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![PyTorch + MLflow
M L F L O W A U T O L O G G I N G
• PyTorch auto logging with Lightning training loop
• Model hyper-params like LR, model summary,
optimizer name, min delta, best score
• Early stopping and other callbacks
• Log every N iterations
• User defined metrics like F1 score, test accuracy
import mlflow.pytorch
parser =
LightningMNISTClassifier.add_model_specific_args(parent_parser=parser)
#just add this and your autologging should work!
mlflow.pytorch.autolog()
model = LightningMNISTClassifier(**dict_args)
dm = MNISTDataModule(**dict_args)
dm.prepare_data()
dm.setup(stage="fit")
early_stopping = EarlyStopping(monitor="val_loss", mode="min",
verbose=True)
checkpoint_callback = ModelCheckpoint(
filepath=os.getcwd(), save_top_k=1, verbose=True,
monitor="val_loss", mode="min", prefix="",
)
lr_logger = LearningRateLogger()
trainer = pl.Trainer.from_argparse_args(
args, callbacks=[lr_logger, early_stopping],
checkpoint_callback=checkpoint_callback
)
trainer.fit(model)
trainer.test()](https://image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-8-2048.jpg)
![PyTorch + MLflow
mlflow.pytorch.save_model(
model,
path=args.model_save_path,
requirements_file="requirements.txt",
extra_files=["class_mapping.json", "bert_base_uncased_vocab.txt"],
)
:param requirements_file: An (optional) string containing the path to requirements file.
If ``None``, no requirements file is added to the model.
:param extra_files: An (optional) list containing the paths to corresponding extra files.
For example, consider the following ``extra_files`` list::
extra_files = ["s3://my-bucket/path/to/my_file1",
"s3://my-bucket/path/to/my_file2"]
In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3.
If ``None``, no extra files are added to the model.
S A V E
A R T I F A C T S
• Additional artifacts for
model reproducibility
• For Example: vocabulary files
for NLP models,
requirements.txt and other
extra files for torchserve
deployment](https://image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-10-2048.jpg)
![PyTorch + MLflow
# deploy model
mlflow deployments create --name mnist_test --target torchserve ——
model-uri mnist.pt -C "MODEL_FILE=mnist_model.py" -C
"HANDLER=mnist_handler.py"
# do prediction
mlflow deployments predict --name mnist_test --target torchserve --
input_path sample.json --output_path output.json
D E P L O Y M E N T P L U G I N
New TorchServe Deployment Plugin
Test models during development cycle, pull
models from MLflow Model repository and run
• CLI
• Run with Local vs remote TorchServe
• Python API
import os
import matplotlib.pyplot as plt
from torchvision import transforms
from mlflow.deployments import get_deploy_client
img = plt.imread(os.path.join(os.getcwd(), "test_data/one.png"))
mnist_transforms = transforms.Compose([
transforms.ToTensor()
])
image = mnist_transforms(img)
plugin = get_deploy_client("torchserve")
config = {
'MODEL_FILE': "mnist_model.py",
'HANDLER_FILE': 'mnist_handler.py'
}
plugin.create_deployment(name="mnist_test", model_uri="mnist_cnn.pt",
config=config)
prediction = plugin.predict("mnist_test", image)](https://image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-13-2048.jpg)
![NEW FEATURES
Integrations and new samples for:
- Model Interpretability using Captum
- Model Signature
- Hyper Parameter Optimization using Ax/Botorch
- Iterative Pruning Example using Ax/Botorch
#Captum
ig = IntegratedGradients(net)
test_input_tensor.requires_grad_()
attr, _ = ig.attribute(test_input_tensor, target=1,
return_convergence_delta=True)
attr = attr.detach().numpy()
# To understand attributions, average across all inputs, print
and visualize average attribution for each feature.
