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.gitignore
View file @ d140bf44
......@@ -8,4 +8,6 @@
**/leaf/
**.egg-info
2021**
2022**
\ No newline at end of file
2022**
**/massif.out*
*swp
README.rst
View file @ d140bf44
==============
decentralizepy
==============
decentralizepy is a framework for running distributed applications (particularly ML) on top of arbitrary topologies (decentralized, federated, parameter server).
It was primarily conceived for assessing scientific ideas on several aspects of distributed learning (communication efficiency, privacy, data heterogeneity etc.).
-------------------------
Setting up decentralizepy
-------------------------
* Fork the repository.
* Clone and enter your local repository.
* Check if you have ``python>=3.8``. ::
python --version
* (Optional) Create and activate a virtual environment. ::
python3 -m venv [venv-name]
source [venv-name]/bin/activate
* Update pip. ::
pip3 install --upgrade pip
pip install --upgrade pip
* On Mac M1, installing ``pyzmq`` fails with `pip`. Use `conda <https://conda.io>`_.
* Install decentralizepy for development. (zsh) ::
pip3 install --editable .\[dev\]
* Install decentralizepy for development. (bash) ::
pip3 install --editable .[dev]
----------------
Running the code
----------------
* Choose and modify one of the config files in ``eval/{step,epoch}_configs``.
* Modify the dataset paths and ``addresses_filepath`` in the config file.
* In eval/run.sh, modify arguments as required.
* Execute eval/run.sh on all the machines simultaneously. There is a synchronization barrier mechanism at the start so that all processes start training together.
------------
Contributing
------------
* ``isort`` and ``black`` are installed along with the package for code linting.
* While in the root directory of the repository, before committing the changes, please run ::
black .
isort .
Node
----
* The Manager. Optimizations at process level.
......@@ -19,7 +71,7 @@ Graph
Mapping
-------
* Naming. The globally unique ids of the processes <-> machine_id, local_rank
* Naming. The globally unique ids of the ``processes <-> machine_id, local_rank``
Sharing
-------
......@@ -28,3 +80,7 @@ Sharing
Communication
-------------
* IPC/Network level. Compression. Privacy. Reliability
Model
-----
* Learning Model
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eval/6_star.edges 0 → 100644
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6
0 1
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1 0
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eval/config_celeba.ini → eval/epoch_configs/config_celeba.ini
View file @ d140bf44
......@@ -2,7 +2,6 @@
dataset_package = decentralizepy.datasets.Celeba
dataset_class = Celeba
model_class = CNN
n_procs = 96
images_dir = /home/risharma/leaf/data/celeba/data/raw/img_align_celeba
train_dir = /home/risharma/leaf/data/celeba/per_user_data/train
test_dir = /home/risharma/leaf/data/celeba/data/test
......@@ -17,8 +16,9 @@ lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator
training_class = GradientAccumulator
epochs_per_round = 5
batch_size = 512
rounds = 1
full_epochs = True
batch_size = 64
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
......
eval/config_celeba_100.ini → eval/epoch_configs/config_celeba_100.ini
View file @ d140bf44
......@@ -2,7 +2,6 @@
dataset_package = decentralizepy.datasets.Celeba
dataset_class = Celeba
model_class = CNN
n_procs = 96
images_dir = /home/risharma/leaf/data/celeba/data/raw/img_align_celeba
train_dir = /home/risharma/leaf/data/celeba/per_user_data/train
test_dir = /home/risharma/leaf/data/celeba/data/test
......@@ -17,8 +16,9 @@ lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
epochs_per_round = 5
batch_size = 512
rounds = 1
full_epochs = True
batch_size = 64
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
......
