Datasets:
Benchify
/
realpbt

Dataset card Data Studio Files
xet
 Community
1
id
int64
1
6.07M
name
stringlengths
1
295
code
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12
426k
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1 value
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start_line
int64
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end_line
int64
1
158k
repo
dict
101
check_files
def check_files(paths: List[Path]) -> None: for p in paths: generated_path = p.parent / str(p).replace(GENERATE_SUFFIX, GENERATED_SUFFIX) print(f"INFO: Checking {generated_path}...") with temp_fname() as temp_file: generate_file(p, temp_file) format_file(temp_file) assert filecmp.cmp( generated_path, temp_file ), f"expected: {open(temp_file).read()}"
python
tools/generate-tool.py
97
106
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
102
main
def main() -> None: path_list = get_paths() if args.generate: generate_files(path_list) if args.format: format_files(path_list) if args.check: check_files(path_list)
python
tools/generate-tool.py
109
118
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
103
locate_py_files
def locate_py_files(root_path: pathlib.Path): """Recursively search for Python files in the given root directory.""" include = {root_path / dir_path for dir_path in CONFIG["include"]} exclude = {root_path / dir_path for dir_path in CONFIG["exclude"]} exclude_unrooted = CONFIG["exclude_unrooted"] for path in map(str, root_path.rglob("*.py")): if ( any( path.startswith(str(root_path / dir_path)) for dir_path in map(pathlib.Path.absolute, include) ) and all( not path.startswith(str(root_path / dir_path)) for dir_path in map(pathlib.Path.absolute, exclude) ) and all(dir_path not in path for dir_path in exclude_unrooted) ): print(path)
python
tools/locate-py-files.py
24
41
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
104
__init__
def __init__(self, size, num_samples): self.len = num_samples self.data = torch.randn(num_samples, size)
python
tests/functional_tests/t0_main/lightning/pl_base.py
7
9
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
105
__getitem__
def __getitem__(self, index): return self.data[index]
python
tests/functional_tests/t0_main/lightning/pl_base.py
11
12
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
106
__len__
def __len__(self): return self.len
python
tests/functional_tests/t0_main/lightning/pl_base.py
14
15
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
107
__init__
def __init__(self): super().__init__() self.layer = torch.nn.Linear(32, 2)
python
tests/functional_tests/t0_main/lightning/pl_base.py
19
21
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
108
forward
def forward(self, x): return self.layer(x)
python
tests/functional_tests/t0_main/lightning/pl_base.py
23
24
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
109
loss
def loss(self, batch, prediction): # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
python
tests/functional_tests/t0_main/lightning/pl_base.py
26
28
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
110
training_step
def training_step(self, batch, batch_idx): output = self.layer(batch) loss = self.loss(batch, output) self.log("loss", loss) return {"loss": loss}
python
tests/functional_tests/t0_main/lightning/pl_base.py
30
34
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
111
training_step_end
def training_step_end(self, training_step_outputs): return training_step_outputs
python
tests/functional_tests/t0_main/lightning/pl_base.py
36
37
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
112
training_epoch_end
def training_epoch_end(self, outputs) -> None: torch.stack([x["loss"] for x in outputs]).mean()
python
tests/functional_tests/t0_main/lightning/pl_base.py
39
40
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
113
validation_step
def validation_step(self, batch, batch_idx): output = self.layer(batch) loss = self.loss(batch, output) return {"x": loss}
python
tests/functional_tests/t0_main/lightning/pl_base.py
42
45
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
114
validation_epoch_end
def validation_epoch_end(self, outputs) -> None: torch.stack([x["x"] for x in outputs]).mean()
python
tests/functional_tests/t0_main/lightning/pl_base.py
47
48
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
115
test_step
def test_step(self, batch, batch_idx): output = self.layer(batch) loss = self.loss(batch, output) self.log("fake_test_acc", loss) return {"y": loss}
python
tests/functional_tests/t0_main/lightning/pl_base.py
50
54
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
116
test_epoch_end
def test_epoch_end(self, outputs) -> None: torch.stack([x["y"] for x in outputs]).mean()
python
tests/functional_tests/t0_main/lightning/pl_base.py
56
57
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
117
configure_optimizers
def configure_optimizers(self): optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1) return [optimizer], [lr_scheduler]
python
tests/functional_tests/t0_main/lightning/pl_base.py
59
62
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
118
main
def main(): # Use concurrency experiment wandb.require(experiment="service") print("PIDPID", os.getpid()) # Set up data num_samples = 100000 train = DataLoader(RandomDataset(32, num_samples), batch_size=32) val = DataLoader(RandomDataset(32, num_samples), batch_size=32) test = DataLoader(RandomDataset(32, num_samples), batch_size=32) # init model model = BoringModel() # set up wandb config = dict(some_hparam="Logged Before Trainer starts DDP") wandb_logger = WandbLogger(log_model=True, config=config, save_code=True) # Initialize a trainer trainer = Trainer( max_epochs=1, logger=wandb_logger, accelerator="tpu", devices=8, strategy="ddp", ) # Train the model trainer.fit(model, train, val) trainer.test(test_dataloaders=test)