feature_imp, feature_imp_dict =
visualize_importances(feature_names, np.mean(attr, axis=0))
mlflow.log_metrics(feature_imp_dict)
mlflow.log_text(str(feature_imp), "feature_imp_summary.txt")
fig, (ax1, ax2) = plt.subplots(2, 1)
fig.tight_layout(pad=3)
ax1.hist(attr[:, 1], 100)
ax1.set(title="Distribution of Sibsp Attribution Values")
#Model Signature
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)](https://image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-16-2048.jpg)
![NEW FEATURES - HPO WITH AX
with mlflow.start_run(run_name="Parent Run"):
train_evaluate(params=params, max_epochs=max_epochs)
ax_client = AxClient()
ax_client.create_experiment(
parameters=[
{"name": "weight_decay", "type": "range", "bounds": [1e-4, 1e-3]},
{"name": "momentum", "type": "range", "bounds": [0.7, 1.0]},
],
objective_name="test_accuracy",
)
for i in range(total_trials):
with mlflow.start_run(nested=True, run_name="Trial " + str(i)) as child_run:
parameters, trial_index = ax_client.get_next_trial()
test_accuracy = train_evaluate(params=parameters, max_epochs=max_epochs)
# completion of trial
ax_client.complete_trial(trial_index=trial_index,
raw_data=test_accuracy.item())
best_parameters, metrics = ax_client.get_best_parameters()
for param_name, value in best_parameters.items():
mlflow.log_param("optimum_" + param_name, value)](https://image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-18-2048.jpg)
![PyTorch + MLflow
M L F L O W A U T O L O G G I N G
• PyTorch auto logging with Lightning training loop
• Model hyper-params like LR, model summary,
optimizer name, min delta, best score
• Early stopping and other callbacks
• Log every N iterations
• User defined metrics like F1 score, test accuracy
import mlflow.pytorch
parser =
LightningMNISTClassifier.add_model_specific_args(parent_parser=parser)
#just add this and your autologging should work!
mlflow.pytorch.autolog()
model = LightningMNISTClassifier(**dict_args)
dm = MNISTDataModule(**dict_args)
dm.prepare_data()
dm.setup(stage="fit")
early_stopping = EarlyStopping(monitor="val_loss", mode="min",
verbose=True)
checkpoint_callback = ModelCheckpoint(
filepath=os.getcwd(), save_top_k=1, verbose=True,
monitor="val_loss", mode="min", prefix="",
)
lr_logger = LearningRateLogger()
trainer = pl.Trainer.from_argparse_args(
args, callbacks=[lr_logger, early_stopping],
checkpoint_callback=checkpoint_callback
)
trainer.fit(model)
trainer.test()](https://crownmelresort.com/image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-8-2048.jpg)
![PyTorch + MLflow
mlflow.pytorch.save_model(
model,
path=args.model_save_path,
requirements_file="requirements.txt",
extra_files=["class_mapping.json", "bert_base_uncased_vocab.txt"],
)
:param requirements_file: An (optional) string containing the path to requirements file.
If ``None``, no requirements file is added to the model.
:param extra_files: An (optional) list containing the paths to corresponding extra files.
For example, consider the following ``extra_files`` list::
extra_files = ["s3://my-bucket/path/to/my_file1",
"s3://my-bucket/path/to/my_file2"]
In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3.
If ``None``, no extra files are added to the model.