eval/epoch_configs/config_celeba_grow.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.Celeba
dataset_class = Celeba
model_class = CNN
images_dir = /home/risharma/leaf/data/celeba/data/raw/img_align_celeba
train_dir = /home/risharma/leaf/data/celeba/per_user_data/train
test_dir = /home/risharma/leaf/data/celeba/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator
training_class = GradientAccumulator
rounds = 1
full_epochs = True
batch_size = 64
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.GrowingAlpha
sharing_class = GrowingAlpha
init_alpha=0.10
max_alpha=0.75
k=6
metadata_cap=0.65
eval/config.ini → eval/epoch_configs/config_femnist.ini
View file @ d140bf44
......@@ -2,9 +2,8 @@
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
model_class = CNN
n_procs = 16
train_dir = /home/risharma/Gitlab/decentralizepy/leaf/data/femnist/per_user_data/train
test_dir = /home/risharma/Gitlab/decentralizepy/leaf/data/femnist/data/test
train_dir = /home/risharma/leaf/data/femnist/per_user_data/train
test_dir = /home/risharma/leaf/data/femnist/data/test
; python list of fractions below
sizes =
......@@ -16,8 +15,9 @@ lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator
training_class = GradientAccumulator
epochs_per_round = 5
batch_size = 1024
rounds = 1
full_epochs = True
batch_size = 128
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
......
eval/epoch_configs/config_femnist_100.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
model_class = CNN
train_dir = /home/risharma/leaf/data/femnist/per_user_data/train
test_dir = /home/risharma/leaf/data/femnist/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 1
full_epochs = True
batch_size = 128
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.Sharing
sharing_class = Sharing
eval/epoch_configs/config_femnist_grow.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
model_class = CNN
train_dir = /home/risharma/leaf/data/femnist/per_user_data/train
test_dir = /home/risharma/leaf/data/femnist/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator
training_class = GradientAccumulator
rounds = 1
full_epochs = True
batch_size = 128
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.GrowingAlpha
sharing_class = GrowingAlpha
init_alpha=0.10
max_alpha=0.75
k=8
metadata_cap=0.65
\ No newline at end of file
eval/ip_addr_7Machines.json 0 → 100644
View file @ d140bf44
{
"0": "10.90.41.127",
"1": "10.90.41.128",
"2": "10.90.41.129",
"3": "10.90.41.130",
"4": "10.90.41.131",
"5": "10.90.41.132",
"6": "10.90.41.133"
}
\ No newline at end of file
eval/main.ipynb
View file @ d140bf44
This diff is collapsed.
eval/plot.py
View file @ d140bf44
import json
import os
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from matplotlib import pyplot as plt
from numpy.core.numeric import indices
def get_stats(l):
......@@ -24,25 +26,41 @@ def get_stats(l):
return mean_dict, stdev_dict, min_dict, max_dict
def plot(means, stdevs, mins, maxs, title, label, loc):
def plot(means, stdevs, mins, maxs, title, label, loc, xlabel="communication rounds"):
plt.title(title)
plt.xlabel("communication rounds")
x_axis = list(means.keys())
y_axis = list(means.values())
err = list(stdevs.values())
plt.errorbar(x_axis, y_axis, yerr=err, label=label)
plt.xlabel(xlabel)
x_axis = np.array(list(means.keys()))
y_axis = np.array(list(means.values()))
err = np.array(list(stdevs.values()))
plt.plot(x_axis, y_axis, label=label)
plt.fill_between(x_axis, y_axis - err, y_axis + err, alpha=0.4)
plt.legend(loc=loc)
def plot_results(path):
def replace_dict_key(d_org: dict, d_other: dict):
result = {}
for x, y in d_org.items():
result[d_other[x]] = y
return result
def plot_results(path, centralized, data_machine="machine0", data_node=0):
folders = os.listdir(path)
if centralized.lower() in ["true", "1", "t", "y", "yes"]:
centralized = True
print("Centralized")
else:
centralized = False
folders.sort()
print("Reading folders from: ", path)
print("Folders: ", folders)
bytes_means, bytes_stdevs = {}, {}
meta_means, meta_stdevs = {}, {}
data_means, data_stdevs = {}, {}
for folder in folders:
folder_path = os.path.join(path, folder)