python
tests/functional_tests/t0_main/lightning/train_tpu_ddp.py
12
40
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
119
main
def main(): # Use concurrency experiment print("PIDPID", os.getpid()) # Set up data num_samples = 100000 train = RandomDataset(32, num_samples) train = DataLoader(train, batch_size=32) val = RandomDataset(32, num_samples) val = DataLoader(val, batch_size=32) test = RandomDataset(32, num_samples) test = DataLoader(test, batch_size=32) # init model model = BoringModel() # set up wandb config = dict(some_hparam="Logged Before Trainer starts DDP") wandb_logger = WandbLogger(log_model=True, config=config, save_code=True) # Initialize a trainer trainer = pl.Trainer( max_epochs=1, devices=2, accelerator="cpu", strategy="ddp", logger=wandb_logger, ) # Train the model trainer.fit(model, train, val) trainer.test(dataloaders=test)
python
tests/functional_tests/t0_main/lightning/t0_strategy_ddp.py
10
40
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
120
main
def main(): # Use concurrency experiment print("PIDPID", os.getpid()) # Set up data num_samples = 100000 train = DataLoader(RandomDataset(32, num_samples), batch_size=32) val = DataLoader(RandomDataset(32, num_samples), batch_size=32) test = DataLoader(RandomDataset(32, num_samples), batch_size=32) # init model model = BoringModel() # set up wandb config = dict(some_hparam="Logged Before Trainer starts DDP") wandb_logger = WandbLogger(log_model=True, config=config, save_code=True) # Initialize a trainer trainer = Trainer( max_epochs=1, devices=2, accelerator="cpu", strategy="ddp_spawn", logger=wandb_logger, ) # Train the model trainer.fit(model, train, val) trainer.test(dataloaders=test)
python
tests/functional_tests/t0_main/lightning/t1_strategy_ddp_spawn.py
10
37
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
121
main
def main(): # Use concurrency experiment wandb.require(experiment="service") # boost stats logging frequency for testing stats_settings = dict(_stats_sample_rate_seconds=0.5, _stats_samples_to_average=2) wandb.setup(settings=stats_settings) print("User process PID:", os.getpid()) # Set up data num_samples = 100000 train = DataLoader(RandomDataset(32, num_samples), batch_size=32) val = DataLoader(RandomDataset(32, num_samples), batch_size=32) test = DataLoader(RandomDataset(32, num_samples), batch_size=32) # init model model = BoringModel() # set up wandb config = dict(some_hparam="Logged Before Trainer starts DDP") wandb_logger = WandbLogger( log_model=True, config=config, save_code=True, name=pathlib.Path(__file__).stem, ) # Initialize a trainer trainer = Trainer( max_epochs=2, devices=2, accelerator="gpu", strategy="ddp", logger=wandb_logger, ) # Train the model trainer.fit(model, train, val) trainer.test(dataloaders=test)
python
tests/functional_tests/t0_main/lightning/train_gpu_ddp.py
13
51
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
122
main
def main(): with tempfile.TemporaryDirectory() as tmpdir: with wandb.init() as run: wandb.log({"metric": 5}) artifact = wandb.Artifact("test-artifact", "test-type") with open(tmpdir + "/boom.txt", "w") as f: f.write("testing") local_path = f"{tmpdir}/boom.txt" artifact.add_file(local_path, "boom/test-name") artifact = run.log_artifact(artifact) artifact.wait() _ = use_model("test-artifact:latest")
python
tests/functional_tests/t0_main/artifacts/use-model-error.py
7
20
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
123
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/artifacts/link-model.py
9
16
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
124
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/artifacts/link-model.py
18
31
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
125
main
def main(): my_model = Net() wandb.init() best_model = log_model(my_model, "my-model", aliases=["boom"], scope_project=True) link_model(best_model, "entity/project/test_portfolio") wandb.finish()
python
tests/functional_tests/t0_main/artifacts/link-model.py
34
43
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
126
make_image
def make_image(): return wandb.Image(np.random.randint(255, size=(32, 32)))
python
tests/functional_tests/t0_main/artifacts/log-image-artifact-path.py
5
6
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
127
main
def main(): # Base Case with wandb.init() as run: run.log({"image": make_image()}) # With Logged Target with wandb.init() as run: art = wandb.Artifact("examples", "images") image = make_image() art.add(image, "image") run.log_artifact(art) run.log({"image": image})
python
tests/functional_tests/t0_main/artifacts/log-image-artifact-path.py
9
20
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
128
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/artifacts/link-model-outside-run.py
10
17
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
129
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/artifacts/link-model-outside-run.py
19
32
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
130
main
def main(): my_model = Net() sm = _SavedModel.init(my_model) art = wandb.Artifact("my-model", "model") art.add(sm, "index") link_model(sm, "entity/project/test_portfolio")
python
tests/functional_tests/t0_main/artifacts/link-model-outside-run.py
35
41
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
131
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/artifacts/use-and-link-model.py
10
17
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
132
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/artifacts/use-and-link-model.py
19
32
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
133
main