S A V E
A R T I F A C T S
• Additional artifacts for
model reproducibility
• For Example: vocabulary files
for NLP models,
requirements.txt and other
extra files for torchserve
deployment](https://crownmelresort.com/image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-10-2048.jpg)
![PyTorch + MLflow
# deploy model
mlflow deployments create --name mnist_test --target torchserve ——
model-uri mnist.pt -C "MODEL_FILE=mnist_model.py" -C
"HANDLER=mnist_handler.py"
# do prediction
mlflow deployments predict --name mnist_test --target torchserve --
input_path sample.json --output_path output.json
D E P L O Y M E N T P L U G I N
New TorchServe Deployment Plugin
Test models during development cycle, pull
models from MLflow Model repository and run
• CLI
• Run with Local vs remote TorchServe
• Python API
import os
import matplotlib.pyplot as plt
from torchvision import transforms
from mlflow.deployments import get_deploy_client
img = plt.imread(os.path.join(os.getcwd(), "test_data/one.png"))
mnist_transforms = transforms.Compose([
transforms.ToTensor()
])
image = mnist_transforms(img)
plugin = get_deploy_client("torchserve")
config = {
'MODEL_FILE': "mnist_model.py",
'HANDLER_FILE': 'mnist_handler.py'
}
plugin.create_deployment(name="mnist_test", model_uri="mnist_cnn.pt",
config=config)
prediction = plugin.predict("mnist_test", image)](https://crownmelresort.com/image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-13-2048.jpg)
![NEW FEATURES
Integrations and new samples for:
- Model Interpretability using Captum
- Model Signature
- Hyper Parameter Optimization using Ax/Botorch
- Iterative Pruning Example using Ax/Botorch
#Captum
ig = IntegratedGradients(net)
test_input_tensor.requires_grad_()
attr, _ = ig.attribute(test_input_tensor, target=1,
return_convergence_delta=True)
attr = attr.detach().numpy()
# To understand attributions, average across all inputs, print
and visualize average attribution for each feature.
feature_imp, feature_imp_dict =
visualize_importances(feature_names, np.mean(attr, axis=0))
mlflow.log_metrics(feature_imp_dict)
mlflow.log_text(str(feature_imp), "feature_imp_summary.txt")
fig, (ax1, ax2) = plt.subplots(2, 1)
fig.tight_layout(pad=3)
ax1.hist(attr[:, 1], 100)
ax1.set(title="Distribution of Sibsp Attribution Values")
#Model Signature
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)](https://crownmelresort.com/image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-16-2048.jpg)
![NEW FEATURES - HPO WITH AX
with mlflow.start_run(run_name="Parent Run"):
train_evaluate(params=params, max_epochs=max_epochs)
ax_client = AxClient()
ax_client.create_experiment(
parameters=[
{"name": "weight_decay", "type": "range", "bounds": [1e-4, 1e-3]},
{"name": "momentum", "type": "range", "bounds": [0.7, 1.0]},
],
objective_name="test_accuracy",
)
for i in range(total_trials):
with mlflow.start_run(nested=True, run_name="Trial " + str(i)) as child_run:
parameters, trial_index = ax_client.get_next_trial()
test_accuracy = train_evaluate(params=parameters, max_epochs=max_epochs)
# completion of trial
ax_client.complete_trial(trial_index=trial_index,
raw_data=test_accuracy.item())
best_parameters, metrics = ax_client.get_best_parameters()
for param_name, value in best_parameters.items():
mlflow.log_param("optimum_" + param_name, value)](https://crownmelresort.com/image.slidesharecdn.com/reproducibleai-april2021final-210427175333/75/Reproducible-AI-using-MLflow-and-PyTorch-18-2048.jpg)
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Reproducible AI using MLflow and PyTorch
The document discusses the integration of PyTorch and MLflow for reproducible AI workflows, emphasizing their roles in experiment tracking, model deployment, and management of machine learning models. It presents the features of MLflow, such as model registration, autologging, and deployment with TorchServe, along with examples and best practices for setting up experiments. The content also highlights the importance of model interpretability and provides resources for further learning and implementation.
In this document
Powered by AI
Introduction to the presentation by Geeta Chauhan on Reproducible AI using PyTorch and MLflow.
Discusses the challenges of reproducibility in ML, such as tracking experiments, model drift, and optimizing metrics.
Introduction of MLflow as an open-source platform for managing the ML lifecycle, including experiment tracking, model registry, and reproducibility.
Features of PyTorch's autologging including logging model hyper-parameters and user-defined metrics during training.
How to compare experiment runs and save artifacts such as model files and additional files for reproducibility.
Logging TorchScript models for optimized and serialized model inference in production environments.
Deployment of models using TorchServe, including logging, versioning, and management via MLflow.