if not os.path.isdir(folder_path):
folder_path = Path(os.path.join(path, folder))
if not folder_path.is_dir() or "weights" == folder_path.name:
continue
results = []
machine_folders = os.listdir(folder_path)
......@@ -56,18 +74,130 @@ def plot_results(path):
filepath = os.path.join(mf_path, f)
with open(filepath, "r") as inf:
results.append(json.load(inf))
if folder.startswith("FL") or folder.startswith("Parameter Server"):
data_node = -1
else:
data_node = 0
with open(folder_path / data_machine / f"{data_node}_results.json", "r") as f:
main_data = json.load(f)
main_data = [main_data]
# Plotting bytes over time
plt.figure(10)
b_means, stdevs, mins, maxs = get_stats([x["total_bytes"] for x in results])
plot(b_means, stdevs, mins, maxs, "Total Bytes", folder, "lower right")
df = pd.DataFrame(
{
"mean": list(b_means.values()),
"std": list(stdevs.values()),
"nr_nodes": [len(results)] * len(b_means),
},
list(b_means.keys()),
columns=["mean", "std", "nr_nodes"],
)
df.to_csv(
os.path.join(path, "total_bytes_" + folder + ".csv"), index_label="rounds"
)
# Plot Training loss
plt.figure(1)
means, stdevs, mins, maxs = get_stats([x["train_loss"] for x in results])
plot(means, stdevs, mins, maxs, "Training Loss", folder, "upper right")
correct_bytes = [b_means[x] for x in means]
df = pd.DataFrame(
{
"mean": list(means.values()),
"std": list(stdevs.values()),
"nr_nodes": [len(results)] * len(means),
"total_bytes": correct_bytes,
},
list(means.keys()),
columns=["mean", "std", "nr_nodes", "total_bytes"],
)
plt.figure(11)
means = replace_dict_key(means, b_means)
plot(
means,
stdevs,
mins,
maxs,
"Training Loss",
folder,
"upper right",
"Total Bytes per node",
)
df.to_csv(
os.path.join(path, "train_loss_" + folder + ".csv"), index_label="rounds"
)
# Plot Testing loss
plt.figure(2)
means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in results])
if centralized:
means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in main_data])
else:
means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in results])
plot(means, stdevs, mins, maxs, "Testing Loss", folder, "upper right")
df = pd.DataFrame(
{
"mean": list(means.values()),
"std": list(stdevs.values()),
"nr_nodes": [len(results)] * len(means),
"total_bytes": correct_bytes,
},
list(means.keys()),
columns=["mean", "std", "nr_nodes", "total_bytes"],
)
plt.figure(12)
means = replace_dict_key(means, b_means)
plot(
means,
stdevs,
mins,
maxs,
"Testing Loss",
folder,
"upper right",
"Total Bytes per node",
)
df.to_csv(
os.path.join(path, "test_loss_" + folder + ".csv"), index_label="rounds"
)
# Plot Testing Accuracy
plt.figure(3)
means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in results])
if centralized:
means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in main_data])
else:
means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in results])
plot(means, stdevs, mins, maxs, "Testing Accuracy", folder, "lower right")
df = pd.DataFrame(
{
"mean": list(means.values()),
"std": list(stdevs.values()),
"nr_nodes": [len(results)] * len(means),
"total_bytes": correct_bytes,
},
list(means.keys()),
columns=["mean", "std", "nr_nodes", "total_bytes"],
)
plt.figure(13)
means = replace_dict_key(means, b_means)
plot(
means,
stdevs,
mins,
maxs,
"Testing Accuracy",
folder,
"lower right",
"Total Bytes per node",
)
df.to_csv(
os.path.join(path, "test_acc_" + folder + ".csv"), index_label="rounds"
)
# Collect total_bytes shared
bytes_list = []
for x in results:
......@@ -77,12 +207,40 @@ def plot_results(path):
bytes_means[folder] = list(means.values())[0]
bytes_stdevs[folder] = list(stdevs.values())[0]
meta_list = []
for x in results:
if x["total_meta"]:
max_key = str(max(list(map(int, x["total_meta"].keys()))))
meta_list.append({max_key: x["total_meta"][max_key]})
else:
meta_list.append({max_key: 0})
means, stdevs, mins, maxs = get_stats(meta_list)
meta_means[folder] = list(means.values())[0]
meta_stdevs[folder] = list(stdevs.values())[0]
data_list = []
for x in results:
max_key = str(max(list(map(int, x["total_data_per_n"].keys()))))
data_list.append({max_key: x["total_data_per_n"][max_key]})