def main(): run = wandb.init() my_model = Net() sm = _SavedModel.init(my_model) art = wandb.Artifact("my-model", "model") art.add(sm, "index") art = run.log_artifact(art) art.wait() sm = use_model("my-model:latest") link_model(sm, "entity/project/test_portfolio") run.finish()
python
tests/functional_tests/t0_main/artifacts/use-and-link-model.py
35
50
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
134
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/artifacts/public-link-model.py
8
15
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
135
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/artifacts/public-link-model.py
17
30
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
136
main
def main(): # create an artifact # call .wait() to get a Public Artifact bound to it # and then do link on that artifact run = wandb.init() with open("my-dataset.txt", "w") as fp: fp.write("this-is-data") art = wandb.Artifact("my-art-name", "my-art-type") art.add_file("my-dataset.txt") art = run.log_artifact(art) art.wait() art._logged_artifact._instance.link("entity/project/test_portfolio", aliases="best") run.finish()
python
tests/functional_tests/t0_main/artifacts/public-link-model.py
33
45
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
137
main
def main(): with tempfile.TemporaryDirectory() as tmpdir: with wandb.init() as run: wandb.log({"metric": 5}) artifact = wandb.Artifact("test-artifact", "test-type") with open(tmpdir + "/boom.txt", "w") as f: f.write("testing") local_path = f"{tmpdir}/boom.txt" artifact.add_file(local_path, "index/test-name") artifact = run.log_artifact(artifact) artifact.wait() _ = use_model("test-artifact:latest")
python
tests/functional_tests/t0_main/artifacts/use-model-outside-run-error.py
7
20
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
138
main
def main(): with tempfile.TemporaryDirectory() as tmpdir: with wandb.init() as run: wandb.log({"metric": 5}) artifact = wandb.Artifact("test-artifact", "model") with open(tmpdir + "/boom.txt", "w") as f: f.write("testing") local_path = f"{tmpdir}/boom.txt" artifact.add_file(local_path, "test-name") run.link_artifact(artifact, "entity/project/test_portfolio")
python
tests/functional_tests/t0_main/artifacts/link-artifact.py
10
20
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
139
main
def main(): run = wandb.init(entity="mock_server_entity", project="test") art = wandb.Artifact("test_artifact", type="model") art.add_file("public_collection.py") run.link_artifact(art, "mock_server_entity/test/test_port") run.finish() collections = wandb.Api().artifact_type("model", "test").collections() assert len(collections) == 2
python
tests/functional_tests/t0_main/artifacts/public_collections.py
4
12
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
140
main
def main(): with tempfile.TemporaryDirectory() as tmpdir: with wandb.init() as run: wandb.log({"metric": 5}) artifact = wandb.Artifact("test-artifact", "test-type") with open(tmpdir + "/boom.txt", "w") as f: f.write("testing") local_path = f"{tmpdir}/boom.txt" artifact.add_file(local_path, "test-name") run.log_artifact(artifact)
python
tests/functional_tests/t0_main/artifacts/log-artifact.py
6
16
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
141
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/artifacts/use-model.py
10
17
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
142
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/artifacts/use-model.py
19
32
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
143
main
def main(): run = wandb.init(entity="mock_server_entity", project="test") my_model = Net() sm = _SavedModel.init(my_model) art = wandb.Artifact("my-model", "model") art.add(sm, "index") art = run.log_artifact(art) art.wait() _ = use_model("my-model:latest") run.finish()
python
tests/functional_tests/t0_main/artifacts/use-model.py
35
49
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
144
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/artifacts/log-model.py
9
16
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
145
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/artifacts/log-model.py
18
31
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
146
main
def main(): my_model = Net() _ = log_model(my_model, "my-model", aliases=["boom"], scope_project=True) wandb.finish()
python
tests/functional_tests/t0_main/artifacts/log-model.py
34
39
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
147
main
def main(): with wandb.init() as run: artifact = wandb.Artifact("my_artifact", type="unicode_artifact") with artifact.new_file("euler.txt", mode="w", encoding="utf-8") as f: f.write("e^(iπ)+1=0") run.log_artifact(artifact)
python
tests/functional_tests/t0_main/artifacts/log-unicode-artifact.py
4
10
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
148
make_env
def make_env(): env = gym.make(config["env_name"]) env = Monitor(env) # record stats such as returns return env
python
tests/functional_tests/t0_main/sb3/t1_stable_baselines3.py
24
27
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
149
train
def train(layers, epochs): for n in range(layers or 10): print(f"Layer: {n}") for n in range(epochs or 10): print(f"Epoch: {n}")
python
tests/functional_tests/t0_main/magic/t2_no_keras.py
26
30
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
150
main
def main(): assert "tensorflow" not in sys.modules parser = argparse.ArgumentParser() parser.add_argument("--layers", type=int, help="num layers") parser.add_argument("--epochs", type=int, default=4, help="num epochs") args = parser.parse_args() train(args.layers, args.epochs)
python
tests/functional_tests/t0_main/magic/t2_no_keras.py
33
39
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
151
process_child
def process_child(run): # need to re-seed the rng otherwise we get image collision rng = np.random.default_rng(os.getpid()) height = width = 2 media = [wandb.Image(rng.random((height, width))) for _ in range(3)] run.log({"media": media})
