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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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View file @ d140bf44
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eval/96_nodes.edges → eval/96_nodes_smallworld.edges
View file @ d140bf44
File moved
eval/96_regular.edges 0 → 100644
View file @ d140bf44
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eval/epoch_configs/config_celeba.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.PartialModel
sharing_class = PartialModel
eval/epoch_configs/config_celeba_100.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.Training
training_class = Training
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.Sharing
sharing_class = Sharing
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
......@@ -2,22 +2,22 @@
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
model_class = CNN
n_procs = 4
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 =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
lr = 0.01
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator
training_class = GradientAccumulator
epochs_per_round = 2
batch_size = 1024
rounds = 1
full_epochs = True
batch_size = 128
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
......@@ -30,4 +30,3 @@ addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.95
\ No newline at end of file
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
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eval/ip_addr_4Machines.json 0 → 100644
View file @ d140bf44
{
"0": "10.90.41.130",
"1": "10.90.41.131",
"2": "10.90.41.132",
"3": "10.90.41.133"
}
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eval/ip_addr_5Machines.json 0 → 100644
View file @ d140bf44
{
"0": "10.90.41.129",
"1": "10.90.41.130",
"2": "10.90.41.131",
"3": "10.90.41.132",
"4": "10.90.41.133"
}
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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"
}
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eval/main.ipynb
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This diff is collapsed.
eval/plot.py 0 → 100644
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
def get_stats(l):
assert len(l) > 0
mean_dict, stdev_dict, min_dict, max_dict = {}, {}, {}, {}
for key in l[0].keys():
all_nodes = [i[key] for i in l]
all_nodes = np.array(all_nodes)
mean = np.mean(all_nodes)
std = np.std(all_nodes)
min = np.min(all_nodes)
max = np.max(all_nodes)
mean_dict[int(key)] = mean
stdev_dict[int(key)] = std
min_dict[int(key)] = min
max_dict[int(key)] = max
return mean_dict, stdev_dict, min_dict, max_dict
def plot(means, stdevs, mins, maxs, title, label, loc, xlabel="communication rounds"):
plt.title(title)
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 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 = 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)
for machine_folder in machine_folders:
mf_path = os.path.join(folder_path, machine_folder)
if not os.path.isdir(mf_path):
continue
files = os.listdir(mf_path)
files = [f for f in files if f.endswith("_results.json")]
for f in files:
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)
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)
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:
max_key = str(max(list(map(int, x["total_bytes"].keys()))))
bytes_list.append({max_key: x["total_bytes"][max_key]})
means, stdevs, mins, maxs = get_stats(bytes_list)
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"), dpi=300)
plt.figure(2)
plt.savefig(os.path.join(path, "test_loss.png"), dpi=300)
plt.figure(3)
plt.savefig(os.path.join(path, "test_acc.png"), dpi=300)
# Plot total_bytes
plt.figure(4)
plt.title("Data Shared")
x_pos = np.arange(len(bytes_means.keys()))
plt.bar(
x_pos,
np.array(list(bytes_means.values())) // (1024 * 1024),
yerr=np.array(list(bytes_stdevs.values())) // (1024 * 1024),
align="center",
)
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"), 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):
plt.figure(4)
folders = os.listdir(path)
for folder in folders:
folder_path = os.path.join(path, folder)
if not os.path.isdir(folder_path):
continue
files = os.listdir(folder_path)
files = [f for f in files if f.endswith("_shared_params.json")]
for f in files:
filepath = os.path.join(folder_path, f)
print("Working with ", filepath)
with open(filepath, "r") as inf:
loaded_dict = json.load(inf)
del loaded_dict["order"]
del loaded_dict["shapes"]
assert len(loaded_dict["0"]) > 0
assert "0" in loaded_dict.keys()
counts = np.zeros(len(loaded_dict["0"]))
for key in loaded_dict.keys():
indices = np.array(loaded_dict[key])
counts = np.pad(
counts,
max(np.max(indices) - counts.shape[0], 0),
"constant",
constant_values=0,
)
counts[indices] += 1
plt.plot(np.arange(0, counts.shape[0]), counts, ".")
print("Saving scatterplot")
plt.savefig(os.path.join(folder_path, "shared_params.png"))
if __name__ == "__main__":
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
View file @ d140bf44
import json
import os
import sys
from pathlib import Path
import numpy as np
from matplotlib import pyplot as plt
from pyexpat import model
def plot(x, y, label, *args):
plt.plot(x, y, *args, label=label)
plt.legend()
def reject_outliers(data, m=2.0):
d = np.abs(data - np.median(data))
mdev = np.median(d)
s = d / (mdev if mdev else 1.0)
return data[s < m]
def plot_model(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")]
for file in files:
filepath = os.path.join(path, file)
with open(filepath, "r") as inf:
model_vec = json.load(inf)
del model_vec["order"]
del model_vec["shapes"]
model_vec = np.array(model_vec[list(model_vec.keys())[0]])
num_elements = model_vec.shape[0]
x_axis = np.arange(1, num_elements + 1)
plt.clf()
plt.title(title)
plot(x_axis, model_vec, "unsorted", ".")
model_vec = np.sort(model_vec)
plot(x_axis, model_vec, "sorted")
plt.savefig(os.path.join(model_path, file[0:-5]))
def plot_ratio(path_change, path_val, title):
model_path = os.path.join(path_change, "plots_ratio")
Path(model_path).mkdir(parents=True, exist_ok=True)
files_change = [f for f in os.listdir(path_change) if f.endswith("json")]
files_val = [f for f in os.listdir(path_val) if f.endswith("json")]
for i, file in enumerate(files_change):
print("Processed ", file)
filepath_change = os.path.join(path_change, file)
filepath_val = os.path.join(path_val, files_val[i])
with open(filepath_change, "r") as inf:
model_change = json.load(inf)
del model_change["order"]
del model_change["shapes"]
model_change = np.array(model_change[list(model_change.keys())[0]])
with open(filepath_val, "r") as inf:
model_val = json.load(inf)
del model_val["order"]
del model_val["shapes"]
model_val = np.array(model_val[list(model_val.keys())[0]])
num_elements = model_val.shape[0]
x_axis = np.arange(1, num_elements + 1)
plt.clf()
plt.title(title)
model_vec = np.divide(
model_change,
model_val,
out=np.zeros_like(model_change),
where=model_val != 0.0,
)
model_vec = reject_outliers(model_vec)
num_elements = model_vec.shape[0]
x_axis = np.arange(1, num_elements + 1)
plot(x_axis, model_vec, "unsorted", ".")
model_vec = np.sort(model_vec)
plot(x_axis, model_vec, "sorted")
plt.savefig(os.path.join(model_path, file[0:-5]))
if __name__ == "__main__":
assert len(sys.argv) == 3
plot_model(
os.path.join(sys.argv[1], "model_change", sys.argv[2]), "Change in Weights"
)
plot_model(os.path.join(sys.argv[1], "model_val", sys.argv[2]), "Model Parameters")
plot_ratio(
os.path.join(sys.argv[1], "model_change", sys.argv[2]),
os.path.join(sys.argv[1], "model_val", sys.argv[2]),
"Ratio",
)