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Learn to build and deploy large neural networks with this virtual, hands-on=
workshop from NVIDIA.
Model Parallelism: Building and Deploying Large Neural Networks
May 3, 2023 - 7:00 AM - 3:00 PM PDT [UTC-7]
Register Now <https://go.nvidianews.com/MTU2LU9GTi03NDIAAAGLUxSnCBhf0nmye5g=
Hkz1nFZ-JaH0jmd6UfCk3ZHp-OYm39A87GpPNEilYI0hgMo31GoFmXlM=3D>
Join this virtual, instructor-led hands-on workshop from the NVIDIA Deep Le=
arning Institute <https://go.nvidianews.com/MTU2LU9GTi03NDIAAAGLUxSnCEUkLyx=
_RUVjyRKJOYBMwlJSF3-vD4OAlG5FLWvd1t596rj_TQ-B3LaN_nV3teom_sM=3D> to learn h=
ow to train large neural networks and deploy them to production.=20
By attending this workshop, you=E2=80=99ll learn how to:
=09- Train neural networks across multiple servers.
=09- Use techniques such as activation checkpointing, gradient accumulation=
, and various forms of model parallelism to overcome the challenges associa=
ted with a large-model memory footprint.
=09- Capture and understand training performance characteristics to optimiz=
e model architecture.
=09- Deploy very large multi-GPU models to production using NVIDIA Triton I=
nference Server=E2=84=A2.
Upon successful completion of the workshop, participants will receive an NV=
IDIA certificate to recognize their subject-matter competency and support p=
rofessional career growth.
Register Now <https://go.nvidianews.com/MTU2LU9GTi03NDIAAAGLUxSnCBhf0nmye5g=
Hkz1nFZ-JaH0jmd6UfCk3ZHp-OYm39A87GpPNEilYI0hgMo31GoFmXlM=3D>
For questions, as well as for discounts on group purchases of 5+ seats, con=
tact nvdli@nvidia.com <https://go.nvidianews.com/MTU2LU9GTi03NDIAAAGLUxSnCC=
xNcmox9mRbAeZyD49uMcwjNbdfV3vcBmhs88buwtgcZFwF0849GVKtgk_LYy1ijHs=3D>.
Workshop Details
Date
Wednesday, May 3, 2023
Time
7:00 am - 3:00 pm PDT
Learning Objectives and Prerequisites=20
Learn More =E2=9D=AF <https://go.nvidianews.com/MTU2LU9GTi03NDIAAAGLUxSnCBT=
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</tr>=20
<tr>=20
<td align=3D"left"><span style=3D"font-size: 12px; color: #999999;">NVIDIA =
Corporation, 2788 San Tomas Expressway Santa Clara, CA 95051.</span></td>=
=20
</tr>=20
</tbody>=20
</table></td>=20
</tr>=20
</tbody>=20
</table> </td>=20
</tr>=20
</tbody>=20
</table> </td>=20
</tr>=20
</tbody>=20
</table></td>=20
</tr>=20
</tbody>
</table> =20
<a href=3D
"https://go.nvidianews.com/MTU2LU9GTi03NDIAAAGLUxSnCNGE6BL_5AgNfFuafEpFzMtP=
yCDGvg26S5VsWyn5nBd4ioFi4dJ_uhx_wPFpXtmj7Zk=3D"
></a>
<img src=3D"https://go.nvidianews.com/trk?t=3D1&mid=3DMTU2LU9GTi03NDI6MDo5O=
TE3NDo1MjY1ODA6MjE4NjAxNDoxMzY0Mzg6OTo1ODI5Mjk6NTY5MDU1NzYyOmNvbnRhY3RAc2Vy=
Z2UuYWk%3D" width=3D"1" height=3D"1" style=3D"display:none !important;" alt=
=3D"" />
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