Support for Captum to interpret model predictions using various attribution algorithms for insights.
Overview of new features such as hyper-parameter optimization and adaptive experimentation capabilities.
Diagrammatic workflow of MLOps integrating MLflow, PyTorch, and TorchServe along the deployment pipeline.
Plans for future enhancements including Captum insights and better integrations within MLflow.
Links to resources for reproducibility and MLflow with PyTorch, including contact information for the presenter.
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Similar to Reproducible AI using MLflow and PyTorch
Reproducible AI using MLflow and PyTorch
- 1. R E PR O D U C I B L E A I U S I N G P Y T O R C H A N D M L F L O W Geeta Chauhan PyTorch Partner Engineering, Facebook AI @ C H A U H A N G
- 2. MLFLOW + PyTorch AG E N D A 0 1 R E P R O D U C I B L E A I C H A L L E N G E 0 2 M L F L O W + P Y T O R C H 0 3 D E M O
- 3. PyTorch + MLflow ReproducibleAI Challenge
- 4. PyTorch + MLflow •Continuous Iterative process, Optimize for a metric • Quality depends on data and running parameters • Experiment tracking is difficult • Over time data changes, model drift • Model artifacts getting lost • Compare & combine many libraries and models • Diverse deployment environments TRADITIONAL SOFTWARE VS MACHINE LEARNING
- 5. PyTorch + MLflow mlflow+ PyTorch
- 6. PyTorch + MLflow ANOPEN SOURCE PLATFORM FOR MACHINE LEARNING LIFECYCLE MANAGEMENT I N T R O D U C I N G Record and query experiments: code, data, config, and results. TRACKING Package data science code in a format that enables reproducible runs on many platforms PROJECTS Deploy machine learning models in diverse serving environments MODELS Store, annotate, and manage models in a central repository MODEL REGISTRY
- 7. PyTorch + MLflow MLFLow+ Pytorch for reproducibility Record and query experiments: code, data, config, and results. TRACKING Package data science code in a format that enables reproducible runs on many platforms PROJECTS Deploy machine learning models in diverse serving environments MODELS Store, annotate, and manage models in a central repository MODEL REGISTRY PYTORCH AUTO LOGGING PYTORCH EXAMPLES W/ MLPROJECTS TORCHSCRIPTED MODELS, SAVE/LOAD ARTIFACTS MLFLOW TORCHSERVE DEPLOYMENT PLUGIN
- 8. PyTorch + MLflow ML F L O W A U T O L O G G I N G • PyTorch auto logging with Lightning training loop • Model hyper-params like LR, model summary, optimizer name, min delta, best score • Early stopping and other callbacks • Log every N iterations • User defined metrics like F1 score, test accuracy import mlflow.pytorch parser = LightningMNISTClassifier.add_model_specific_args(parent_parser=parser) #just add this and your autologging should work! mlflow.pytorch.autolog() model = LightningMNISTClassifier(**dict_args) dm = MNISTDataModule(**dict_args) dm.prepare_data() dm.setup(stage="fit") early_stopping = EarlyStopping(monitor="val_loss", mode="min", verbose=True) checkpoint_callback = ModelCheckpoint( filepath=os.getcwd(), save_top_k=1, verbose=True, monitor="val_loss", mode="min", prefix="", ) lr_logger = LearningRateLogger() trainer = pl.Trainer.from_argparse_args( args, callbacks=[lr_logger, early_stopping], checkpoint_callback=checkpoint_callback ) trainer.fit(model) trainer.test()
- 9. PyTorch + MLflow CO M P A R E E X P E R I M E N T R U N S