python
tests/functional_tests/t0_main/grpc/t2_mp_log_image_sequence.py
15
21
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
152
main
def main(): # Temporary environment variable for testing grpc service mode os.environ["WANDB_SERVICE_TRANSPORT"] = "grpc" wandb.require("service") run = wandb.init() # Start a new run in parallel in a child process processes = [ mp.Process(target=process_child, kwargs=dict(run=run)) for _ in range(2) ] for p in processes: p.start() for p in processes: p.join() run.finish()
python
tests/functional_tests/t0_main/grpc/t2_mp_log_image_sequence.py
24
42
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
153
objective
def objective(config, checkpoint_dir=None): for _i in range(30): loss = config["mean"] + config["sd"] * np.random.randn() session.report({"loss": loss})
python
tests/functional_tests/t0_main/raytune/t2_callback.py
14
17
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
154
tune_function
def tune_function(api_key_file): """Example for using a WandbLoggerCallback with the function API.""" tuner = tune.Tuner( objective, tune_config=tune.TuneConfig( metric="loss", mode="min", ), run_config=air.RunConfig( callbacks=[ WandbLoggerCallback(api_key_file=api_key_file, project="Wandb_example") ], ), param_space={ "mean": tune.grid_search([1, 2, 3, 4, 5]), "sd": tune.uniform(0.2, 0.8), }, ) results = tuner.fit() return results.get_best_result().config
python
tests/functional_tests/t0_main/raytune/t2_callback.py
20
40
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
155
main
def main(): api_key_file = get_wandb_api_key_file() tune_function(api_key_file)
python
tests/functional_tests/t0_main/raytune/t2_callback.py
43
45
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
156
train_fn
def train_fn(config): for _i in range(10): loss = config["a"] + config["b"] wandb.log({"loss": loss}) tune.report(loss=loss)
python
tests/functional_tests/t0_main/raytune/t1_example.py
17
21
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
157
decorated_objective
def decorated_objective(config, checkpoint_dir=None): for _i in range(30): loss = config["mean"] + config["sd"] * np.random.randn() session.report({"loss": loss}) wandb.log(dict(loss=loss))
python
tests/functional_tests/t0_main/raytune/t3_mixin.py
16
20
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
158
tune_decorated
def tune_decorated(api_key_file): """Example for using the @wandb_mixin decorator with the function API.""" tuner = tune.Tuner( decorated_objective, tune_config=tune.TuneConfig( metric="loss", mode="min", ), param_space={ "mean": tune.grid_search([1, 2, 3, 4, 5]), "sd": tune.uniform(0.2, 0.8), "wandb": {"api_key_file": api_key_file, "project": "Wandb_example"}, }, ) results = tuner.fit() return results.get_best_result().config
python
tests/functional_tests/t0_main/raytune/t3_mixin.py
23
39
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
159
main
def main(): api_key_file = get_wandb_api_key_file() tune_decorated(api_key_file)
python
tests/functional_tests/t0_main/raytune/t3_mixin.py
42
44
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
160
train_mnist
def train_mnist(config): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") train_loader, test_loader = get_data_loaders() model = ConvNet() model.to(device) optimizer = optim.SGD( model.parameters(), lr=config["lr"], momentum=config["momentum"] ) for _i in range(5): train(model, optimizer, train_loader, device=device) acc = test(model, test_loader, device=device) # When using WandbLogger, the metrics reported to tune are also logged in the W&B dashboard tune.report(mean_accuracy=acc) # @wandb_mixin enables logging custom metrics using wandb.log() error_rate = 100 * (1 - acc) wandb.log({"error_rate": error_rate})
python
tests/functional_tests/t0_main/raytune/t5_colab.py
20
41
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
161
run
def run(): torch.backends.cudnn.deterministic = True random.seed(2022) np.random.seed(2022) torch.manual_seed(2022) torch.cuda.manual_seed_all(2022) wandb.login() wandb_init = {"project": "raytune-colab"} analysis = tune.run( train_mnist, loggers=[WandbLogger], resources_per_trial={"gpu": 0}, config={ # wandb dict accepts all arguments that can be passed in wandb.init() "wandb": wandb_init, # hyperparameters are set by keyword arguments "lr": tune.grid_search([0.0001, 0.001, 0.1]), "momentum": tune.grid_search([0.9, 0.99]), }, ) print("Best config: ", analysis.get_best_config(metric="mean_accuracy", mode="max"))
python
tests/functional_tests/t0_main/raytune/t5_colab.py
44
67
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
162
step
def step(self): for _i in range(30): loss = self.config["mean"] + self.config["sd"] * np.random.randn() wandb.log({"loss": loss}) return {"loss": loss, "done": True}
python
tests/functional_tests/t0_main/raytune/t4_trainable.py
16
20
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
163
tune_trainable
def tune_trainable(api_key_file): """Example for using a WandTrainableMixin with the class API.""" tuner = tune.Tuner( WandbTrainable, tune_config=tune.TuneConfig( metric="loss", mode="min", ), param_space={ "mean": tune.grid_search([1, 2, 3, 4, 5]), "sd": tune.uniform(0.2, 0.8), "wandb": {"api_key_file": api_key_file, "project": "Wandb_example"}, }, ) results = tuner.fit() return results.get_best_result().config
python
tests/functional_tests/t0_main/raytune/t4_trainable.py
23
39
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
164
main
def main(): api_key_file = get_wandb_api_key_file() tune_trainable(api_key_file)
python
tests/functional_tests/t0_main/raytune/t4_trainable.py
42
44
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
