["tb", "sb_tb"]:
tb_src_dirs = tuple(
sacred_util.filter_subdirs(
run_dir, lambda path: osp.basename(path) == basename))
if tb_src_dirs:
assert len(
tb_src_dirs) == 1, "expect at most one TB dir of each type"
tb_src_dir = tb_src_dirs[0]
symlinks_dir = osp.join(tmp_dir, basename)
os.makedirs(symlinks_dir, exist_ok=True)
tb_symlink = osp.join(symlinks_dir, run_name)
os.symlink(tb_src_dir, tb_symlink)
tb_dirs_count += 1
tf.logging.info(
f"Symlinked {tb_dirs_count} TensorBoard dirs to {tmp_dir}.")
tf.logging.info(
f"Start Tensorboard with `tensorboard --logdir {tmp_dir}`.")
return {"n_tb_dirs": tb_dirs_count, "gather_dir": tmp_dir}
def _get_sacred_dicts(source_dir: str, run_name: str, env_name: str,
skip_failed_runs: bool) -> List[sacred_util.SacredDicts]:
sacred_dirs = sacred_util.filter_subdirs(source_dir)
sacred_dicts = [
sacred_util.SacredDicts.load_from_dir(sacred_dir)
for sacred_dir in sacred_dirs
]
if run_name is not None:
sacred_dicts = filter(
lambda sd: get(sd.run, "experiment.name") == run_name,
sacred_dicts)
if env_name is not None:
sacred_dicts = filter(
lambda sd: get(sd.config, "env_name") == env_name, sacred_dicts)
if skip_failed_runs:
sacred_dicts = filter(lambda sd: get(sd.run, "status") != "FAILED",
sacred_dicts)
return list(sacred_dicts)