- 10. PyTorch + MLflow mlflow.pytorch.save_model( model, path=args.model_save_path, requirements_file="requirements.txt", extra_files=["class_mapping.json","bert_base_uncased_vocab.txt"], ) :param requirements_file: An (optional) string containing the path to requirements file. If ``None``, no requirements file is added to the model. :param extra_files: An (optional) list containing the paths to corresponding extra files. For example, consider the following ``extra_files`` list:: extra_files = ["s3://my-bucket/path/to/my_file1", "s3://my-bucket/path/to/my_file2"] In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3. If ``None``, no extra files are added to the model. S A V E A R T I F A C T S • Additional artifacts for model reproducibility • For Example: vocabulary files for NLP models, requirements.txt and other extra files for torchserve deployment
- 11. PyTorch + MLflow model= LightningMNISTClassifier(**dict_args) # Convert to TorchScripted model scripted_model = torch.jit.script(model) mlflow.start_run() # Log the scripted model using log_model mlflow.pytorch.log_model(scripted_model, "scripted_model") # If you need to reload the model just call load_model uri_path = mlflow.get_artifact_uri() scripted_loaded_model = mlflow.pytorch.load_model(os.path.join(uri_path, "scripted_model")) mlflow.end_run() T O R C H S C R I P T E D M O D E L • Log TorchScripted model • Static subset of the python language specialized for ML applications • Serialize and Optimize models for python- free process • Recommended for production inference
- 12. PY TORCH DEVELOPERDAY 2020 #PTD2 TORCHSERVE • Default handlers for common use cases (e.g., image segmentation, text classification) along with custom handlers support for other use cases and a Model Zoo • • Multi-model serving, Model versioning and ability to roll back to an earlier version • Automatic batching of individual inferences across HTTP requests • Logging including common metrics, and the ability to incorporate custom metrics • Robust HTTP APIS - Management and Inference model1.pth model1.pth model1.pth torch-model-archiver HTTP HTTP http://localhost:8080/ … http://localhost:8081/ … Logging Metrics model1.mar model2.mar model3.mar model4.mar model5.mar <path>/model_store Inference API Management API TorchServe Metrics API Inference API Serving Model 3 Serving Model 2 Serving Model 1 torchserve --start
- 13. PyTorch + MLflow #deploy model mlflow deployments create --name mnist_test --target torchserve —— model-uri mnist.pt -C "MODEL_FILE=mnist_model.py" -C "HANDLER=mnist_handler.py" # do prediction mlflow deployments predict --name mnist_test --target torchserve -- input_path sample.json --output_path output.json D E P L O Y M E N T P L U G I N New TorchServe Deployment Plugin Test models during development cycle, pull models from MLflow Model repository and run • CLI • Run with Local vs remote TorchServe • Python API import os import matplotlib.pyplot as plt from torchvision import transforms from mlflow.deployments import get_deploy_client img = plt.imread(os.path.join(os.getcwd(), "test_data/one.png")) mnist_transforms = transforms.Compose([ transforms.ToTensor() ]) image = mnist_transforms(img) plugin = get_deploy_client("torchserve") config = { 'MODEL_FILE': "mnist_model.py", 'HANDLER_FILE': 'mnist_handler.py' } plugin.create_deployment(name="mnist_test", model_uri="mnist_cnn.pt", config=config) prediction = plugin.predict("mnist_test", image)
- 14. CAPTUM Text Contributions: 7.54 ImageContributions: 11.19 Total Contributions: 18.73 0 200 400 600 800 400 300 200 100 0 S U P P O R T F O R AT T R I B U T I O N A LG O R I T H M S T O I N T E R P R E T: • Output predictions with respect to inputs • Output predictions with respect to layers • Neurons with respect to inputs • Currently provides gradient & perturbation based approaches (e.g. Integrated Gradients) Model interpretability library for PyTorch https://captum.ai/