165
get_wandb_api_key
def get_wandb_api_key() -> str: base_url = os.environ.get("WANDB_BASE_URL", "https://api.wandb.ai") api_key = os.environ.get("WANDB_API_KEY") if not api_key: auth = requests.utils.get_netrc_auth(base_url) if not auth: raise ValueError( f"must configure api key by env or in netrc for {base_url}" ) api_key = auth[-1] return api_key
python
tests/functional_tests/t0_main/raytune/_test_support.py
6
16
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
166
get_wandb_api_key_file
def get_wandb_api_key_file(file_name: str = None) -> str: file_name = file_name or ".wandb-api-key.secret" api_key = get_wandb_api_key() with open(file_name, "w") as f: f.write(api_key) return os.path.abspath(file_name)
python
tests/functional_tests/t0_main/raytune/_test_support.py
19
24
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
167
run_first
def run_first() -> str: with wandb.init() as run: assert not run.resumed wandb.log(dict(m1=1)) wandb.log(dict(m2=2)) wandb.log(dict(m3=3)) run_id = run.id run_path = run.path return run_id, run_path
python
tests/functional_tests/t0_main/resume/two_runs.py
8
16
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
168
run_again
def run_again(run_id: str, resume: Optional[str]) -> None: kwargs = dict(id=run_id) if resume: kwargs["resume"] = resume with wandb.init(**kwargs) as run: if run.resumed: print("RUN_STATE: run resumed") else: print("RUN_STATE: run not resumed") wandb.log(dict(m1=11)) wandb.log(dict(m2=22)) wandb.log(dict(m4=44))
python
tests/functional_tests/t0_main/resume/two_runs.py
19
30
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
169
delete_run
def delete_run(run_path: str) -> None: api = wandb.Api() run = api.run(run_path) print(f"Deleting: {run_path}...") run.delete() print("done.")
python
tests/functional_tests/t0_main/resume/two_runs.py
33
38
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
170
main
def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--resume", type=str) parser.add_argument("--delete_run", action="store_true") args = parser.parse_args() run_id, run_path = run_first() if args.delete_run: delete_run(run_path) run_again(run_id=run_id, resume=args.resume)
python
tests/functional_tests/t0_main/resume/two_runs.py
41
51
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
171
cfg
def cfg(): c = 1.0 # noqa: F841 gamma = 0.7 # noqa: F841
python
tests/functional_tests/t0_main/sacred/t1_regression.py
11
13
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
172
run
def run(c, gamma): iris = datasets.load_iris() per = permutation(iris.target.size) iris.data = iris.data[per] iris.target = iris.target[per] clf = svm.SVC(C=c, kernel="rbf", gamma=gamma) clf.fit(iris.data[:90], iris.target[:90]) return clf.score(iris.data[90:], iris.target[90:])
python
tests/functional_tests/t0_main/sacred/t1_regression.py
17
24
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
173
add_wandb_env_variables
def add_wandb_env_variables(op): env = { "WANDB_API_KEY": os.getenv("WANDB_API_KEY"), "WANDB_BASE_URL": os.getenv("WANDB_BASE_URL"), "WANDB_KUBEFLOW_URL": os.getenv("WANDB_KUBEFLOW_URL"), "WANDB_PROJECT": "wandb_kfp_integration_test", } for name, value in env.items(): op = op.add_env_variable(V1EnvVar(name, value)) return op
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
12
22
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
174
preprocess_data
def preprocess_data( X_train_path: components.OutputPath("np_array"), # noqa: F821,N803 X_test_path: components.OutputPath("np_array"), # noqa: F821,N803 y_train_path: components.OutputPath("np_array"), # noqa: F821 y_test_path: components.OutputPath("np_array"), # noqa: F821 mlpipeline_ui_metadata_path: components.OutputPath(), seed: int = 1337, ): import json import numpy as np import wandb from sklearn import datasets from sklearn.model_selection import train_test_split def add_wandb_visualization(run, mlpipeline_ui_metadata_path): """NOTE: To use this, you must modify your component to have an output called `mlpipeline_ui_metadata_path` AND call `wandb.init` yourself inside that component. Example usage: def my_component(..., mlpipeline_ui_metadata_path: OutputPath()): import wandb from wandb.integration.kfp.helpers import add_wandb_visualization with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) ... # the rest of your code here """ def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>' iframe_html = get_iframe_html(run) metadata = { "outputs": [ {"type": "markdown", "storage": "inline", "source": iframe_html} ] } with open(mlpipeline_ui_metadata_path, "w") as metadata_file: json.dump(metadata, metadata_file) with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) X, y = datasets.load_iris(return_X_y=True) # noqa: N806 X_train, X_test, y_train, y_test = train_test_split( # noqa: N806 X, y, test_size=0.2, random_state=seed ) with open(X_train_path, "wb") as f: np.save(f, X_train) with open(y_train_path, "wb") as f: np.save(f, y_train) with open(X_test_path, "wb") as f: np.save(f, X_test) with open(y_test_path, "wb") as f: np.save(f, y_test)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
25
86
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
175
add_wandb_visualization
def add_wandb_visualization(run, mlpipeline_ui_metadata_path): """NOTE: To use this, you must modify your component to have an output called `mlpipeline_ui_metadata_path` AND call `wandb.init` yourself inside that component. Example usage: def my_component(..., mlpipeline_ui_metadata_path: OutputPath()): import wandb from wandb.integration.kfp.helpers import add_wandb_visualization with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) ... # the rest of your code here """ def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>' iframe_html = get_iframe_html(run) metadata = { "outputs": [ {"type": "markdown", "storage": "inline", "source": iframe_html} ] } with open(mlpipeline_ui_metadata_path, "w") as metadata_file: json.dump(metadata, metadata_file)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