means, stdevs, mins, maxs = get_stats(data_list)
data_means[folder] = list(means.values())[0]
data_stdevs[folder] = list(stdevs.values())[0]
plt.figure(10)
plt.savefig(os.path.join(path, "total_bytes.png"), dpi=300)
plt.figure(11)
plt.savefig(os.path.join(path, "bytes_train_loss.png"), dpi=300)
plt.figure(12)
plt.savefig(os.path.join(path, "bytes_test_loss.png"), dpi=300)
plt.figure(13)
plt.savefig(os.path.join(path, "bytes_test_acc.png"), dpi=300)
plt.figure(1)
plt.savefig(os.path.join(path, "train_loss.png"))
plt.savefig(os.path.join(path, "train_loss.png"), dpi=300)
plt.figure(2)
plt.savefig(os.path.join(path, "test_loss.png"))
plt.savefig(os.path.join(path, "test_loss.png"), dpi=300)
plt.figure(3)
plt.savefig(os.path.join(path, "test_acc.png"))
plt.savefig(os.path.join(path, "test_acc.png"), dpi=300)
# Plot total_bytes
plt.figure(4)
plt.title("Data Shared")
......@@ -96,7 +254,31 @@ def plot_results(path):
plt.ylabel("Total data shared in MBs")
plt.xlabel("Fraction of Model Shared")
plt.xticks(x_pos, list(bytes_means.keys()))
plt.savefig(os.path.join(path, "data_shared.png"))
plt.savefig(os.path.join(path, "data_shared.png"), dpi=300)
# Plot stacked_bytes
plt.figure(5)
plt.title("Data Shared per Neighbor")
x_pos = np.arange(len(bytes_means.keys()))
plt.bar(
x_pos,
np.array(list(data_means.values())) // (1024 * 1024),
yerr=np.array(list(data_stdevs.values())) // (1024 * 1024),
align="center",
label="Parameters",
)
plt.bar(
x_pos,
np.array(list(meta_means.values())) // (1024 * 1024),
bottom=np.array(list(data_means.values())) // (1024 * 1024),
yerr=np.array(list(meta_stdevs.values())) // (1024 * 1024),
align="center",
label="Metadata",
)
plt.ylabel("Data shared in MBs")
plt.xlabel("Fraction of Model Shared")
plt.xticks(x_pos, list(meta_means.keys()))
plt.savefig(os.path.join(path, "parameters_metadata.png"), dpi=300)
def plot_parameters(path):
......@@ -133,6 +315,10 @@ def plot_parameters(path):
if __name__ == "__main__":
assert len(sys.argv) == 2
plot_results(sys.argv[1])
assert len(sys.argv) == 3
# The args are:
# 1: the folder with the data
# 2: True/False: If True then the evaluation on the test set was centralized
# for federated learning folder name must start with "FL"!
plot_results(sys.argv[1], sys.argv[2])
# plot_parameters(sys.argv[1])
eval/plot_model.py 0 → 100644
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eval/plot_percentile.py 0 → 100644
View file @ d140bf44
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eval/plot_shared.py 0 → 100644
View file @ d140bf44
import json
import os
import sys
from pathlib import Path
import numpy as np
from matplotlib import pyplot as plt
def plot(x, y, label, *args):
plt.plot(x, y, *args, label=label)
plt.legend()
def plot_shared(path, title):
model_path = os.path.join(path, "plots")
Path(model_path).mkdir(parents=True, exist_ok=True)
files = [f for f in os.listdir(path) if f.endswith("json")]
assert len(files) > 0
for i, file in enumerate(files):
filepath = os.path.join(path, file)
with open(filepath, "r") as inf:
model_vec = json.load(inf)
del model_vec["order"]
if i == 0:
total_params = 0
for l in model_vec["shapes"].values():
current_params = 1
for v in l:
current_params *= v
total_params += current_params
print("Total Params: ", str(total_params))
shared_count = np.zeros(total_params, dtype=int)
del model_vec["shapes"]
model_vec = np.array(model_vec[list(model_vec.keys())[0]])
shared_count[model_vec] += 1
print("sum: ", np.sum(shared_count))
num_elements = shared_count.shape[0]
x_axis = np.arange(1, num_elements + 1)
plt.clf()
plt.title(title)
plot(x_axis, shared_count, "unsorted", ".")
shared_count = np.sort(shared_count)
plot(x_axis, shared_count, "sorted")
plt.savefig(os.path.join(model_path, "shared_plot.png"))
if __name__ == "__main__":
assert len(sys.argv) == 2
plot_shared(sys.argv[1], "Shared Parameters")
eval/plotting_from_csv.py 0 → 100644
View file @ d140bf44
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eval/run.sh 0 → 100755
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eval/run_all.sh 0 → 100755
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