- 15. GradientSHAP DeepLiftSHAP SHAP Methods IntegratedGradients Saliency GuidedGradCam Attribute model output (or internal neurons) to input features LayerGradientSHAP LayerDeepLiftSHAP SHAP Methods LayerConductance InternalInfluence GradCam Attribute model output to the layers of the model DeepLift NoiseTunnel (Smoothgrad, Vargrad, Smoothgrad Square) LayerActivation LayerGradientXActivation LayerDeepLift FeatureAblation / FeaturePermutation GuidedBackprop / Deconvolution AT TRIBUTION ALGORITHMS Input * Gradient LayerFeatureAblation LayerIntegratedGradients Occlusion Shapely Value Sampling Gradient Perturbation Other
- 16. NEW FEATURES Integrations andnew samples for: - Model Interpretability using Captum - Model Signature - Hyper Parameter Optimization using Ax/Botorch - Iterative Pruning Example using Ax/Botorch #Captum ig = IntegratedGradients(net) test_input_tensor.requires_grad_() attr, _ = ig.attribute(test_input_tensor, target=1, return_convergence_delta=True) attr = attr.detach().numpy() # To understand attributions, average across all inputs, print and visualize average attribution for each feature. feature_imp, feature_imp_dict = visualize_importances(feature_names, np.mean(attr, axis=0)) mlflow.log_metrics(feature_imp_dict) mlflow.log_text(str(feature_imp), "feature_imp_summary.txt") fig, (ax1, ax2) = plt.subplots(2, 1) fig.tight_layout(pad=3) ax1.hist(attr[:, 1], 100) ax1.set(title="Distribution of Sibsp Attribution Values") #Model Signature from mlflow.models.signature import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions)
- 17. EXPERIMENT CANDIDATES OFFLINE SIMULATION ONLINE A / BTEST AX / BOTORCH MULTITASK MODEL ADAPTIVE EXPERIMENTATION FOR NEWS FEED RANKING
- 18. NEW FEATURES -HPO WITH AX with mlflow.start_run(run_name="Parent Run"): train_evaluate(params=params, max_epochs=max_epochs) ax_client = AxClient() ax_client.create_experiment( parameters=[ {"name": "weight_decay", "type": "range", "bounds": [1e-4, 1e-3]}, {"name": "momentum", "type": "range", "bounds": [0.7, 1.0]}, ], objective_name="test_accuracy", ) for i in range(total_trials): with mlflow.start_run(nested=True, run_name="Trial " + str(i)) as child_run: parameters, trial_index = ax_client.get_next_trial() test_accuracy = train_evaluate(params=parameters, max_epochs=max_epochs) # completion of trial ax_client.complete_trial(trial_index=trial_index, raw_data=test_accuracy.item()) best_parameters, metrics = ax_client.get_best_parameters() for param_name, value in best_parameters.items(): mlflow.log_param("optimum_" + param_name, value)
- 19.
- 20. PY TORCH DEVELOPERDAY 2020 #PTD2 MLOPS WORKFLOW: MLFLOW + PY TORCH + TORCHSERVE Deployment TorchServe Management Inference Build PyTorch Model Data Scientist Training/ Distributed Training PyTorch Model Optimized Model: TorchScript Autolog Experiment Runs Model Registry MLflow TorchServe Plugin +
- 21. PyTorch + MLflow FUTURE •Captum Interpretability for Inference in mlflow-torchserve • Captum Insights visualizations in MLflow UI • PyTorch Profiler integration with Tensorboard in MLflow UI • More examples
- 22. PyTorch + MLflow RESOURCES •Reproducibility Checklist: https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf • NeurIPS Reproducibility updates: https://ai.facebook.com/blog/new-code-completeness-checklist-and- reproducibility-updates/ • arXiv + Papers with code: https://medium.com/paperswithcode/papers-with-code-partners-with-arxiv- ecc362883167 • MLflow + PyTorch Autolog blog: https://medium.com/pytorch/mlflow-and-pytorch-where-cutting-edge-ai- meets-mlops-1985cf8aa789 • MLflow TorchServe deployment plugin: https://github.com/mlflow/mlflow-torchserve • MLflow + PyTorch Examples: https://github.com/mlflow/mlflow/tree/master/examples/pytorch
- 23. T H AN K Y O U Contact: Email: gchauhan@fb.com Linkedin: https://www.linkedin.com/in/geetachauhan/