40
66
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
176
get_iframe_html
def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>'
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
55
56
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
177
train_model
def train_model( X_train_path: components.InputPath("np_array"), # noqa: F821,N803 y_train_path: components.InputPath("np_array"), # noqa: F821 model_path: components.OutputPath("sklearn_model"), # noqa: F821 mlpipeline_ui_metadata_path: components.OutputPath(), ): import json import joblib import numpy as np import wandb from sklearn.ensemble import RandomForestClassifier def add_wandb_visualization(run, mlpipeline_ui_metadata_path): """NOTE: To use this, you must modify your component to have an output called `mlpipeline_ui_metadata_path` AND call `wandb.init` yourself inside that component. Example usage: def my_component(..., mlpipeline_ui_metadata_path: OutputPath()): import wandb from wandb.integration.kfp.helpers import add_wandb_visualization with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) ... # the rest of your code here """ def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>' iframe_html = get_iframe_html(run) metadata = { "outputs": [ {"type": "markdown", "storage": "inline", "source": iframe_html} ] } with open(mlpipeline_ui_metadata_path, "w") as metadata_file: json.dump(metadata, metadata_file) with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) with open(X_train_path, "rb") as f: X_train = np.load(f) # noqa: N806 with open(y_train_path, "rb") as f: y_train = np.load(f) model = RandomForestClassifier() model.fit(X_train, y_train) joblib.dump(model, model_path)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
89
142
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
178
add_wandb_visualization
def add_wandb_visualization(run, mlpipeline_ui_metadata_path): """NOTE: To use this, you must modify your component to have an output called `mlpipeline_ui_metadata_path` AND call `wandb.init` yourself inside that component. Example usage: def my_component(..., mlpipeline_ui_metadata_path: OutputPath()): import wandb from wandb.integration.kfp.helpers import add_wandb_visualization with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) ... # the rest of your code here """ def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>' iframe_html = get_iframe_html(run) metadata = { "outputs": [ {"type": "markdown", "storage": "inline", "source": iframe_html} ] } with open(mlpipeline_ui_metadata_path, "w") as metadata_file: json.dump(metadata, metadata_file)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
102
128
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
179
get_iframe_html
def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>'
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
117
118
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
180
test_model
def test_model( X_test_path: components.InputPath("np_array"), # noqa: F821,N803 y_test_path: components.InputPath("np_array"), # noqa: F821 model_path: components.InputPath("sklearn_model"), # noqa: F821 mlpipeline_ui_metadata_path: components.OutputPath(), ) -> NamedTuple( "Output", [("accuracy", float), ("precision", float), ("recall", float)] ): import json from collections import namedtuple import joblib import numpy as np import wandb from sklearn.ensemble import RandomForestClassifier # noqa: F401 from sklearn.metrics import accuracy_score, precision_score, recall_score def add_wandb_visualization(run, mlpipeline_ui_metadata_path): """NOTE: To use this, you must modify your component to have an output called `mlpipeline_ui_metadata_path` AND call `wandb.init` yourself inside that component. Example usage: def my_component(..., mlpipeline_ui_metadata_path: OutputPath()): import wandb from wandb.integration.kfp.helpers import add_wandb_visualization with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) ... # the rest of your code here """ def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>' iframe_html = get_iframe_html(run) metadata = { "outputs": [ {"type": "markdown", "storage": "inline", "source": iframe_html} ] } with open(mlpipeline_ui_metadata_path, "w") as metadata_file: json.dump(metadata, metadata_file) with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) with open(X_test_path, "rb") as f: X_test = np.load(f) # noqa: N806 with open(y_test_path, "rb") as f: y_test = np.load(f) model = joblib.load(model_path) preds = model.predict(X_test) accuracy = accuracy_score(y_test, preds) precision = precision_score(y_test, preds, average="micro") recall = recall_score(y_test, preds, average="micro") output = namedtuple("Output", ["accuracy", "precision", "recall"]) return output(accuracy, precision, recall)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
145
207
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
181
add_wandb_visualization
def add_wandb_visualization(run, mlpipeline_ui_metadata_path): """NOTE: To use this, you must modify your component to have an output called `mlpipeline_ui_metadata_path` AND call `wandb.init` yourself inside that component. Example usage: def my_component(..., mlpipeline_ui_metadata_path: OutputPath()): import wandb from wandb.integration.kfp.helpers import add_wandb_visualization with wandb.init() as run: add_wandb_visualization(run, mlpipeline_ui_metadata_path) ... # the rest of your code here """ def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>' iframe_html = get_iframe_html(run) metadata = { "outputs": [ {"type": "markdown", "storage": "inline", "source": iframe_html} ] } with open(mlpipeline_ui_metadata_path, "w") as metadata_file: json.dump(metadata, metadata_file)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
162
188
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
182
get_iframe_html
def get_iframe_html(run): return f'<iframe src="{run.url}?kfp=true" style="border:none;width:100%;height:100%;min-width:900px;min-height:600px;"></iframe>'
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
177
178
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
183
testing_pipeline
def testing_pipeline(seed: int): conf = dsl.get_pipeline_conf() conf.add_op_transformer(add_wandb_env_variables) preprocess_data_task = preprocess_data(seed) train_model_task = train_model( preprocess_data_task.outputs["X_train"], preprocess_data_task.outputs["y_train"] ) test_model_task = test_model( # noqa: F841 preprocess_data_task.outputs["X_test"], preprocess_data_task.outputs["y_test"], train_model_task.output, )
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-helper.py
231
243
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
184
add_wandb_env_variables
def add_wandb_env_variables(op): env = { "WANDB_API_KEY": os.getenv("WANDB_API_KEY"), "WANDB_BASE_URL": os.getenv("WANDB_BASE_URL"), "WANDB_KUBEFLOW_URL": os.getenv("WANDB_KUBEFLOW_URL"), "WANDB_PROJECT": "wandb_kfp_integration_test", } for name, value in env.items(): op = op.add_env_variable(V1EnvVar(name, value)) return op
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
13
23
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
185
setup_data
def setup_data( train_dataset_path: components.OutputPath("tensor"), # noqa: F821 test_dataset_path: components.OutputPath("tensor"), # noqa: F821 ): import torch from torchvision import datasets, transforms transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] ) train_dataset = datasets.FakeData( size=2000, image_size=(1, 28, 28), num_classes=10, transform=transform ) test_dataset = datasets.FakeData( size=2000, image_size=(1, 28, 28), num_classes=10, transform=transform ) torch.save(train_dataset, train_dataset_path) torch.save(test_dataset, test_dataset_path)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
27
45
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
186
setup_dataloaders
def setup_dataloaders( train_dataset_path: components.InputPath("tensor"), # noqa: F821 test_dataset_path: components.InputPath("tensor"), # noqa: F821 train_dataloader_path: components.OutputPath("dataloader"), # noqa: F821 test_dataloader_path: components.OutputPath("dataloader"), # noqa: F821 batch_size: int = 32, ): import torch train_dataset = torch.load(train_dataset_path) test_dataset = torch.load(test_dataset_path) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batch_size, ) test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=batch_size, ) torch.save(train_loader, train_dataloader_path) torch.save(test_loader, test_dataloader_path)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
49
71
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
187
train_model
def train_model( train_dataloader_path: components.InputPath("dataloader"), # noqa: F821 test_dataloader_path: components.InputPath("dataloader"), # noqa: F821 model_path: components.OutputPath("pytorch_model"), # noqa: F821 seed: int = 1337, use_cuda: bool = False, lr: float = 1.0, gamma: float = 0.7, epochs: int = 1, log_interval: int = 10, dry_run: bool = False, save_model: bool = False, ): import torch import torch.nn as nn import torch.nn.functional as F # noqa: N812 import torch.optim as optim from torch.optim.lr_scheduler import StepLR class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(model, device, train_loader, optimizer, epoch, log_interval, dry_run): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % log_interval == 0: wandb.log( {"epoch": epoch, "step": batch_idx * len(data), "loss": loss.item()} ) if dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss( output, target, reduction="sum" ).item() # sum up batch loss pred = output.argmax( dim=1, keepdim=True ) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) wandb.log( {"test_loss": test_loss, "accuracy": correct / len(test_loader.dataset)} ) torch.manual_seed(seed) train_loader = torch.load(train_dataloader_path) test_loader = torch.load(test_dataloader_path) device = torch.device("cuda" if use_cuda else "cpu") model = Net() model.to(device) optimizer = optim.Adadelta(model.parameters(), lr=lr) scheduler = StepLR(optimizer, step_size=1, gamma=gamma) for epoch in range(1, epochs + 1): train( model, device, train_loader, optimizer, epoch, log_interval, dry_run, ) test(model, device, test_loader) scheduler.step() if save_model: torch.save(model.state_dict(), model_path)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
75
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{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
188
__init__
def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
95
102
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
189
forward
def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
104
117
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
190
train
def train(model, device, train_loader, optimizer, epoch, log_interval, dry_run): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % log_interval == 0: wandb.log( {"epoch": epoch, "step": batch_idx * len(data), "loss": loss.item()} ) if dry_run: break
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
119
133
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
191
test
def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss( output, target, reduction="sum" ).item() # sum up batch loss pred = output.argmax( dim=1, keepdim=True ) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) wandb.log( {"test_loss": test_loss, "accuracy": correct / len(test_loader.dataset)} )
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
135
154
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
192
testing_pipeline
def testing_pipeline(seed, save_model): conf = dsl.get_pipeline_conf() conf.add_op_transformer(add_wandb_env_variables) setup_data_task = setup_data() setup_dataloaders_task = setup_dataloaders( setup_data_task.outputs["train_dataset"], setup_data_task.outputs["test_dataset"], batch_size=32, ) train_model_task = train_model( # noqa: F841 setup_dataloaders_task.outputs["train_dataloader"], setup_dataloaders_task.outputs["test_dataloader"], save_model=save_model, )
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
203
217
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
193
wandb_probe_package
def wandb_probe_package(): if not os.environ.get("WB_PROBE_PACKAGE"): return s, o = subprocess.getstatusoutput("git rev-parse HEAD") if s: return wandb_local = f"git+https://github.com/wandb/wandb.git@{o}#egg=wandb" return wandb_local
python
tests/functional_tests/t0_main/kfp/wandb_probe.py
5
12
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
194
add_wandb_env_variables
def add_wandb_env_variables(op): env = { "WANDB_API_KEY": os.getenv("WANDB_API_KEY"), "WANDB_BASE_URL": os.getenv("WANDB_BASE_URL"), "WANDB_KUBEFLOW_URL": os.getenv("WANDB_KUBEFLOW_URL"), "WANDB_PROJECT": "wandb_kfp_integration_test", } for name, value in env.items(): op = op.add_env_variable(V1EnvVar(name, value)) return op
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-sklearn.py
13
23
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
195
preprocess_data
def preprocess_data( X_train_path: components.OutputPath("np_array"), # noqa: F821,N803 X_test_path: components.OutputPath("np_array"), # noqa: F821,N803 y_train_path: components.OutputPath("np_array"), # noqa: F821 y_test_path: components.OutputPath("np_array"), # noqa: F821 seed: int = 1337, ): import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split X, y = datasets.load_iris(return_X_y=True) # noqa: N806 X_train, X_test, y_train, y_test = train_test_split( # noqa: N806 X, y, test_size=0.2, random_state=seed ) with open(X_train_path, "wb") as f: np.save(f, X_train) with open(y_train_path, "wb") as f: np.save(f, y_train) with open(X_test_path, "wb") as f: np.save(f, X_test) with open(y_test_path, "wb") as f: np.save(f, y_test)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-sklearn.py
27
53
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
196
train_model
def train_model( X_train_path: components.InputPath("np_array"), # noqa: F821,N803 y_train_path: components.InputPath("np_array"), # noqa: F821 model_path: components.OutputPath("sklearn_model"), # noqa: F821 ): import joblib import numpy as np from sklearn.ensemble import RandomForestClassifier with open(X_train_path, "rb") as f: X_train = np.load(f) # noqa: N806 with open(y_train_path, "rb") as f: y_train = np.load(f) model = RandomForestClassifier() model.fit(X_train, y_train) joblib.dump(model, model_path)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-sklearn.py
57
75
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
197
test_model
def test_model( X_test_path: components.InputPath("np_array"), # noqa: F821,N803 y_test_path: components.InputPath("np_array"), # noqa: F821 model_path: components.InputPath("sklearn_model"), # noqa: F821 ) -> NamedTuple( "Output", [("accuracy", float), ("precision", float), ("recall", float)] ): from collections import namedtuple import joblib import numpy as np from sklearn.ensemble import RandomForestClassifier # noqa: F401 from sklearn.metrics import accuracy_score, precision_score, recall_score with open(X_test_path, "rb") as f: X_test = np.load(f) # noqa: N806 with open(y_test_path, "rb") as f: y_test = np.load(f) model = joblib.load(model_path) preds = model.predict(X_test) accuracy = accuracy_score(y_test, preds) precision = precision_score(y_test, preds, average="micro") recall = recall_score(y_test, preds, average="micro") output = namedtuple("Output", ["accuracy", "precision", "recall"]) return output(accuracy, precision, recall)
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-sklearn.py
79
107
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
198
testing_pipeline
def testing_pipeline(seed: int): conf = dsl.get_pipeline_conf() conf.add_op_transformer(add_wandb_env_variables) preprocess_data_task = preprocess_data(seed) train_model_task = train_model( preprocess_data_task.outputs["X_train"], preprocess_data_task.outputs["y_train"] ) test_model_task = test_model( # noqa: F841 preprocess_data_task.outputs["X_test"], preprocess_data_task.outputs["y_test"], train_model_task.output, )
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-sklearn.py
131
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{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
199
add_wandb_env_variables
def add_wandb_env_variables(op): env = { "WANDB_API_KEY": os.getenv("WANDB_API_KEY"), "WANDB_BASE_URL": os.getenv("WANDB_BASE_URL"), "WANDB_KUBEFLOW_URL": os.getenv("WANDB_KUBEFLOW_URL"), "WANDB_PROJECT": "wandb_kfp_integration_test", } for name, value in env.items(): op = op.add_env_variable(V1EnvVar(name, value)) return op
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-simple.py
12
22
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }
200
add
def add(a: float, b: float) -> float: return a + b
python
tests/functional_tests/t0_main/kfp/kfp-pipeline-simple.py
26
27
{ "name": "Git-abouvier/wandb", "url": "https://github.com/Git-abouvier/wandb.git", "license": "MIT", "stars": 0, "forks": 0 }